International Journal of Computing and Digital Systems ISSN (2210-142X) Int. J. Com. Dig. Sys. #, No.# (Mon-20..)
Word Sense Disambiguation in Hindi Language Using Score Based Modified Lesk Algorithm
Praffullit Tripathi1, Prasenjit Mukherjee2, Manik Hendre3, Manish Godse4, Baisakhi Chakraborty5
1,5Department of Computer Science and Engineering, NIT, Durgapur, India 2 ,3,4Department of Analytics and IT, PIBM, Pune, India
[email protected], 2 [email protected], [email protected], 4 [email protected], [email protected]
Abstract: Hindi is the widely used spoken language in the Indian subcontinent, and is used by more than 260 million Indians citizens. Indian governments has many digital initiatives to serve Indian citizen better, hence Hindi language becomes one of the important languages to serve Indian citizen. The Government initiatives are like smart city, Hospital Services, Common Service Centers, Digital Payment Ecosystem, Pensioners Scheme, Digital Locker and many more. These all initiative are served using mobile and web based applications, which citizens can access easily instead of visiting various government departments. To serve the large Hindi speaking population, it is necessary to handle the ambiguous words which have multiple connotations in any natural language processing task. In this paper, word sense disambiguation for Hindi language is proposed. Proposed method makes use of Lesk algorithm to disambiguate the Hindi words. Novel scoring method is used to assign a sense score to each token of the Hindi sentence. The sense score is calculated based on the gloss, hypernym, hyponym and synonym of the combinations of different sense of tokens. Hindi WordNet database created by CFILT, IIT Bombay is used in the proposed system. The proposed algorithm takes a natural language (NL) sentence in Hindi (Devanagari script) and process the sentence according to the score based approach modeled on the basic Lesk algorithm with the help of Hindi WordNet designed by CFILT IIT Bombay. The solution provided in this paper can be used vividly in various web based applications like Query-Response Systems, Question-Answer Systems, Sentiment analysis, Recommendation systems etc.
Keywords: NLP, Lesk Algorithm, word sense disambiguation, multi word WSD, Hindi WordNet.
in a Sentence or Word by the NLP based systems is an 1. INTRODUCTION open research problem. Human languages contain many Hindi is a Devanagari scripting language. Most of the ambiguous words that create problems at the time of citizens in the India and its subcontinent uses Hindi processing sentences. Human languages contain language for communication. After Digital India vocabulary that is comprised of words. All the languages initiative, demand of regional language based web are full of words that have multiple meanings associated applications are increasing exponentially. Citizens are with it. The meaning of a context is dissected depending using application in Healthcare, Transportation, on the words used in the sentence. Sometimes same word Education, Tourism, Financial and Pension Applications can have multiple senses [2]. These words are called which are in regional languages. Citizens are flexible polysemous words. A sentence with one or more with their native language and Hindi is the most polysemous words in it is said to be ambiguous. Human acceptable language in India and its subcontinent. Native neural networks (human brain) are way too efficient in speaker of Hindi is almost 260 million people according disambiguating the correct sense of a word used in a to Ethnologue as in [1]. Many Hindi based applications sentence. However, computer systems are not too have been developed that are related to the Query- proficient in it. Human languages are too complex for Response Systems, Question-Answer Systems, Sentiment machines to interpret and understand. It is preferred not analysis systems, etc. These systems are natural language to have such ambiguities in computer related processing (NLP) based systems, hence has the problem applications. Identification of polysemous words and of Word Sense Disambiguation. Understanding of a sense assigning their correct sense is classical problem in NLP.
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Process of assigning senses to the polysemous words is belong to the same detail of separated words. For a known as word-sense disambiguation problem. given query having ‘n’ tokens each having ‘k’ senses, there will be total ‘nk’ different combinations. Proposed Word sense disambiguation is one of the major area of scoring method is applied to all these different interest for NLP researchers. Computer programs do not combinations to give sense scoring and choose have the human experience in languages, so appropriate sense. Before applying word sense-count automatically correct sense detection of a polysemous firstly, we have to trim the query so as to reduce the word is a difficult task. This is the problem that Word useless words (stop words) for unnecessary Sense Disambiguation (WSD) tries to solve. WSD is a computation. Further, we develop the query by core research problem in NLP which tries to identify the searching stemmed words for validity. If there is no sense of a word which is used in a given context as in [3] such validity in WordNet, we reject the word just like [4]. Most of the WSD algorithms [5] [6] are divided as stops words. After a data set generation, we implement supervised, unsupervised, and knowledge-based the Lesk algorithm for further processing of sentences. techniques and that can be utilized to improve many NLP Word sense disambiguation is an open problem in based applications like Information Retrieval [7] [8], computational linguistic where it detects correct sense Sentiment Analysis [9] [10], Machine Translation [11] of a word that is used in a sentence. In the Simplified [12] or Text Summarization [13] [14]. Supervised Lesk algorithm, the meaning of a word is calculated by learning [15] requires large volumes of human tagged finding the overlaps between the context and the data in order to find set of rules to disambiguate future dictionary meanings of the word. The meaning for queries. There are two drawbacks to this approach. First, which maximum overlap takes place is taken as the human interaction is required. Second, no set of rules can word sense. The word sense of each word is found always dissect correct meaning of the word. On the other individually. Internet is emerging as a major source of hand, unsupervised learning [16] uses classical sources communication in India. It is expected to have large like dictionary based WordNet [17] etc. to disambiguate data analysis requirement in regional local languages senses. Lesk algorithm falls under the category of with the availability of internet services is rural areas. knowledge base based algorithm. Classical Lesk Hindi language contains many ambiguous words algorithm offers a score based approach to find overlap handling which is difficult for NLP systems because between different senses of target word and words nearby meaning of a same word is different in different to it. The Lesk algorithm is one of the most well-known sentences with different contexts. algorithm for word sense disambiguation which is introduced by Michael E. Lesk [2] in 1986. A simplified In this paper, we have proposed to modify the Lesk version of the Lesk algorithm is to compare the Algorithm to disambiguate ambiguous Hindi words dictionary definition of an ambiguous word with the efficiently. The score and combination based approach words contained in its neighborhood. Versions have been have been applied on Lesk algorithm. NLP based steps adapted to use WordNet. The proposed work presents an like tokenization, lemmatization, POS tagging are used to adaptation of Lesk algorithm that includes dictionary to process the Hindi sentences. The Hindi WordNet is used disambiguate ambiguous Hindi words from a Hindi to extract the correct sense of an ambiguous Hindi word. sentence. Rather than using a standard dictionary, this The International Phonetic Alphabet (IPA) notation [1] algorithm employs a different kind of dictionary called as for Hindi words is used in this research work. The WordNet. Traditional dictionary arranges words proposed algorithm takes Hindi sentences as an input. alphabetically while WordNet organize the words The output will be generated as correct sense of the Hindi semantically. Glosses refer to the definitive meaning of word. Many WSD systems have been discussed briefly in the word. Overlapping between the glosses of the Related Works Section (Section 2). The Architecture of neighbouring words plays a key role in Lesk’s approach this WSD system has been elaborated in Section 3. The [2]. The gloss of a word provides us the following Time Complexity of Proposed system has been information. calculated in Section 4 where Methodology has been 1. List of different senses of the word in WordNet. Each given in Section 5. Applications of the system have been sense belongs to a different synset. stated in Section 6. Finally, the Future Work and 2. Information about the context in which word appears. Conclusion has been drawn in Section 7 and Section 8 respectively. A scoring method is provided with the algorithm in order to give sense score to each set of combination. 2. RELATED WORKS This scoring method is carried out by considering all Word sense disambiguation is one of the key problems in pairwise matching such that each element of the pair the NLP domain. A large volume of research is going on
