DOCSLIB.ORG

Morpho-Syntactically Annotated Amharic Treebank

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Morpho-Syntactically Annotated Amharic Treebank Morpho-syntactically Annotated Amharic Treebank Seyoum, Binyam Ephrem Miyao, Yusuke Mekonnen, Baye Yimam Addis Ababa University National Institute of Informatics Addis Ababa University [email protected] [email protected] [email protected] morphological features and syntactic structure Abstract (dependency structures). Parsing, on the other hand, is a process of In this paper, we describe an ongoing project recognizing input sentences and identifying units of developing a treebank for Amharic. The main objective of developing the treebank is to like subject, verb, and object. It is used to use it as an input for the development of a determine the interaction among these parser. Morphologically-rich Languages like grammatical functions. Deep parsing, sometimes Arabic, Amharic and other Semitic languages referred to as deep processing, is a process present challenges to the state-of-art in whereby rich linguistic resource is used to give a parsing. In such language morphemes play detailed (or deep) syntactic as well as semantic important functions in both morphology and analysis of input sentences. Such systems are syntax. In addition to the existence of high basic components and language specific lexical variations due the morphology, resources for any Natural Language Processing Amharic has a number of clitics which are not applications which require deeper understanding indicated with any special marker in the orthography. Considering the status of of Natural Languages. Amharic resources and challenges to the The structure of the paper is as follows: after existing approach to parsing, we suggest to we give a brief discussion on the motivation for develop a treebank where clitics are separated the development of the treebank in Section 2, the manually from content words and annotated challenges of Amharic to treebank development semi-automatically for part-of-speech, will be discussed in Section 3. Section 4 deals morphological features and syntactic with the existing resources and their limitation. relations. Section 5 is devoted to the proposed solution and finally the paper will concluded in Section 6. 1. Introduction 2. Motivation A treebank in general can be viewed as a linguistically annotated corpus that includes The main objective of the project is to develop a grammatical analysis beyond part-of-speech level parser for Amharic sentences. For parser (Nivre, 2008). The level of annotation could development there are two major approaches. include word, phrase and sentence levels (Frank These are grammar-driven (or rule-based) and and Erhard, 2012). Such language specific data-driven (or statistical-based) (Nivre, 2008). annotated corpus is an input for the development In grammar-driven parsing, a formal grammar is of various natural language processing tools that used to define possible parsing results for each use data-driven approaches. Though the design of string in the language. We define the grammar a treebank should be motivated by its intended rules and the list of possible lexical items to use, most treebank annotation schemes are which the rules can apply. It follows linguistic organized into a number of layers (Nivre, 2008). motivation to precisely describe a grammar of a For our purpose, we propose three layers of language. Even though, the rules are hand-written information. These are the part-of-speech, (or hand-crafted), they are capable of delivering a 48 highly accurate in-depth analysis of complex be categorized as less resourced language. As a natural language phenomena. result, to develop a parser for Amharic we Motivated by the hypothesis that humans suggested first to develop a treebank manually. In recognize patterns and phrases that have occurred this paper, we are going to address some of the in past experience (Bod et al., 2012), the task in challenges and solution in developing the data-driven approaches is to learn syntactic treebank for Amharic. structures from an existing treebank or a large syntactically annotated corpus. In data-driven 3. Challenges approach, the development of a parsing system presupposes availability of a treebank for a given In developing a treebank for languages which are language (Nivre et al., 2007). In data-driven, the morphologically rich and less resourced, there are knowledge of the grammar rules will be learned a number of challenging issues which should be from a manually-parsed training data. given due attention. In the following sections we Both methods have their own shortcomings provide a summary of the major challenges we and benefits. Grammar-driven methods are have noticed so far. known to be linguistically precise, but have a 3. 