Chatbot Application on Cryptocurrency.Pdf

Total Page:16

File Type:pdf, Size:1020Kb

Chatbot Application on Cryptocurrency.Pdf © 2019 IEEE. Access to this work was provided by the University of Maryland, Baltimore County (UMBC) ScholarWorks@UMBC digital repository on the Maryland Shared Open Access (MD- SOAR) platform. Please provide feedback Please support the ScholarWorks@UMBC repository by emailing [email protected] and telling us what having access to this work means to you and why it’s important to you. Thank you. Chatbot Application on Cryptocurrency Qitao Xie1, Qingquan Zhang2, Dayuan Tan1, Ting Zhu1, Shen Xiao3, Beibei Li4, Lei Sun5, Ping Yi6, Junyu Wang3 1Department of Computer Science and Electrical Engineering, UMBC, USA, fqxie1, dayuan1, [email protected] 2University of Illinois, Urbana Champaign, [email protected] 3Hunan University, [email protected] 4Carnegie Mellon University, [email protected] 5Shanghai Graduate School of Chinese Academy of Social Sciences, [email protected] 6Shanghai Jiaotong University, [email protected] Abstract—Many chatbots have been developed that provide human speech so that users would think that they are inter- a multitude of services through a wide range of methods. acting with other people instead of computers. Technological A chatbot is a brand-new conversational agent in the high- advancement in recent decades, like faster computers, faster speed changing technology world. With the advance of Artificial Intelligence and machine learning, chatbots are becoming more internet service, and the invention of smartphones, makes the and more popular. A chatbot is the extension of human interface information communication and sharing feasible in reasonable mediums such as the phone and social platforms. Similarly, manners. People created machines and software to simulate Cryptocurrency is a new extension of digital or virtual currency a human-like conversation, which was called ChatterBot by designed to work as a medium of exchange. In the current digital Michael Mauldin in 1994 [2], shortened to chatbot later. Since exchanging world, investors and interested parties are eager to know more information about, and the capabilites of, this new then, the term chatbot has been used to refer to conversational type of currency. One of the potential paths to retrieve the info agents that primarily use text-based or speech-based input. The automatically and quickly is through a chatbot. We explored idea of combining artificial intelligence and the interaction of the open source python library, Chatterbot, to apply Itchat API natural form sped up the development of new chatbot plat- (a WeChat interface) with the aim of building a robot chatting forms. Chatbots are immensely popular at the moment. Tech application, I&C Chat, on the topic of cryptocurrency. First, we collected question and answer pairs datasets from Quora giants’ core products such as IBM Watson, Amazon Alexa, websites. Furthermore, we also created API calls to query the Apple Siri are some examples of popular chatbots. Facebook real time quote for the top 25 cryptocurrencies. Then we used created an online assistant named M and already has bots for the collected data to train our chatbot and implemented a logic small things, like booking a haircut or sending flowers, on its adapter to receive the price quote of cryptocurrencies based on Messenger app. IBM created Watson, a question answering the incoming question. The Itchat API method will return the best matched answer to the asking party automatically. The response computer system, which proved its power by winning the time of different questions has been investigated. The results popular quiz show Jeopardy. Google is also working on a imply that this application is quite useful, feasible and beneficial chat-bot based app to answer user questions. to the digital currency world. Another thing making chatbots popular is the ease with Index Terms—Chatbot, Cryptocurrency which anyone can create one themselves, without any pro- gramming skills needed. As of now, some of the popular I. INTRODUCTION chatbot platforms are: Microsoft Bot platform, Pandorabots, The interaction in the format of speech or text between Facebook Bots for Messenger. Today chatbots are used in humans and computers is gaining more and more in popularity education, business, and in information retrieval processes. nowadays. People expect to have similar experiences when Today, commercial applications of chatbots include: travel they talk to machines as when they talk to human beings. agents, customer service support [3], technical support, per- In order to provide suitable responses based on phrases or sonal assistants, education, marketing, entertainment, legal keywords taken from questions, a dialogue system or program and financial fields, and so on. The communication between is needed, which is often called a chatbot or a chatterbot. the companies and their clients has reached a new level The chatbot is a computer program that has an ability to of intensity. With the rapid development of technology, the communicate with people by providing answers to questions. customer experience is changing dramatically. Therefore, the People input the natural language speech or text, while the use of chatbots in business can be a crucial issue of improv- program provides the most feasible intelligent response in the ing customer communication and customer support. Finally, form of text or speech. information retrieval processes [4] in the legal and financial The history of chatbots started in the 1950s when computer fields are gaining more and more attention. scientist Alan Turing experimented with the concept of com- There is no doubt that digital service and marketing are municating like humans do. In the 1960s Joseph Weizenbaum developing rapidly, with different platforms reaching the audi- published the ELIZA program [1]. At