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Int. J. Com. Dig. Sys. #, No.#, ..-.. (Mon-20..)3 for the selection of an efficient WSD algorithm. Majority problems. Zankhana B. Vaishnav applied Knowledge- of the unsupervised learning solutions use Lesk Based Approach to disambiguate polysemous words Algorithm as their underlying algorithm. Basic Lesk Using Genetic Algorithm. Genetic approach proposed algorithm is implemented for the English corpus. Some used Indo-Aryan WordNet for Gujarati language as of the languages does not have rich corpus like English. lexical database as in [23]. S. N. Mohan Raj propose Such languages require some variations in classical Lesk method to eliminate ambiguity due to homonymy using algorithm in order to disambiguate senses efficiently. cluster and deep learning approach. Homonymy Rajat Pandit proposed dependency tree based lesk variant ambiguity is a problem in Malayalam language. This to perform WSD for low resource language like Bengali problem is attempted by the Authors in [24]. The system [18]. Shancheng Tang proposed a solution using deep uses POS tagging lemmatization and sense annotation. Chinese word sense disambiguation method which is The neural network has been used in deep learning based on sequence to sequence that improved method and this method is using the corpus to performance by 11.48% [19]. Evaluation of different disambiguate the homonymous words in Malayalam as in WSD algorithm is a major sub problem in the NLP [24]. Jagbir Singh proposed a word Sense disambiguation domain. Alessandro Raganato designed a unified system that is based on Punjabi language. The enhanced evaluation system and measure the performance of Lesk approach has been utilized to extract correct sense various word sense disambiguation algorithms. This of ambiguous Punjabi words. The methodology of this paper offers two main contributions. The first system is supervised learning and Indo WordNet has contribution is (1) standardizing the WSD datasets and been used for disambiguating Punjabi words as in [25]. training corpora into a structured format, (2) semi- Lekshmi R Pillai proposed a question answering system automatically annotation conversion from any dataset to to predict an answer for an input question. This model is WordNet 3.0, and (3) pre-processing the datasets by based on a combined approach using word sense consistently using the same pipeline. Second, the disambiguation (WSD) and semantic role labelling evaluation framework has been used to perform a fair (SRL). The proposed system is a factoid sense based quantitative and qualitative empirical comparison of the question generation system. The Lesk tool has been used main techniques that is discussed in the WSD literature, for WSD where the senna tool has been used for SRL. including the latest advances based on neural networks They used Lesk tool for WSD and senna tool for SRL [20]. Quang-Phuoc Nguyen designed a lexical semantic which are based on the sense affiliated with the sentence network for Korean language which is useful for Korean system generates questions that are semantically solvable morphological and word sense disambiguation [21]. The as in [26]. Word sense disambiguation can be solved Lexical semantic network [21] uses largest Korean LSN using three kinds of approaches, knowledge based, that consist of Lexical networks of nouns, predicates, and corpus based and hybrid approach. Himdweep Walia adverbs. Each node is connected with other node using proposed a Naïve Bayes based WSD approach that has six types semantic relation that are hyponymy, shown higher accuracy than other implementations as in synonymy, similarity, antonym, part-whole, and [27]. Training data is an issue to disambiguate ambiguous association relations. To represent a certain sense of a words in Persian language that has been faced by the word, each node is consisted with a word and its sense Author [28]. The Machine Learning (ML) algorithm with code. Korean morphological analysis is difficult because minimum supervision has been considered to solve this different types of morphems and parts of speech are problem. Various news articles have been used as main encoded into the same eojeol. The meaning of a source of the reference corpus. This method uses some morphem can be changed because of different parts of predefined features of target words to disambiguate speech tagging with the morphems. The morphological senses of the word as in [28]. G. Sajini designs a user analysis refers to discover the correct set of sense in a interface where user manually enters a sentence in Hindi given context. In Korean morphological analysis, the with polysemy word. This system [29] identifies the conventional methods are a) Segment the input eojeol polysemous words and list one or more meanings that are into morphemes and b) tag POS to each morpheme as in associated with that word. Using machine learning [21]. The application of WSD is not limited to any techniques, this system identifies the correct meaning of particular domain. Saeed Seifollahi, Mehdi Shajari use the polysemy word which is based on the given context WSD to analyze the sentiments of news headlines in as in [29]. Author [30] proposed a WSD system which is order to predict their impact on the stock prices. Some of based on Naive Bayes classifier (ML technique). Sense the applications require heavy computations. To make corpus and Ambiguous corpus have been used by this the application time efficient, several heuristics can be WSD system. utilized to improve the performance. Genetic algorithms are used widely to address various hard optimization
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TABLE I. COMPARATIVE STUDY AMONG SIMILAR TYPE WSD SYSTEMS WITH THE PROPOSED WSD SYSTEMS
Sl. No. Authors WSD based Systems Technique(s) used in similar type WSD Technique(s) used in proposed System WSD System 1 Rajat Pandit et al. Word Sense The LESK algorithm has been improved for The classical Lesk algorithm has [18] Disambiguation by Word sense disambiguation (WSD) on been modified for the WSD in Improvised Lesk Bengali language. The dependency tree Hindi language. The proposed Algorithm [18] based Lesk has been used to perform the algorithm uses score based WSD. approach. 2 Shancheng Tang et Deep Chinese Word Chinese WSD method has been used in this The Hindi sentence is used to al. [19] Sense Disambiguation system. The Deep learning method has process according to the score Method [19] been used for feature extraction of Chinese which is modeled on basic text. Lesk algorithm. The score is assigned on each combination of sense. The largest sense score is the output of this system.
3 Alessandro A Unified Evaluation An evaluation framework has been The proposed system is using Raganato et al. [20] Framework on Word designed, that is based on various WSD. supervised method of Lesk Sense Disambiguation The first step of evaluation is standardizing algorithm. The Hindi WordNet of [20] the WSD datasets and training corpora into CFILT, IIT, Bombay has been a unified format, The second step is used to implement this system. Converting annotation from any dataset to This system is able disambiguate WordNet and third step is pre-processing of ambiguous Hindi words easily. dataset using same pipeline. The proposed frame work performs a comparison which based on quantitative and qualitative.
4 Quang-Phuoc Korean-Vietnamese Korean is a morphologically rich language Hindi is an Indo-Aryan language. Nguyen et al.[21] Neural Machine where Vietnamese is an analytical language. Hindi is used by most of the Translation System [21] Korean language contains word ambiguities people in India and Indian which is a problem for Neural MT. The subcontinent. Korean knowledge base has been prepared Understanding a Hindi Sentence for the lexical semantic network which is is a real issue because Hindi used for morphological analysis and WSD sentence contains many in Korean language. ambiguous Hindi words. This Another large Korean-Vietnamese corpus problem has been solved by the has been prepared for the Vietnamese word Lesk algorithm using Hindi segmentation method. WordNet.The Classical Lesk algorithm has been modified. 5 Saeed Seifollahi et Word Sense A sentiment analysis has been done on the The Score based approach has al. [22] Disambiguation in News headlines. The WSD has been used on been applied on Lesk algorithm to Sentiment Analysis [22] sentiment analysis to predict the impact of disambiguate the ambiguous the stock price. They have used the news words. A sense score has been headlines as input of the proposed system. assigned to each possible combination of senses where largest sense score is the output of this proposed system.
6 Zankhana B. Word Sense A genetic algorithm has been used to A score based modified Lesk Vaishnav et al. [23] Disambiguation in Disambiguate polysemous words. The algorithm has been used to Gujarati Language [23] proposed system is based on the Gujarati disambiguate the ambiguous language. It uses Indo-Aryan WordNet words. The proposed system is database for the Gujarati as lexical database. based on Hindi language.
7 S. N. Mohan Raj et Word Sense This system uses WSD on Malayalam The proposed system uses Hindi al. [24] Disambiguation of language. The proposed work concerned words for disambiguation. The Malayalam Nouns [24] about the homonymy ambiguity. To Hindi language is Devanagari resolve the homonymy ambiguity, two Script and this script is containing approaches have been given- Clustering and many ambiguous words. To Deep learning. Clustering is supervised extract the correct sense, The method where POS tagging, lemmatization Lesk algorithm has been applied and sense annotation have been used. The after modification. Stop words Deep learning approach is based on neural elimination, Stemming, POS network that uses corpus for disambiguating tagging, Sense generation are homonymous words. most popular steps have been applied on this proposed system.