1 Morphologically-rich languages problem of robustness and ambiguity. On the other hand, data-driven methods are good for Amharic being one of the morphologically rich developing wide-coverage parsers rapidly. languages presents a challenge to the area of NLP However, the accuracy of the parser depends on in general and to parsing in particular (Dehadari, the magnitude of the training data and the et al., 2011). It is considered as a existence of accurate language specific resources. morphologically-rich languages in the sense that As data-driven methods depend on the size of grammatical relations like subject, object, etc. or corpus, it is also subject to problems of word arrangements and syntactic information are robustness, the ability of a system to give a indicated morphologically or at word level certain analysis for a new or unseen input (Habash, 2010).When parsing models which sentence (Nivre, 2006). have the highest performance for languages like On the other hand, in choosing which English are adopted and implemented for approaches to follow for the development of morphologically-rich languages like Amharic, parsing system, we need to consider the status of they perform poorly. This is due to the the language. In general, languages can be highly complexity of the morphological structure resourced or less resourced. Such distinction is (Tsarfaty, et al., 2010). based on the availability of tools and electronic An orthographic word in such a language data prepared in a language. Regarding Amharic which is delimited by white space, may be a there are initiatives to develop a large corpus. It combination of one or more function words and is also stated that language processing research inflectional morphemes. For instance, an for Amharic has shown some progress in recent Amharic orthographic word like ከየቤቱና years in both corpus and basic Natural Language /kəjjəbetunna/ “from each house and”, includes Processing (NLP) tools development (Gamback, the preposition ከ- /kə-/ “from”, reduced form of 2012).The existing corpus so far focuses on web the distributive marker እየ- /ɨjjə/ “each”, ቤት /bet/ or news corpus. These sources are good for “house”, the definite marker -ኡ /-u / “the” and the producing a large corpus quickly. However, for conjunction -ና /-nna/ “and”. The clitics like Amharic, there are no text preparation tools like preposition, conjunction, auxiliaries, etc. have Spell Checker, Grammar Checker, etc. As a syntactic roles that indicate grammatical relations result, a part of such corpus is produced with with the content words (Tsarfaty, 2013). In order some level of errors. The efficiency of NLP tools to show the syntactic relation between clitics and trained on such corpus may also be questioned. content words, we need to consider how to Therefore, in both tools and corpus, Amharic can 49 represent and tag such elements. In addition, in handle this feature. In some cases, morphologically-rich languages a syntactic gemination can be used to convey position may not give clue about the syntactic grammatical information. Such case is relation. Rather it provides morphological very common in the construction of features that determine the function of a phrase in relativized verbs. a sentence. Thus, we should combine both B. Compound words - As can be observed structural and morphological features to predict in other languages, compound words are syntactic dependencies. written in three ways; with a space State-of-the-art in parsing technology has between the combined words (ቡና ቤት been using data-driven systems. However, due to high morphological variations that exist in /bunna bet/ “bar”፣ አየር መንገድ /ʔajər morphologically rich languages, it is impractical məngəd/ “airline”), separated by a hyphen to observe all such variations in a given annotated (ስነ-ስርዓት /sɨnə-sɨrʔat/ “procedures”፣ ስነ- data. As a result, data-driven systems do not ጥበብ /sɨnə-t‘ɨbəb/ “art”) or written as a guarantee recognition of all morphological single word (ቤተክርስቲያን /bətəkɨrsɨtijan/ variations (Tsarfaty, 2013). Verbs in Amharic, “church”፣ መስሪያቤቶች /məsrijabetoʧʧ/ for instance, can have more than thousands word “offices”). forms (Gasser, 2010). Capturing all these word C. Syntactic words – words which are forms in a corpus is unthinkable. Apart from the separated by white-space (semicolon in morphological variations, the written forms may old documents) may be coupled with cause further variations due to clitics that are functional words like a preposition, attached to content words. Since there are a conjunction or auxiliaries. Thus, an number of clitics in Amharic, the degree of variations increases. Thus, in order to decrease orthographic word may be a phrase (ለሰው the degree of variations arising from attachment /ləsəw/ “to human”), a clause (የሚገኙትና of functional words, one needs to segment these /jəmmigəɲɲutɨnna/“and those that are forms. Segmenting clitics is not a simple task as found/available), or even a sentence it can be part of a content word or can be a word (አልመጣችም /ʔalmət‘t‘aʧʧɨm/ “She did not which is reduced due to phonological processes. come.”) 3.2 Writing system All such features need to be addressed in processing Amharic texts. Some of the above The Amharic script is called Ge’ez or Ethiopic problems call for standardization efforts to be where a consonant and a vowel are represented made whereas others are due to the decision to by one symbol.