that time researchers ence through things such as website, e-mail, instant messaging, aimed to examine whether these systems can imitate the and social networks. However, a new exciting communication channel, chatbot, is playing the starring role. People already of multi-platform deploying, group chat usage, knowledge- use it daily – Siri and Cortana, which are intelligent personal acquisition beforehand, and access to external resources. assistants in the form of chatbots used in Apple and Windows The paper is organized as follows: in section II, we will devices. With chatbot, you can easily deliver any information introduce why we want to design and implement a chatbot to the potential end-user by creating some pre-defined conver- system on the cryptocurrency topic, then in section III the sations. details of each part of our design will be explained, section Cryptocurrency is new to many people but it has been get- IV illustrates our evaluation and results, and we will conclude ting very hot recently. A cryptocurrency is a digital or virtual in section V. currency designed to work as a medium of exchange [5]. II. MOTIVATION AND BACKGROUND STUDY Different from the conventional currencies, a cryptocurrency uses cryptography to secure and verify transactions as well as People pay attention to cryptocurrency for a multitude of control the creation of new units of a particular cryptocurrency. reasons. One of the most important motives is that in the It was introduced in 2009 but did not grab mainstream media past three years the market capitalization and volume have and people’s eyes until 2012. It may have the potential to had a massive increase. As of May 6, 2018, the total market compete against traditional payment methods like cash or capitalization of cryptocurrencies is bigger than 450 billion check, and other online payment method like credit and debit USD and the record high daily volume is larger than 25 billion cards and PayPal [6]. USD [7], which is almost 1400% bigger than it was in March The construction of a new chatbot steeped in the topic of 2017. Huge capitalization comes along with a huge profile cryptocurrency may prove more than just a novel project. On margin. More and more people are attracted to this field in the one hand, people eagerly want to obtain information on the the hope of cashing in on the trend. topic of cryptocurrency and a quick price quote. On the other On the way of getting on board there are so many new hand, chatbot is a very good conversational agent to meet this terms and concepts in the cryptocurrencies field, as well as requirement, which can provide the information in a human- detailed operations corresponding to this topic, especially for to-human manner. Our goal in this study was to identify a beginners. Meanwhile that information on the internet is so technology for building a chatbot that fulfills the technical scattered that it takes a long time to search and collect all requirements and needs of today’s exciting crypto-market. The of what you want. It would be very convenient if the quick ideal technology should meet the following requirements: be and accurate answers to what we want to know were readily based on an open source platform, to be simple enough and available. One of the best approaches is through a chatbot, a easy enough to implement, and facilitate the support from an human-like conversation application. open source community. The selected technology should be According to the report [8], the chatbot interface has been deployed onto platforms that can be easy to access. very popular and maintains a steady growth. It has been used There are some cryptocurrency information bots in the in Instant Messaging systems, such as Facebook Messenger, market, but most of them are hosted on a specific platform Google Assistant, Amazon Alexa, and so on. Indeed, Instant such as Facebook messenger. Some of them also limit one- Messaging has more active users than any other social network on-one chatting. For some bots, knowledge information is or mail application [9]. Nearly 4 billion users are accounted for collected on the fly so that it slows down the response.
Recommended publications
  • Intelligent Chat Bot
    INTELLIGENT CHAT BOT A. Mohamed Rasvi, V.V. Sabareesh, V. Suthajebakumari Computer Science and Engineering, Kamaraj College of Engineering and Technology, India ABSTRACT This paper discusses the workflow of intelligent chat bot powered by various artificial intelligence algorithms. The replies for messages in chats are trained against set of predefined questions and chat messages. These trained data sets are stored in database. Relying on one machine-learning algorithm showed inaccurate performance, so this bot is powered by four different machine-learning algorithms to make a decision. The inference engine pre-processes the received message then matches it against the trained datasets based on the AI algorithms. The AIML provides similar way of replying to a message in online chat bots using simple XML based mechanism but the method of employing AI provides accurate replies than the widely used AIML in the Internet. This Intelligent chat bot can be used to provide assistance for individual like answering simple queries to booking a ticket for a trip and also when trained properly this can be used as a replacement for a teacher which teaches a subject or even to teach programming. Keywords : AIML, Artificial Intelligence, Chat bot, Machine-learning, String Matching. I. INTRODUCTION Social networks are attracting masses and gaining huge momentum. They allow instant messaging and sharing features. Guides and technical help desks provide on demand tech support through chat services or voice call. Queries are taken to technical support team from customer to clear their doubts. But this process needs a dedicated support team to answer user‟s query which is a lot of man power.