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Int. J. Com. Dig. Sys. #, No.#, ..-.. (Mon-20..)5
8 Jagbir Singh et al. Word Sense This work is analyzing the correct The proposed system is able to [25] Disambiguation in meaning of the ambiguous words in extract correct sense of the Punjabi [25] Punjabi language. An enhanced Lesk ambiguous Hindi word. Modified approach have been utilized to extract the Lesk has been used. Cltk library correct sense of the ambiguous words. The has been used for Hindi Stop supervised learning methodology and Indo Words detection. Indicnlp library WordNet have been utilized to develop this has been used for the Stemming system. and POS tagging of Hindi words. The combinations of senses have been generated by the proposed algorithm to assign score. The largest score of a sense combination detected as output. 9 Lekshmi R Pillai et Word Sense This is a factoid sense based question- This is modified Lesk algorithm al. [26] Disambiguation for answering system. The WSD and based Hindi WSD system. Score Question-Answering Semantic Role Labelling (SRL) approach has been modeled on System [26] approaches have been applied on this Lesk algorithm and sense system. The Lesk is used for WSD and combination has been generated Senna is used for SRL. to assign score. Output is dependent on to the score.
10 Himdweep Walia et Gurmukhi Word Sense Three kind of approaches is used in WSD. Dataset generation is a one of the al. [27] Disambiguation [27] The first approach is knowledge base, the important part of this proposed second approach is corpus based, and third WSD system. The Hindi approach is hybrid based. The Naïve Bayes WordNet has been used to filter classification algorithm is used on WSD to out various Hindi word senses. disambiguate Gurumukhi words. Extracted information from the The Punjabi corpus have been used for WordNet has been assigned as Gurumukhi words. value. The dataset has been represented from this extracted information. This dataset has been utilized to evaluate sense score.
11 Mohamadreza Persian word sense Persian language based WSD problem has Hindi language based WSD Mahmood et al. disambiguation [28] been solved by the Machine Learning (ML) problem has been solved by the [28] algorithm where minimal supervision modified Lesk algorithm which is has been considered. The corpus has been score based. Combinations of represented from various news articles. The senses are generated to assign the proposed system uses some predefined score for determination of correct features of targeted words and collaborative sense of a Hindi word. The most learning method. popular Hindi WordNet has been utilized which is Developed by the CFILT, IIT, Bombay.
12 Saiba Nazah et al. Word Sense This WSD system is based on Bengali The Score and Combination [32] Disambiguation of words. Naïve Bayes classifier and based approach have been used to Bangla sentences [32] artificial neural network (ANN) have been modify Lesk algorithm. The used to disambiguate the Bengali Words. algorithm is flexible to handle The Naïve Bayes classifier has been use in Hindi ambiguous words. The training phase and ANN has been used to natural language processing steps detect the correct sense of the word. have been utilized. The dataset has been generated using the Hindi WordNet.
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International Journal of Computing and Digital Systems ISSN (2210-142X) Int. J. Com. Dig. Sys. #, No.# (Mon-20..)
The Sense corpus contains synsets, synonyms and antonyms score is output of the algorithm. Output is returned to the where Ambiguous corpus contains all possible ambiguous client in the form of word sense pairs. words. The experimental result of this system is promising The proposed algorithm is modularized into following 6 to disambiguate the ambiguous words as in [30]. steps. Each of these modules are explained in details in Unsupervised approach is a most popular approach in subsequent sections. Machine Learning. A WSD system has been described by the Author [30] which is unsupervised approach and 1. Post NL Sentence in Hindi supervised approach has been bypassed by this system 2. Stop Word Elimination using simulation of the semantic inference process that is 3. Stemming and POS Tagging performed by human language users as in [31]. Another 4. Dataset Generation Bengali WSD system has been proposed by the Author 5. Modified Lesk Algorithm Implementation [32] that uses Naïve Bayes classifier and Artificial Neural a. Sense Combination Generation Network (ANN) to disambiguate the ambiguous Bengali b. Sense Score Assignment words in Bengali Language. Naïve Bayes classifier has 6. Output (Word Sense Pair) been used for the training phase and ANN has been utilized to predict and detect the correct sense of an ambiguous Bengali word as in [32]. Adapted Lesk method has been used by the Author [33] in their WSD system. The WordNet has been utilized instead of a standard dictionary. The WordNet contains a hierarchy of a semantic relation. The proposed system takes a single target word and its surrounding words as Input. The output is generated as sense of the target word. The comparison has been done on glosses of involved words and related words like hypernym, hyponym, holonym, meronym, troponym, and attribute that are available in WordNet. The sample data from SENSEVAL-2 has been utilized for method evaluation and this system has achieved 32% overall accuracy as in [33]. A comparative study has been done on similar type WSD system and proposed WSD system. The comparative study has been given in Table 1.
3. ARCHITECTURE OF THE PROPOSED SYSTEM Proposed System architecture (Figure-1) takes a natural language sentence in Hindi as input. It first removes Stop Words from the sentence. Stemming and POS tagging is major step for any natural language application. After Figure 1. Architecture of the Proposed WSD System eliminating the stop words from the input sentence, refined query is stemmed word by word. Further, refined query is POS tagged and only tokens with multiple 3.1. Algorithm senses are left. Each of these tokens are queries in the Basic Algorithmic Steps of this Proposed WSD System WordNet semantic database and all the information has been described here. usable in future is separated in a distinct variable named as dataset. Different possible sense combinations are i. Post NL Sentence in Hindi generated one by one. Each of the combination is assigned a value evaluated using proposed scoring The proposed WSD system will read natural language method. The sense combination with maximum sense sentence in Hindi. It will check for sentence validation.
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Int. J. Com. Dig. Sys. #, No.#, ..-.. (Mon-20..)7
If sentence is not validated then system will prompt to Examples: user. Otherwise the sentence will go for next step. a) Determiner: 'नह 'ीं (nɦiː[6]) (No),'कुछ'(kʊ[6]tʃʰ) (Few), 'हर'(ɦr) (Each), etc. ii. Stop Words Elimination b) Pronoun: 'उनक '(ʊ[6]nkiː[6]) (him/her), 'हम'(ɦm)(I),
'अपना'(ə[5]pnaː)(My), 'तुम'(t[2]ʊ[6]m)(You), Each language has a variety of stop words which is of no use while processing them for natural language 'मेर '(meːriː[6]) (My), 'वह'(ʋ[4]ɦ) (He/She) etc. processing applications. So it would be better to c) Adverb: 'यह ' (jɦ iː[6]) (here), 'अब' (ə[5] ʋ[4]) (Now), eliminate these stop words prior to advance of further etc. processing. d) Preposition: 'द्वारा' (d[2]aːr aː) (By),'जब' (dʒb) (If), etc. e) Wh Words: 'कैसे' (kɔːseː) (How), 'कहँ' (kɦ◌̃) (Where), Few example of Stop Words used in Hindi language are 'क्या', 'कक' (kjaː) (kɪ[6] ) (What), 'जहा' (dʒɦaː) (Where), listed here. These are in Devanagari script. etc. ['हᴂ'(ɦeː◌̃), 'है'(ɦɛː), 'हℂ'(ɦɛː◌̃), 'हह'(ɦɪ[6]), 'ही'(ɦiː[6]), 'हो'(ɦoː), 'हे'(ɦeː), 'से'(seː), 'अत'(ə[5]t[2]), 'के '(keː), These words are Stop words which are not very important 'रहे'(rɦ eː), 'का'(kaː), 'की'(kiː[6]), 'हक'(kɪ[6]), for the Hindi sentence processing. In this step, Stop words 'तो'(t[2]oː), 'ने'(neː), 'एक'(eːk), 'नहीीीीं'(nɦiː[6]), 'पे'(peː), are eliminated from the natural language sentence. 'मᴂ'(meː◌̃), 'वाले'(ʋ[4]aːl eː), 'सकते'(skt[2]eː), Example sentence has been given here in Figure 2 and 3. 'वह'(ʋ[4]ɦ), 'वे'(ʋ[4]eː), 'कई'(kiː[6]), 'होती'(ɦoːt[2]iː[6]), 'आप'(aːp), 'यह'(jɦ), 'और'(ɔːr), Natural language Sentence: मतकेआधारपरफैसलाआ। (mt[2] keː aːdʱ[2]aːr pr pʰɔːslaː ɦʊ[6]aː) (The decision 'एवीीीं'(eːʋ[4]◌̃), 'को'(koː), 'मे'(meː), 'दो'(d[2]oː), was made according to the openion.) 'थे'(tʰ[2]eː),'यहद'(jd[2]ɪ[6]), 'उनके '(ʊ[6]nkeː), 'थी'(tʰ[2]iː[6]), 'पर'(pr), 'इस'(ɪ[6]s), 'साथ'(saːtʰ[2]), मत के आधार पर फैसला आ। 'हलए'(lɪ[6]eː), 'जो'(dʒ oː), 'होता'(ɦoːt[2]aː), 'या'(jaː), 'हलये'(lɪ[6]jeː), 'द्वारा'(d[2]aːr aː), 'हुई'(ɦiː[6]), 'जब'(dʒb), 'होते'(ɦoːt[2]eː), 'व'(ʋ[4]), 'न'(n), Stop Words 'उनकी'(ʊ[6]nkiː[6]), 'आहद'(aːd[2]ɪ[6]), Figure 2. Example sentence with Stop Words 'सकता'(skt[2]aːeː), 'उनका'(ckaː), 'इतना'(ɪ[6]t[2]naː), 'इतयाहद'(ɪ[6]t[2]jaːd[2]ɪ[6]), 'हजस'(dʒɪ[6]s), ‘के ’ and ‘पर’ are filtered out as both of them are stop words. Refined query is - 'उस'(ʊ[6]s), 'कै 'से(kɛːseː), 'हूँ'(ɦuː[6]◌̃), 'ना'(naː),
'कहह'(kɦɪ[6]), 'सम'(sm), 'र् '(r), 'कहूँ'(kɦ◌̃), 'बस'(bs), मत आधार फैसला आ 'अपना'(ə[5]pnaː), 'यही'(jɦiː[6]), 'कहीीीीं'(kɦiː[6]◌̃),
'हाू ूूँ '(ɦaː◌̃), 'मℂने'(mɛːneː), 'जहूँ'(dʒɦ◌̃), 'सब'(sb), Figure 3. Example sentence after Stop Words Elimination 'यह'(jɦ), 'था'(tʰ[2]aː), 'तुम'(t[2]uːm), 'ये'(jeː), 'जे'(dʒ eː), 'भी'(bʱiː[6]), 'हम'(ɦm), 'अब'(ə[5]b), 'ऐसे'(eːseː), Pseudo code of Stop Words Elimination step has been 'वहाू ूूँ '(bɦaː◌̃), 'क्या'(kjaː), 'ओर'(ɔːoːr), given here.
'इसी'(ɪ[6]siː[6]), 'सके '(skeː), 'कभी'(kbʱiː[6]), 'हर'(ɦr), 'मेरी'(meːriː[6]), 'कम'(k m), 'सा'(saː), Pseudo Code: Stop Words Elimination
'उन्हᴂ'(ʊ[6]nɦeː◌), 'मेरे'(meːreː), 'उन'(ʊ[6]n), 'कु ̃ # install morfessor library 'छ(kʊ[6]tʃʰ), 'इन'(ɪ[6] n), 'ऐसा'(eːsaː), 'जहा'(dʒɦ aː), import itertools 'तीन'(t[2] iː[6] n)] import math from pyiwn import pyiwn iwn = pyiwn.IndoWordNet('hindi') Above is the exclusive list of all stop words in Hindi language. Python Cltk library ( https://pypi.org/project/ # initialize indicnlp library cltk/) has been used for the stop words. In code, these from indicnlp import common stop words are represented by STOPS_LIST list variable. This list contains many Hindi words that are # place the directory of indic_nlp_resources related to the Determiner, Pronoun and Adverb, common.set_resources_path(r"C:\Users\Praffullit Preposition, Wh word of English. Tripathi\.spyder-py3\Proj\indic_nlp_resources") from indicnlp import loader loader.load()
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8 Author Name:Paper Title … from indicnlp.morph import unsupervised_morph # tokenize as well as morphological splitting of words analyzer=unsupervised_morph.UnsupervisedMorphAnalyzer('m for word in hindi_text_no_stop_word.split(): r') if len(iwn.synsets(word, pos=pyiwn.NOUN))>0 or len(iwn.synsets(word, pos=pyiwn.VERB))>0 or # include STOPS_LIST using cltk library len(iwn.synsets(word, pos=pyiwn.ADJECTIVE))>0 or from cltk.stop.classical_hindi.stops import STOPS_LIST len(iwn.synsets(word, pos=pyiwn.ADVERB))>0: from cltk.tokenize.sentence import TokenizeSentence hindi_text_morphe = hindi_text_morphe+ word +" " elif len(iwn.synsets(word+"ना", pos=pyiwn.VERB))>0: matra = ["ँँ ","ँीं ","ँ ","ँ ","ँा","कँ","ँ ","ँु ","ँ ","ँ ","ँ ","ँ ", hindi_text_morphe = hindi_text_morphe + word +"ना" "ँे ","ँै ","ँ ","ँ ","ँ ","ँ "] else: karak = ["ने","क ","से","के ","द्वारा","कलए","मᴂ","पर", "का","क ", morphed_text_temp = analyzer.morph_analyze_document(word.split(' ')) "के ","रा","रे ","र "] temp_str = ""
if morphed_text_temp != word: print(STOPS_LIST) for word_iter in morphed_text_temp:
if word_iter in matra: STOPS_LIST.append(karak) word_iter = temp_str + word_iter
if len(iwn.synsets(word_iter, pos=pyiwn.NOUN))>0 or def tokenizer(string): len(iwn.synsets(word_iter, pos=pyiwn.VERB))>0 or # this tokenize sentence only len(iwn.synsets(word_iter, pos=pyiwn.ADJECTIVE))>0 or tokenizer = TokenizeSentence('hindi') len(iwn.synsets(word_iter, pos=pyiwn.ADVERB))>0: hindi_text_tokenize = tokenizer.tokenize(string) hindi_text_morphe = hindi_text_morphe+ word_iter+" " filtered_token_list = [] for word in hindi_text_tokenize: elif len(iwn.synsets(word_iter+"ना" ,pos=pyiwn.VERB))>0: if word not in STOPS_LIST: hindi_text_morphe = hindi_text_morphe+ word_iter+"ना" filtered_token_list.append(word) temp_str = word_iter hindi_text_no_stop_word = "" for it in filtered_token_list: final_token_list=[] hindi_text_no_stop_word=hindi_text_no_stop_word+it+" " return hindi_text_no_stop_word for word in hindi_text_morphe.split(): final_token_list.append(word) iii. Stemming and POS tagging for word_it in final_token_list: print(word_it), Filtered query sentence (query without stop words) is return final_token_list again parsed word by word in order to stem the root word from each filtered token. Indicnlp library iv. Dataset Generation (https://pypi.org/project/indicnlp/) provides support to stem words using morphological analysis of their After filtering and stemming the query sentence, we’re WordNet resource. While stemming, we further querying wordnet to filter out various senses of the improvise query by searching stemmed word into tokens. A dictionary of tokens acts as the key and all the WordNet for any valid sense. We search this stem word extracted information from the wordnet are assigned as as either noun, adverb, adjective or verb as all other part the value to the tokens or the key. This algorithm requires of speech is of no use. If stem word have no valid senses hypernym, gloss, hyponym and synset values in further as above stated part of speech then we simply reject the steps to evaluate sense scores. word.