    [Show full text]
  • An Ensemble Regression Approach for Ocr Error Correction
    AN ENSEMBLE REGRESSION APPROACH FOR OCR ERROR CORRECTION by Jie Mei Submitted in partial fulfillment of the requirements for the degree of Master of Computer Science at Dalhousie University Halifax, Nova Scotia March 2017 © Copyright by Jie Mei, 2017 Table of Contents List of Tables ................................... iv List of Figures .................................. v Abstract ...................................... vi List of Symbols Used .............................. vii Acknowledgements ............................... viii Chapter 1 Introduction .......................... 1 1.1 Problem Statement............................ 1 1.2 Proposed Model .............................. 2 1.3 Contributions ............................... 2 1.4 Outline ................................... 3 Chapter 2 Background ........................... 5 2.1 OCR Procedure .............................. 5 2.2 OCR-Error Characteristics ........................ 6 2.3 Modern Post-Processing Models ..................... 7 Chapter 3 Compositional Correction Frameworks .......... 9 3.1 Noisy Channel ............................... 11 3.1.1 Error Correction Models ..................... 12 3.1.2 Correction Inferences ....................... 13 3.2 Confidence Analysis ............................ 16 3.2.1 Error Correction Models ..................... 16 3.2.2 Correction Inferences ....................... 17 3.3 Framework Comparison ......................... 18 ii Chapter 4 Proposed Model ........................ 21 4.1 Error Detection .............................. 22
    [Show full text]
  • Practice with Python
    CSI4108-01 ARTIFICIAL INTELLIGENCE 1 Word Embedding / Text Processing Practice with Python 2018. 5. 11. Lee, Gyeongbok Practice with Python 2 Contents • Word Embedding – Libraries: gensim, fastText – Embedding alignment (with two languages) • Text/Language Processing – POS Tagging with NLTK/koNLPy – Text similarity (jellyfish) Practice with Python 3 Gensim • Open-source vector space modeling and topic modeling toolkit implemented in Python – designed to handle large text collections, using data streaming and efficient incremental algorithms – Usually used to make word vector from corpus • Tutorial is available here: – https://github.com/RaRe-Technologies/gensim/blob/develop/tutorials.md#tutorials – https://rare-technologies.com/word2vec-tutorial/ • Install – pip install gensim Practice with Python 4 Gensim for Word Embedding • Logging • Input Data: list of word’s list – Example: I have a car , I like the cat → – For list of the sentences, you can make this by: Practice with Python 5 Gensim for Word Embedding • If your data is already preprocessed… – One sentence per line, separated by whitespace → LineSentence (just load the file) – Try with this: • http://an.yonsei.ac.kr/corpus/example_corpus.txt From https://radimrehurek.com/gensim/models/word2vec.html Practice with Python 6 Gensim for Word Embedding • If the input is in multiple files or file size is large: – Use custom iterator and yield From https://rare-technologies.com/word2vec-tutorial/ Practice with Python 7 Gensim for Word Embedding • gensim.models.Word2Vec Parameters – min_count:
    [Show full text]
  • NLP - Assignment 2
    NLP - Assignment 2 Week 2 December 27th, 2016 1. A 5-gram model is a order Markov Model: (a) Six (b) Five (c) Four (d) Constant Ans : c) Four 2. For the following corpus C1 of 3 sentences, what is the total count of unique bi- grams for which the likelihood will be estimated? Assume we do not perform any pre-processing, and we are using the corpus as given. (i) ice cream tastes better than any other food (ii) ice cream is generally served after the meal (iii) many of us have happy childhood memories linked to ice cream (a) 22 (b) 27 (c) 30 (d) 34 Ans : b) 27 3. Arrange the words \curry, oil and tea" in descending order, based on the frequency of their occurrence in the Google Books n-grams. The Google Books n-gram viewer is available at https://books.google.com/ngrams: (a) tea, oil, curry (c) curry, tea, oil (b) curry, oil, tea (d) oil, tea, curry Ans: d) oil, tea, curry 4. Given a corpus C2, The Maximum Likelihood Estimation (MLE) for the bigram \ice cream" is 0.4 and the count of occurrence of the word \ice" is 310. The likelihood of \ice cream" after applying add-one smoothing is 0:025, for the same corpus C2. What is the vocabulary size of C2: 1 (a) 4390 (b) 4690 (c) 5270 (d) 5550 Ans: b)4690 The Questions from 5 to 10 require you to analyse the data given in the corpus C3, using a programming language of your choice.