Structure of the Dataset: DATASET[ ‘word’][in1] After stemming we get tokens as below: Dataset is a dictionary, each token of the query sentence [“मत” (mt[2]) (Opinion), ”आधार” (aːdʱ[2]aːr) acts as key. Dataset is formed in a nested structure. Each (according),”फै सला”(pʰɔːslaː) (Decision)] sense of the token is further classified according to their part of speech. in1 iterator uses to iterate through highest The Pseudo Code of Stemming and POS Tagging has level of depth which access the meaning in the following been given here. order
Pseudo Code: Stemming and POS Tagging “मत”(m t[2]), “आधार”(aːdʱ[2]aːr) and “फैसला”(pʰɔːslaː) have 5,14 and 2 possible senses. All these senses are def tokenizer_and_stemmer(string): encapsulated in variable name dataset. hindi_text_no_stop_word=tokenizer(string) hindi_text_morphe=""
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TABLE II. SIGNIFICANCE OF INDICES USED IN DATASET hyponym_str ="" Index Value Represented property for k2 in range(0,len(hyponym)): 0 Gloss of the word itself if flag == 1: 1 Gloss of the hypernym of the word gloss_hypo = tokenizer(hyponym[k2].gloss()) 2 Gloss of hyponym of the word hyponym_str = hyponym_str + gloss_hypo +" || " 3 Word synonymous to the word else: 4 Part of speech of the in1 th sense hyponym_str = hyponym_str + hyponym[k2].gloss() +" || "
temp.append(hyponym_str) Pseudo Code: Dataset Generation syn_words = "" def dataset_generation(token_list): for j in syns[i].lemmas(): reject_words = {'','ने'} syn_words = syn_words+ j.name() +" || " dataset = {} temp.append(syn_words) for iter in token_list: temp.append(syns[0].pos()) if iter in reject_words: dataset[iter].append(temp) continue return dataset else: dataset[iter] = [] v. Modified Lesk Algorithm implementation # iterate for each word 4 times once as noun ,verb, adj and verb a) Sense Combination Generation respectively This algorithm generates all the different combinations of for pos_iter in range(0,5): if pos_iter == 0: sense of the tokens as depicted in the Figure 4 and uses syns = iwn.synsets(iter, pos=pyiwn.NOUN) scoring method explained in the next segment of this elif pos_iter == 1: section to give a sense score to each of the combination. syns = iwn.synsets(iter+"ना", pos=pyiwn.VERB) A query with n tokens and each token having k senses elif pos_iter == 2: have (nk) different combinations to check. syns = iwn.synsets(iter, pos=pyiwn.VERB) elif pos_iter == 3: If a query has 3 tokens (A, B and C) and these tokens syns = iwn.synsets(iter, pos=pyiwn.ADJECTIVE) have 3, 2 and 4 senses respectively. Then there are total elif pos_iter == 4: 3x2x4 = 24 possible combinations of sense to check for. syns = iwn.synsets(iter, pos=pyiwn.ADVERB) if len(syns)>0:
# Set Flag 1 To Remove Stop_Words While Formation Of Dataset flag=1 for i in range(0,len(syns)): temp = [] if flag == 1: gloss_main = tokenizer(syns[i].gloss()) temp.append(gloss_main) else: temp.append(syns[i].gloss()) if pos_iter<=2: hypernym = syns[i].hypernymy() else: hypernym = [] hypernym_str ="" for k1 in range(0,len(hypernym)): Figure 4. Various Possible Sense Combinations for 3 Token query if flag == 1: gloss_hyper = tokenizer(hypernym[k1].gloss()) According to the Figure 5, A2B2C4 is one such hypernym_str = hypernym_str + gloss_hyper +" || " nd nd else: combination where 2 sense of word A, 2 sense of word th hypernym_str = hypernym_str + B and 4 sense of word C is used. hypernym[k1].gloss() +" || " temp.append(hypernym_str) if pos_iter<=0: hyponym = syns[i].hyponymy() else: hyponym = []
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The algorithm provides additional score in case the gloss of the one element of pair matches with the synonym directly, as it increases the probability of referred sense as the correct sense.
Pseudo Code 1: Sense Score Assignment Figure 5. List of all possible sense combinations def scoring_method(string_a,string_b,factor): score = 0 For the example query, possible combinations of senses count = 0 are depicted in the Table 3. start_index = 0 max_count = 0 TABLE III. POSSIBLE COMBINATIONS FOR EXAMPLE QUERY stri = "" string_a_list = string_a.split() Sense Score मत आधार फैसला string_b_list = string_b.split() (Opinion) (According) (Decision) iter1 = 0 A B C S 1 1 1 1 iter2 = 0 A1 B1 C2 S2 while iter1
Figure 6. All possible pair of relations used to score a 2 token long query sentence. Pseudo Code 2:
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Int. J. Com. Dig. Sys. #, No.#, ..-.. (Mon-20..)11 def lesk_algorithm(dataset): f = open("out.txt", "w") dataset_comb = [] temp_list = [] for keys1 in dataset.keys(): temp_list.append(keys1) dataset_comb.append(list(range(0,len(dataset[keys1])))) Figure 7. Output of the Example Sentence print(dataset_comb) print(len(list(itertools.product(*dataset_comb)))) 3.2. Hindi WordNet Database sense_comb = list(range(0,4*len(dataset.keys()))) max_sense_score = 0 Typical dictionary has words associated with their factor =0 meanings. Humans can dissect the sense of a sentence by for list_iter in list(itertools.product(*dataset_comb)): simply looking into the meaning of its component words sense_score = 0 in the dictionary. Human brain is way more proficient in for first_sense_iter in sense_comb: disambiguating a sentence while computer system cannot first_word_index = int(first_sense_iter/4) rely on such a dictionary. Thus computer system requires first_sense_index = first_sense_iter%4 a way more advanced form of the dictionary in order to second_sense_iter = int(first_sense_iter/4)*4+4 find relation between words and their different senses. while second_sense_iter
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The word मनुष्य(mnʊ[6]ʂj) is a noun in Hindi, hence all 4. TIME COMPLEXITY OF PROPOSED SYSTEM the words in this synset are noun themselves. Similarly for Let, query sentence is ‘l’ words long and it contains ‘ ’ any given word we can have different synsets for the different parts of speech that it can be used such as verbs, Stop words and ‘ ’ word with no useful senses (words adjective or adverb. with no sense as noun, verb, adverb or adjective). For example the word खेल (Khel (Hindi), Play (English)) Tokens after the filtering and stemming step = l - - = has 7 senses as a noun and 10 senses as verb and thus has l’ different synsets for each of these senses in the WordNet, Assume, each of the l’ tokens having , …. thus 17 in total. Senses associated with them. We also have hypernyms and hyponyms for each word. Let, Hyponymy shows the relationship between a generic term (hypernym) and a specific instance of it (hyponym). A Number of Total possible sense combination = x hyponym is a word or phrase whose semantic field is x…..x = C more specific than its hypernym. It is required to calculate sense score for n token long For the word खेल, its hypernym synset is: query. खेलक द(kʰeːlkuː[6]d[2]), खेल-क द(kʰeːl-kuː[6]d[2]), Then total number of sense details pair =n x 4 x (n-1) x 4 2 खेलक द(kʰeːlkuː[6]d[2]), म ज-मस्त (mɔːdʒ-mst[2]ɪ[6]), operations= O (n )
म जमस्त( mɔːdʒ mst[2]ɪ[6]), म जमस्त (mɔːdʒmst[2]ɪ[6]), Let, each sense detail pair is L lengths long. Time खेल(kʰeːl), क्र (डा krɪɪ[6]ɽaː), द्धखलवाड(kʰɪ[6]ʋ[4]aːɽ), complexity require to compare a sense detail pair = O (L2 खेलवाड(kʰeːʋ[4]aːɽ), आक्र (aडन ːkrɪɪ[6]ɖ[2]n) ) This complexity can be further improved by using any which is identified by the definition: efficient string matching algorithm like KMP algorithm मनबहलानेयाव्यायामकेकलएउछल-क द, द ड- or Robin Karp algorithm etc. ध पयाऔरक ईमन रींजकक (mnर्त् bɦlaːneː jaː ʋ[4]jaːjaːm Overall complexity of the algorithm = O (C x L2 x n2 ) keː lɪ[6]jɛː ʊ[6]tʃʰl- kuː[6]d[2] d[2]ɔːɽ-dʱ[2]uː[6]p jaː ɔːr koːiː[6] mnoːr◌̃dʒk krɪt[2]j) The hyponyms of the wordखेलwould be: कबकलयड यस(bɪ[6]lɪ[6]jrɖ[2]s), अींताक्षर( ə[5]ू̃t[2]aːʃiː[6]), 5. METHODOLOGY कबड्ड(k bɖ[2] ɖ[2] iː[6])and so on. Each of the sub steps explained in the architecture is elaborated here with an example sentence in Hindi. We define gloss as the definition for each synset. From now on, gloss will be used to refer to any definition for a Input Hindi Sentence: “आर् मᴂ मेरा घर जल र्या” (aːɡ synset. meː◌ meːraː ɡʱr dʒl ɡjaː) Some other notations: ̃ 1. Synset(word): Represents the set of all words present आर्(aːɡ) (Aag), मᴂ(meː◌̃) (me), मेरा, (meːraː) (mera), in the synset of the given word. घर(ɡʱr) (ghar), जल(dʒl) (jol), र्या(ɡjaː) (gayaa) English Sentence: “My house got burnt in the fire”. 2. Synset(hypernym(word)): Represents the synset of Figure 8 shows the Stop word in the given example the hyponym of a given word. sentence. Sentence consists of ‘मᴂ’which is a stop word.
3. Gloss(synset(word)): Represents the gloss(definition) 0 1 2 3 4 5 of the synset to which the word is associated. आर् मᴂ मेरा घर जल र्या
In addition we’re using indicnlp python library in order to perform stemming of the query sentence.i.e. Seperation of Stop Word Figure 8. Removing Stop Words from the Hindi Sentence various forms of verbs into its root sentence for further processing i) Filtered query after stop words elimination has been [“ब लते”(boːlt[2]eː),”ब लना”(boːlnaː),”ब ले”(boːleː),”ब ल given in Figure 9. र्े”boːloː ɡ eː] all four words mapped to a single verb ब ल(boːl) [to speak]. 0 1 2 3 4 5 आर् - मेरा घर जल र्या Figure 9. After Stop Word Elimination
ii) Stemming find the root word associated.
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Example: For word ‘जल’(dʒl) (Jal), root word is ‘जलाना’ v) Dataset is used to generate different sense (dʒlaːnaː) (Jalanaa). combinations, each of which is evaluated using the Now, the Stemmed sentence has been given in Figure 10. designed algorithm.
0 1 2 3 4 5 Token आर् घर जल,जलाना र्या आर् - मेरा घर जल,जलाना र्या No. of senses 3 10 11 3 Figure 10. Stemmed Sentence Figure 14. Dataset Format iii) POS tagging identifies the part of speech associate According to Figure 15, Total no. of sense combinations with each token in the filtered sentence. If there is no possible are = 3 x 10 x 11 x 3 = 990 combinations. noun, adjective, verb, adverb sense associated with the token then algorithm simply reject that token as all the vi) Each of the 990 combinations are evaluated and one words belong to remaining part of speech is unisemous with the maximum score assigned as the output of the that does not need any disambiguation i.e. preposition, algorithm. conjunction etc. Figure 11 and Figure 12 shows the rejection of unisemous word. Python has been used to implement this algorithm. Python is an open source software tool and it is 0 1 2 3 4 5 available in Web with its all modules and supporting आर् - मेरा घर जल,जलाना र्या libraries. The detailed software tools and libraries of this Noun Pronoun Noun Verb,noun Noun,ver proposed system have been listed below. b i. Python 3.6: Basic environment for the application ii. NLTK python library: Natural language Unisemous word processing library comprise of basic NLP Figure 11. Unisemous word methods iii. CLTK python library: In order to inherit the stop 0 1 2 3 4 5 word list for Hindi language आर् - - घर जलाना र्या iv. Itertools python library: To successfully generate various sense combination in order to evaluate them Figure 12. Stemmed Sentence for score. v. Indicnlp python library: For the morphological iv) Dataset generation step form a dataset that include all processing of data to support stemming. possible sense of the token left in the filtered sentence. A component of generated dataset is 6. APPLICATIONS OF THE PROPOSED SYSTEM depicted in the Figure 13. The proposed algorithm is useful in systems, which includes analysis of a query to recommend related queries. In case user is not able to exactly describe the desired query, related results are suggested to user by this system. This system has many applications like in Pension Systems, where senior citizen can use words with which they are comfortable. Sentiment analysis of news headlines in order to predict the stock prices is another possible business application of the proposed algorithm. Positive and negative news have vast impact on the present trend for a stock. An automated system predicting the effect of a news on a stock price can use our proposed algorithm. The dissected sense can be used to various text summarization problems. Text summarization can use word sense disambiguation as one of their sub problems. Proposed algorithm can enhance the performance of text summarization algorithm and improve the efficiency of a search engine by understanding what the user is actually looking for after dissecting the input query.
Figure 13. Dataset Format
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7. FUTURE WORK good performance over constrained input data. Despite Proposed algorithm finds many combinations for sense unavailability of large amount of ambiguous data tokens. It is not possible always to evaluate each of the involving polysemous words in Hindi language for combinations to get the correct sense as an output. testing, the proposed algorithm successfully dissects the Because even after numerous combinations that generate sense for most of the test queries provided. several senses, the correct or desired sense may not be ACKNOWLEDGMENT derived as the output. Thus, we may incorporate some This research work is done at the Research Project Lab heuristic that can approximate our output to a near under Dept. of Computer Science and Engineering of optimal result. Though, it is observed that such kind of National Institute of Technology (NIT), Durgapur. The queries does not occur too often in the natural language authors would like to thank Dept. of Computer Science domain. Future work lies on identifying one or two and Engineering, NIT, Durgapur, India for academically heuristic methods based on genetic algorithm that may be supporting this research work. used to provide a near optimal solution. Even deep learning based algorithm may be used in future to REFERENCES identify the correct sense of an ambiguous word in Hindi sentences. Hypernym, hyponym, gloss of each sense [1] S. Malviya, R. Mishray, U. S. Tiwary, “Structural Analysis of consist of various stop words and words with no Hindi Phonetics and A Method for Extraction of Phonetically Rich Sentences from a Very Large Hindi Text Corpus”, 2016 available senses. Incorporating these words affect the Conference of The Oriental Chapter of International Committee performance of the algorithm. One possible solution to for Coordination and Standardization of Speech Databases and eliminate these words is to process them again by Assessment Technique (O-COCOSDA), Bali, Indonesia, 2016, performing morphological analysis. But re-processing pp. 1-6. will increase the time complexity of the algorithm. As [2] M.E. Lesk, “Automatic Sense Disambiguation Using Machine Readable Dictionaries: How to Tell a Pine Cone from an Ice discussed earlier, this algorithm depends hugely on the Cream Cone”, Proc. SIGDOC Conference, Toronto, Ontario, semantic collection of words and their meanings June, 1986, pp. 24-26. provided by the WordNet. So the performance of the [3] A.M. Butnaru and R. T. Ionescu, “ShotgunWSD 2.0: An algorithm depends hugely on the WordNet. Dataset Improved Algorithm for Global Word Sense Disambiguation,” in generation is a major step in the proposed algorithm. As IEEE Access, vol. 7, 2019, pp. 120961-120975. it segregates only a portion of the data from WordNet, [4] Preeti Dubey, “Word Sense Disambiguation in Natural Language Due to which the database needs to be queried again and Processing”, JK Research Journal in Mathematics and Computer again. So an efficient execution of querying operation of Sciences, vol. 1(1), 2018, pp.114-120. WordNet can further enhance the performance of the [5] M. Y. Kang, T. H. Min and J. S. Lee, “Sense Space for Word Sense Disambiguation”, 2018 IEEE International Conference on proposed algorithm. The proposed system can be Big Data and Smart Computing (BigComp), Shanghai, 2018, pp. enhanced using machine learning algorithm or deep 669-672. learning algorithms. [6] R. Navigli, “Word sense disambiguation: A survey”, ACM Comput. Surv., vol. 41, no. 2, Art. no. 10, 2009.