    [Show full text]
  • 3 Dictionaries and Tolerant Retrieval
    Online edition (c)2009 Cambridge UP DRAFT! © April 1, 2009 Cambridge University Press. Feedback welcome. 49 Dictionaries and tolerant 3 retrieval In Chapters 1 and 2 we developed the ideas underlying inverted indexes for handling Boolean and proximity queries. Here, we develop techniques that are robust to typographical errors in the query, as well as alternative spellings. In Section 3.1 we develop data structures that help the search for terms in the vocabulary in an inverted index. In Section 3.2 we study WILDCARD QUERY the idea of a wildcard query: a query such as *a*e*i*o*u*, which seeks doc- uments containing any term that includes all the five vowels in sequence. The * symbol indicates any (possibly empty) string of characters. Users pose such queries to a search engine when they are uncertain about how to spell a query term, or seek documents containing variants of a query term; for in- stance, the query automat* would seek documents containing any of the terms automatic, automation and automated. We then turn to other forms of imprecisely posed queries, focusing on spelling errors in Section 3.3. Users make spelling errors either by accident, or because the term they are searching for (e.g., Herman) has no unambiguous spelling in the collection. We detail a number of techniques for correcting spelling errors in queries, one term at a time as well as for an entire string of query terms. Finally, in Section 3.4 we study a method for seeking vo- cabulary terms that are phonetically close to the query term(s).
    [Show full text]
  • Use of Word Embedding to Generate Similar Words and Misspellings for Training Purpose in Chatbot Development
    USE OF WORD EMBEDDING TO GENERATE SIMILAR WORDS AND MISSPELLINGS FOR TRAINING PURPOSE IN CHATBOT DEVELOPMENT by SANJAY THAPA THESIS Submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science at The University of Texas at Arlington December, 2019 Arlington, Texas Supervising Committee: Deokgun Park, Supervising Professor Manfred Huber Vassilis Athitsos Copyright © by Sanjay Thapa 2019 ACKNOWLEDGEMENTS I would like to thank Dr. Deokgun Park for allowing me to work and conduct the research in the Human Data Interaction (HDI) Lab in the College of Engineering at the University of Texas at Arlington. Dr. Park's guidance on the procedure to solve problems using different approaches has helped me to grow personally and intellectually. I am also very thankful to Dr. Manfred Huber and Dr. Vassilis Athitsos for their constant guidance and support in my research. I would like to thank all the members of the HDI Lab for their generosity and company during my time in the lab. I also would like to thank Peace Ossom Williamson, the director of Research Data Services at the library of the University of Texas at Arlington (UTA) for giving me the opportunity to work as a Graduate Research Assistant (GRA) in the dataCAVE. i DEDICATION I would like to dedicate my thesis especially to my mom and dad who have always been very supportive of me with my educational and personal endeavors. Furthermore, my sister and my brother played an indispensable role to provide emotional and other supports during my graduate school and research. ii LIST OF ILLUSTRATIONS Fig: 2.3: Rasa Architecture ……………………………………………………………….7 Fig 2.4: Chatbot Conversation without misspelling and with misspelling error.