8. CONCLUSION [7] A.G. Chifu and R. T. Ionescu, “Word sense disambiguation to improve precision for ambiguous queries”, Central Eur. J. The proposed algorithm provides a word sense Comput. Sci., vol. 2, no. 4, 2012, pp. 398-411. disambiguation solution for the Hindi language. The [8] M. N. Asim, M. Wasim, M. U. Ghani Khan, N. Mahmood and W. Proposed method rightly, gives pair of words and their Mahmood, “The Use of Ontology in Retrieval: A Study on evaluated senses as an outcome of the system. Novel Textual, Multilingual, and Multimedia Retrieval”, in IEEE Access, vol. 7, 2019, pp. 21662-21686. scoring method used with Lesk alogorithm gives improved performance in disambiguating the ambiguous [9] A.Sumanth and D. Inkpen, “How much does word sense disambiguation help in sentiment analysis of micropost data?”, in words for the Hindi language. Hindi WordNet database Proc.WASSA, 2015, pp. 115-121. designed by CFILT IIT Bombay is very useful in solving [10] G. Xu, Z. Yu, H. Yao, F. Li, Y. Meng and X. Wu, “Chinese Text Hindi word sense disambiguation problem. To improve Sentiment Analysis Based on Extended Sentiment Dictionary”, the performance of Lesk algorithm, we have used the IEEE Access, vol. 7, 2019, pp. 43749-43762. scoring method which assigns scores to the sense of the [11] M. Carpuat and D. Wu, “Improving statistical machine translation tokens depending on the combinations of sense tokens. using word sense disambiguation”, in Proc. EMNLP, 2007, pp. Word gloss, hypernym, hyponym and synonym are very 61-72. useful for calculating the sense score. The time [12] X. Pu, N. Pappas, J. Henderson, and A. Popescu-Belis, complexity of the proposed algorithm is O(n2) where n is “Integrating weakly supervised word sense disambiguation into neural machine translation”, Trans. Assoc. Comput. Linguistics, the number of tokens. Though algorithm imposes vol. 6, 2018, pp. 635-649. restriction on the length of the query sentence, it yields a
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[13] J. N. Madhuri and R. Ganesh Kumar, “Extractive Text [28] M. Mahmood, M. Hourali “Semi-supervised approach for Persian Summarization Using Sentence Ranking”, International word sense disambiguation”, 7th International Conference on Conference on Data Science and Communication (IconDSC), Computer and Knowledge Engineering (ICCKE), 2017, pp. 104- Bangalore, India, 2019, pp. 1-3. 110. [14] L. Plaza, A. J. Jimeno-Yepes, A. Díaz, and A. R. Aronson, [29] G. Sajini , Jagadish S. Kallimani, “Recognition and “Studying the correlation between different word sense disambiguation of polysemy words in entered hindi documents”, disambiguation methods and summarization effectiveness in International Conference on Electrical, Electronics, biomedical texts”, BMC Bioinf., vol. 12, Art. no. 355, 2011. Communication, Computer, and Optimization Techniques (ICEECCOT), 2017, pp. 1-8. [15] A.Taherkhani, A. Belatreche, Y. Li and L. P. Maguire, “A Supervised Learning Algorithm for Learning Precise Timing of [30] S. Gopal , R. P. Haroon, “Malayalam word sense disambiguation Multiple Spikes in Multilayer Spiking Neural Networks”, IEEE using Naïve Bayes classifier”, International Conference on Transactions on Neural Networks and Learning Systems, vol. 29, Advances in Human Machine Interaction (HMI), Doddaballapur, no. 11, 2018, pp. 5394-5407. 2016, pp. 1-4. [16] M. Usama et al., “Unsupervised Machine Learning for [31] X. Wang , X. Tang , W. Qu , M. Gu, “Word sense disambiguation Networking: Techniques, Applications and Research Challenges”, by semantic inference”, International Conference on Behavioral, IEEE Access, vol. 7, 2019, pp. 65579-65615. Economic, Socio-cultural Computing (BESC), Krakow, 2017, pp. 1-6. [17] L. Stanchev, “Semantic Document Clustering Using Information from WordNet and DBPedia”, 2018 IEEE 12th International [32] S. Nazah, M. M. Hoque, Md. R. Hossain, “Word sense Conference on Semantic Computing (ICSC), Laguna Hills, CA, disambiguation of Bangla sentences using statistical approach”, 2018, pp. 100-107. 3rd International Conference on Electrical Information and Communication Technology (EICT), 2017, pp. 1-6. [18] R. Pandit, S. Sengupta, S. K. Naskar and M. M. Sardar, “Improving Lesk by Incorporating Priority for Word Sense [33] S. Banerjee, T. Pedersen, “An adapted lesk algorithm for word Disambiguation”, Fifth International Conference on Emerging sense disambiguation using wordnet”, Computational Linguistics Applications of Information Technology (EAIT), Kolkata, 2018, and Intelligent Text Processing, Springer, 2002, pp 136-145. pp. 1-4. UTHORS [19] S. Tang , F. Ma, X. Chen , P. Zhang, “Deep Chinese Word Sense A Disambiguation Method Based on Sequence to Sequence”, International Conference on Sensor Networks and Signal Praffullit Tripathi is a Software Processing (SNSP), 2018, pp. 498-503. Engineer in Qualcomm Company. He [20] A.Raganato, J. Camacho-Collados, R. Navigli, “Word Sense has completed his B.Tech-IT degree Disambiguation: A Unified Evaluation Framework and Empirical from National Institute of Technology, Comparison”, 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, 2017, pp. Durgapur, India. He has sucessfully 99-110. completed his summer internship at Qualcomm Company. He has a good [21] Q. P. Nguyen, A. D. Vo, J. C. Shin, P. Tran, C. Y. Ock, “Korean- interest in Implementation of Natural Vietnamese Neural Machine Translation System With Korean Morphological Analysis and Word Sense Disambiguation”, IEEE Language Processing based Applications Access, vol. 7, 2019, pp. 32602-32616. like Word Sense Disambiguation, Query-Response Model, Question Answering System, Sentiment Analysis, etc. [22] S. Seifollahi, M. Shajari, “Word sense disambiguation application in sentiment analysis of news headlines: an applied approach to FOREX market prediction”, Journal of Intelligent Information Prasenjit Mukherjee has 12 years of Systems, vol. 52(1), 2019, pp 57–83. experience in academics and industry. He was a fulltime Ph.D. Research Scholar in [23] Z. Vaishnav, P. S. Sajja “Knowledge-Based Approach for Word Computer Science and Engineering in the Sense Disambiguation Using Genetic Algorithm for Gujarati”, ICTIS, vol. 1, 2018, pp. 485-494. area of Natural Language Processing from National Institute of Technology (NIT), [24] S. N. Mohan Raj, S. S. Kumar, S. Rajendran, K. P. Soman, “Word Durgapur, India under the Visvesvaraya Sense Disambiguation of Malayalam Nouns”, Recent Advances in Computational Intelligence, Springer, 2019, pp 291-314. PhD Scheme from 2015 to 2019. Presently, He is working as a Data [25] J. Singh, I. Singh, “Word Sense Disambiguation: Enhanced Lesk Scientist under Analytics and IT Department,RamanByte, Pune Approach in Punjabi Language”, International Journal of Institute of Business Management, Pune, Maharashtra, India. Computer Applications, vol. 129(6), 2015, pp. 23-27.
[26] L. R. Pillai, V. Gangadharan, D. Gupta “A Combined Approach Manik Hendre has 5 years of experience Using Semantic Role Labelling and Word Sense Disambiguation in academics and industry. He is a Ph.D. for Question Generation and Answer Extraction”, Second research scholar at the University of Pune. International Conference on Advances in Electronics, Computers and Communications (ICAECC), Bangalore, 2018, pp. 1-6. He is currently working as a Data Scientist in RamanByte Pune. His [27] H. Walia, A. Rana, V. Kansal, “A Naïve Bayes Approach for research areas include Biometrics Image working on Gurmukhi Word Sense Disambiguation”, 6th International Conference on Reliability, Infocom Technologies Processing, Machine learning and Data and Optimization (Trends and Future Directions) (ICRITO), 2017, Analytics. Noida, pp. 432-435.
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16 Author Name:Paper Title …
Dr. Manish Godse has 25 years of experience in academics and industry. He holds Ph.D. from Indian Institute of Technology, Bombay (IITB). He is currently working as an Industry Professor and IT Director in the PIBM, Pune in the area of Artificial Intelligence and Analytics. His research areas of interest include automation, machine learning, natural language processing and business analytics. He has multiple research papers indexed at IEEE, ELSEVIER, INDERSCIENCE, etc.