    [Show full text]
  • Feature Combination for Measuring Sentence Similarity
    FEATURE COMBINATION FOR MEASURING SENTENCE SIMILARITY Ehsan Shareghi Nojehdeh A thesis in The Department of Computer Science and Software Engineering Presented in Partial Fulfillment of the Requirements For the Degree of Master of Computer Science Concordia University Montreal,´ Quebec,´ Canada April 2013 c Ehsan Shareghi Nojehdeh, 2013 Concordia University School of Graduate Studies This is to certify that the thesis prepared By: Ehsan Shareghi Nojehdeh Entitled: Feature Combination for Measuring Sentence Similarity and submitted in partial fulfillment of the requirements for the degree of Master of Computer Science complies with the regulations of this University and meets the accepted standards with respect to originality and quality. Signed by the final examining commitee: Chair Dr. Peter C. Rigby Examiner Dr. Leila Kosseim Examiner Dr. Adam Krzyzak Supervisor Dr. Sabine Bergler Approved Chair of Department or Graduate Program Director 20 Dr. Robin A. L. Drew, Dean Faculty of Engineering and Computer Science Abstract Feature Combination for Measuring Sentence Similarity Ehsan Shareghi Nojehdeh Sentence similarity is one of the core elements of Natural Language Processing (NLP) tasks such as Recognizing Textual Entailment, and Paraphrase Recognition. Over the years, different systems have been proposed to measure similarity between fragments of texts. In this research, we propose a new two phase supervised learning method which uses a combination of lexical features to train a model for predicting similarity between sentences. Each of these features, covers an aspect of the text on implicit or explicit level. The two phase method uses all combinations of the features in the feature space and trains separate models based on each combination.
    [Show full text]
  • The Research of Weighted Community Partition Based on Simhash
    Available online at www.sciencedirect.com Procedia Computer Science 17 ( 2013 ) 797 – 802 Information Technology and Quantitative Management (ITQM2013) The Research of Weighted Community Partition based on SimHash Li Yanga,b,c, **, Sha Yingc, Shan Jixic, Xu Kaic aInstitute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190 bGraduate School of Chinese Academy of Sciences, Beijing, 100049 cNational Engineering Laboratory for Information Security Technologies, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100093 Abstract The methods of community partition in nowadays mainly focus on using the topological links, and consider little of the content-based information between users. In the paper we analyze the content similarity between users by the SimHash method, and compute the content-based weight of edges so as to attain a more reasonable community partition. The dataset adopt the real data from twitter for testing, which verify the effectiveness of the proposed method. © 2013 The Authors. Published by Elsevier B.V. Selection and peer-review under responsibility of the organizers of the 2013 International Conference on Information Technology and Quantitative Management Keywords: Social Network, Community Partition, Content-based Weight, SimHash, Information Entropy; 1. Introduction The important characteristic of social network is the community structure, but the current methods mainly focus on the topological links, the research on the content is very less. The reason is that we can t effectively utilize content-based information from the abundant short text. So in the paper we hope to make full use of content-based information between users based on the topological links, so as to find the more reasonable communities.
    [Show full text]
  • Hybrid Algorithm for Approximate String Matching to Be Used for Information Retrieval Surbhi Arora, Ira Pandey
    International Journal of Scientific & Engineering Research Volume 9, Issue 11, November-2018 396 ISSN 2229-5518 Hybrid Algorithm for Approximate String Matching to be used for Information Retrieval Surbhi Arora, Ira Pandey Abstract— Conventional database searches require the user to hit a complete, correct query denying the possibility that any legitimate typographical variation or spelling error in the query will simply fail the search procedure. An approximate string matching engine, often colloquially referred to as fuzzy keyword search engine, could be a potential solution for all such search breakdowns. A fuzzy matching program can be summarized as the Google's 'Did you mean: ...' or Yahoo's 'Including results for ...'. These programs are entitled to be fuzzy since they don't employ strict checking and hence, confining the results to 0 or 1, i.e. no match or exact match. Rather, are designed to handle the concept of partial truth; be it DNA mapping, record screening, or simply web browsing. With the help of a 0.4 million English words dictionary acting as the underlying data source, thereby qualifying as Big Data, the study involves use of Apache Hadoop's MapReduce programming paradigm to perform approximate string matching. Aim is to design a system prototype to demonstrate the practicality of our solution. Index Terms— Approximate String Matching, Fuzzy, Information Retrieval, Jaro-Winkler, Levenshtein, MapReduce, N-Gram, Edit Distance —————————— —————————— 1 INTRODUCTION UZZY keyword search engine employs approximate Fig. 1. Fuzzy Search results for query string, “noting”. F search matching algorithms for Information Retrieval (IR). Approximate string matching involves spotting all text The fuzzy logic behind approximate string searching can matching the text pattern of given search query but with be described by considering a fuzzy set F over a referential limited number of errors [1].
    [Show full text]
  • Levenshtein Distance Based Information Retrieval Veena G, Jalaja G BNM Institute of Technology, Visvesvaraya Technological University
    International Journal of Scientific & Engineering Research, Volume 6, Issue 5, May-2015 112 ISSN 2229-5518 Levenshtein Distance based Information Retrieval Veena G, Jalaja G BNM Institute of Technology, Visvesvaraya Technological University Abstract— In today’s web based applications information retrieval is gaining popularity. There are many advances in information retrieval such as fuzzy search and proximity ranking. Fuzzy search retrieves relevant results containing words which are similar to query keywords. Even if there are few typographical errors in query keywords the system will retrieve relevant results. A query keyword can have many similar words, the words which are very similar to query keywords will be considered in fuzzy search. Ranking plays an important role in web search; user expects to see relevant documents in first few results. Proximity ranking is arranging search results based on the distance between query keywords. In current research information retrieval system is built to search contents of text files which have the feature of fuzzy search and proximity ranking. Indexing the contents of html or pdf files are performed for fast retrieval of search results. Combination of indexes like inverted index and trie index is used. Fuzzy search is implemented using Levenshtein’s Distance or edit distance concept. Proximity ranking is done using binning concept. Search engine system evaluation is done using average interpolated precision at eleven recall points i.e. at 0, 0.1, 0.2…..0.9, 1.0. Precision Recall graph is plotted to evaluate the system. Index Terms— stemming, stop words, fuzzy search, proximity ranking, dictionary, inverted index, trie index and binning.
    [Show full text]
  • Thesis Submitted in Partial Fulfilment for the Degree of Master of Computing (Advanced) at Research School of Computer Science the Australian National University
    How to tell Real From Fake? Understanding how to classify human-authored and machine-generated text Debashish Chakraborty A thesis submitted in partial fulfilment for the degree of Master of Computing (Advanced) at Research School of Computer Science The Australian National University May 2019 c Debashish Chakraborty 2019 Except where otherwise indicated, this thesis is my own original work. Debashish Chakraborty 30 May 2019 Acknowledgments First, I would like to express my gratitude to Dr Patrik Haslum for his support and guidance throughout the thesis, and for the useful feedback from my late drafts. Thank you to my parents and Sandra for their continuous support and patience. A special thanks to Sandra for reading my very "drafty" drafts. v Abstract Natural Language Generation (NLG) using Generative Adversarial Networks (GANs) has been an active field of research as it alleviates restrictions in conventional Lan- guage Modelling based text generators e.g. Long-Short Term Memory (LSTM) net- works. The adequacy of a GAN-based text generator depends on its capacity to classify human-written (real) and machine-generated (synthetic) text. However, tra- ditional evaluation metrics used by these generators cannot effectively capture clas- sification features in NLG tasks, such as creative writing. We prove this by using an LSTM network to almost perfectly classify sentences generated by a LeakGAN, a state-of-the-art GAN for long text generation. This thesis attempts a rare approach to understand real and synthetic sentences using meaningful and interpretable features of long sentences (with at least 20 words). We analyse novelty and diversity features of real and synthetic sentences, generate by a LeakGAN, using three meaningful text dissimilarity functions: Jaccard Distance (JD), Normalised Levenshtein Distance (NLD) and Word Mover’s Distance (WMD).
    [Show full text]
  • Effective Search Space Reduction for Spell Correction Using Character Neural Embeddings
    Effective search space reduction for spell correction using character neural embeddings Harshit Pande Smart Equipment Solutions Group Samsung Semiconductor India R&D Bengaluru, India [email protected] Abstract of distance computations blows up the time com- plexity, thus hindering real-time spell correction. We present a novel, unsupervised, and dis- For Damerau-Levenshtein distance or similar edit tance measure agnostic method for search distance-based measures, some approaches have space reduction in spell correction using been tried to reduce the time complexity of spell neural character embeddings. The embed- correction. Norvig (2007) does not check against dings are learned by skip-gram word2vec all dictionary words, instead generates all possi- training on sequences generated from dic- ble words till a certain edit distance threshold from tionary words in a phonetic information- the misspelled word. Then each of such generated retentive manner. We report a very high words is checked in the dictionary for existence, performance in terms of both success rates and if it is found in the dictionary, it becomes a and reduction of search space on the Birk- potentially correct spelling. There are two short- beck spelling error corpus. To the best of comings of this approach. First, such search space our knowledge, this is the first application reduction works only for edit distance-based mea- of word2vec to spell correction. sures. Second, this approach too leads to high time complexity when the edit distance threshold is 1 Introduction greater than 2 and the possible characters are large. Spell correction is now a pervasive feature, with Large character set is real for Unicode characters presence in a wide range of applications such used in may Asian languages.
    [Show full text]