Dr. Baisakhi Chakraborty received the PhD. degree in 2011 from National Institute of Technology, Durgapur, India in Computer Science and Engineering. Her research interest includes knowledge systems, knowledge engineering and management, database systems, data mining, natural language processing and software engineering. She has several research scholars under her guidance. She has more than 60 international publications. She has a decade of industrial and 14 years of academic experience.
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International Journal of Computing and Digital Systems ISSN (2210-142X) Int. J. Com. Dig. Sys. #, No.# (Mon-20..)
Response of Reviewer Comments
Paper #1570637815 ('Word Sense Disambiguation in Hindi Language Using Score Based Modified Lesk Algorithm')
Sl. No. Reviewer Comments Response of Reviewer Comments Reviewer 1 1 1. The paper is presented in a very comprehensive 1. Thank you to Reviewer 1 for his/her positive way. comments on our research paper.
2. Results are very interesting. 2. Results have been given clearly in this research paper and thank you to Reviewer 1 for his/her 3. Authors collected very interesting data and used positive comments on Results. python as a programming language and data 3. Python language has been used because today mining techniques to data mining and processing. python is a most famous computer language in computer science. Hindi WordNet of IIT, Bombay, 4. I would accept this paper as novel research work India has been used in this WSD because WordNet and recommend to accept this paper. on regional languages is still in developing state. The IIT, Bombay has developed Hindi WordNet which is very useful for NLP researcher. 4. Thank you to Reviewer 1 for recommendation to accept our paper.
Reviewer 2 2 1. The problem undertaken is quite interesting and 1. Thank you to Reviewer 2 for his/her positive review important. Solution to the Word sense comment on our research work. disambiguation (WSD) problem will open opportunities to Hindi savvy people to use relevant 2. The Lesk algorithm has been modified by using web applications. scoring method. Lesk is a most popular algorithm in word sense disambiguation. The Lesk algorithm has 2. The authors have used Lesk algorithm improved been used to implement a WSD system on Hindi by novel scoring method. language.
3. The results are encouraging. 3. Thank you to Reviewer 2 for his/her positive comment on results. 4. Larger size of the data set could have strengthened the validation of the solution. 4. Yes, larger size of the data set could have strengthened the validation of the solution. But 5. Implementation of the Lesk algorithm in getting larger size of dataset on regional language is conjunction with improved scoring method makes difficult. We will improve our system accordingly a fair contribution towards solving the problem of which has been discussed in the future work section WSD. of the paper.
6. It will open the opportunities in web based 5. Thank you to Reviewer 2 for his/her positive applications including query-response system, comment on implementation of the Lesk algorithm. question-answer system and sentiment analysis. 6. Yes, WSD system can be used in web based 7. A promising solution to WSD has been proposed applications like query-response system, question-
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18 Author Name:Paper Title …
and reasonably validated against the answer system and sentiment analysis. The proposed HindiWordNet data base. WSD system can be modified for multiple regional languages and that will be an added advantage for 8. The results are important. such applications. 7. The Hindi WordNet has been designed by the IIT, Bombay which is very useful for NLP research or NLP based system implementation. Thank you to Reviewer 2 for his/her positive comment.
8. Thank you to Reviewer 2 for his/her positive comment on results.
Reviewer 3 3 1. The architecture and the methodology of the 1. Thank you to Reviewer 3 for his/her positive proposed WSD system is well builded and clearly comment on the architecture and the methodology explained. of our proposed WSD system.
2. As we all know, the accuracy of the machine 2. From a scientific point of view, the paper lacks of learning algorithm highly depends on the available novelty and scientific innovation. Today, modern training data. WSD solutions include machine learning NLP based system implementation on regional techniques. language is very difficult. Because the unavailability of regional language data. Computer 3. From a scientific point of view, the paper lacks of supporting script, regional language dataset novelty and scientific innovation. generation are the other major issues. We have implemented our WSD system using Hindi 4. The paper is in fact a well and properly done study WordNet by IIT, Bombay. Lesk is a most popular that involves a fairly modification of the most and successful algorithm in WSD. The Lesk popular algorithm in this field. algorithm has been modified by scoring method. The Machine Learning or Deep Learning algorithm can be useful to get better result in Hindi Word Sense Disambiguation and we have mentioned it as our further enhancement in Future Work section.
3. The Lesk algorithm has been modified using the novel scoring method. The traditional Lesk algorithm is not sufficient to solve the Hindi Disambiguation problem. The scoring method has been used to disambiguate the Hindi words. We have got good results on Hindi sentences and Time complexity of proposed system has been given which is O(n2). Word Sense Disambiguation on regional languages is an open research problem and it is less researched.
4. The paper has been modified again according to Reviewer 3 comments and Thank you to Reviewer 3 for his/her positive comments.
Reviewer 4 4 1. The paper contains interesting algorithm and 1. Yes, WSD system can be used in web based practical issue for the ease of using web applications like query-response system, question-
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applications for unfamiliar users with English, answer system and sentiment analysis. The proposed which considered important issue. However, the WSD system can be modified for multiple regional language of the work must be written in good languages and that will be added advantage for such English language which was not, there are a lot of applications. This paper has been revised and English semantic and syntax errors, example from grammatical and language corrections have been the abstract: Being many citizens aren’t using done according to Reviewer 4 comment to eliminate English, the major challenges in to manage semantic and syntax errors. Ambiguous Hindi words, the inappropriate meaning always leads to chaos and increase 2. Thank you to Reviewer 4 for his/her positive support load. So I suggest the writers to revise the comment. The proposed algorithm has been tested paper and resubmit. using Hindi WordNet database which is also available in IIT, Bombay website. Maximum Hindi 2. The work is well analytical and procedural methods words and their meanings are available that are for addressing the problem of catching the semantic essential for our proposed algorithm. The extraction of the Hindi, still the algorithm must be tested by of meaning of ambiguous Hindi word has been developing a testing experiments for well clearly described in Architecture and Methodology evaluation. from my point view further practical section as an experiment. experiments are a must.
Reviewer 5 5 1. The article contains good information and The 1. Thank you to Reviewer 5 for his/her positive algorithm was applied to a set of characters that no comment on the algorithm of our proposed WSD other article used. system.
2. The article does not contain modernity, but it 2. This paper has been revised according to the discusses a topic containing the application of the Reviewer 5 comments. The proposed system has algorithm to the Indian characters. been discussed clearly with architecture and methodology. The Hindi WordNet has been used to 3. One of the most important notes: implement this system that is also available in IIT, a)- The research needs to be reformulated Bombay websites. A comparative analysis among b)- The large number of shapes in the article is not similar type recent WSD systems with proposed desirable WSD system has been done in Related Works c)- Most shapes can be combined and made into one section. The Time complexity has been calculated table which is O (n2). d)- The equation for the algorithm used must be written 3. and the variables used in the algorithm defined a) The paper has been revised according to the Reviewer 5 comment.
b) Unused Shape (Figure 2) has been removed according to the comment of Reviewer 5. Each shape and each table is containing different information and that are given in different steps of algorithm or in sections. For better clarity, each shape and each table are needed for large audiences. Figures and Tables have been given to understand the work of proposed WSD system properly.
c) Each shape and each table is containing different information and that are given in different steps or sections. The characteristics of proposed Hindi WSD system and characteristics Hindi words are clearly visible in these Figures tables.
d) The equation of proposed algorithm has been given from Dataset Generation to Output subsection of
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20 Author Name:Paper Title …
Algorithm Section (Section 3.1) in Architecture of Proposed System (Section 3). The each step of algorithm and its Pseudo code in Python has been clearly described. Today Python is a most popular computer language which is easy to understand for large audience. Variables are defined in pseudo code are also easily understandable for the audience.
Managing Editor Comments 6 Your paper #1570637815 ('Word Sense The paper has been revised according to the Reviewers Disambiguation in Hindi Language Using Score Based comments. The Response of each comment has been Modified Lesk Algorithm') for IJCDS-2020 has been given above. I would like to thank you for acceptance of accepted, but requires major revisions and will be my paper. published in IJCDS after completing all the required correction. The modification section has been highlighted by the Yellow colour.
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