Load more

Recommended publications
  • Spell Checker
    International Journal of Scientific and Research Publications, Volume 5, Issue 4, April 2015 1 ISSN 2250-3153 SPELL CHECKER Vibhakti V. Bhaire, Ashiki A. Jadhav, Pradnya A. Pashte, Mr. Magdum P.G ComputerEngineering, Rajendra Mane College of Engineering and Technology Abstract- Spell Checker project adds spell checking and For handling morphology language-dependent algorithm is an correction functionality to the windows based application by additional step. The spell-checker will need to consider different using autosuggestion technique. It helps the user to reduce typing forms of the same word, such as verbal forms, contractions, work, by identifying any spelling errors and making it easier to possessives, and plurals, even for a lightly inflected language like repeat searches .The main goal of the spell checker is to provide English. For many other languages, such as those featuring unified treatment of various spell correction. Firstly the spell agglutination and more complex declension and conjugation, this checking and correcting problem will be formally describe in part of the process is more complicated. order to provide a better understanding of these task. Spell checker and corrector is either stand-alone application capable of A spell checker carries out the following processes: processing a string of words or a text or as an embedded tool which is part of a larger application such as a word processor. • It scans the text and selects the words contained in it. Various search and replace algorithms are adopted to fit into the • domain of spell checker. Spell checking identifies the words that It then compares each word with a known list of are valid in the language as well as misspelled words in the correctly spelled words (i.e.
    [Show full text]
  • Finite State Recognizer and String Similarity Based Spelling
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by BRAC University Institutional Repository FINITE STATE RECOGNIZER AND STRING SIMILARITY BASED SPELLING CHECKER FOR BANGLA Submitted to A Thesis The Department of Computer Science and Engineering of BRAC University by Munshi Asadullah In Partial Fulfillment of the Requirements for the Degree of Bachelor of Science in Computer Science and Engineering Fall 2007 BRAC University, Dhaka, Bangladesh 1 DECLARATION I hereby declare that this thesis is based on the results found by me. Materials of work found by other researcher are mentioned by reference. This thesis, neither in whole nor in part, has been previously submitted for any degree. Signature of Supervisor Signature of Author 2 ACKNOWLEDGEMENTS Special thanks to my supervisor Mumit Khan without whom this work would have been very difficult. Thanks to Zahurul Islam for providing all the support that was required for this work. Also special thanks to the members of CRBLP at BRAC University, who has managed to take out some time from their busy schedule to support, help and give feedback on the implementation of this work. 3 Abstract A crucial figure of merit for a spelling checker is not just whether it can detect misspelled words, but also in how it ranks the suggestions for the word. Spelling checker algorithms using edit distance methods tend to produce a large number of possibilities for misspelled words. We propose an alternative approach to checking the spelling of Bangla text that uses a finite state automaton (FSA) to probabilistically create the suggestion list for a misspelled word.
    [Show full text]
  • Spell Checking in Computer-Assisted Language Learning: a Study of Misspellings by Nonnative Writers of German
    SPELL CHECKING IN COMPUTER-ASSISTED LANGUAGE LEARNING: A STUDY OF MISSPELLINGS BY NONNATIVE WRITERS OF GERMAN Anne Rimrott Diplom, Justus Liebig Universitat, 2002 THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF ARTS In the Department of Linguistics O Anne Rimrott 2005 SIMON FRASER UNIVERSITY Spring 2005 All rights reserved. This work may not be reproduced in whole or in part, by photocopy or other means, without permission of the author. APPROVAL Name: Anne Rimrott Degree: Master of Arts Title of Thesis: Spell Checking in Computer-Assisted Language Learning: A Study of Misspellings by Nonnative Writers of German Examining Committee: Chair: Dr. Alexei Kochetov Assistant Professor, Department of Linguistics Dr. Trude Heift Senior Supervisor Associate Professor, Department of Linguistics Dr. Chung-hye Han Supervisor Assistant Professor, Department of Linguistics Dr. Maria Teresa Taboada Supervisor Assistant Professor, Department of Linguistics Dr. Mathias Schulze External Examiner Assistant Professor, Department of Germanic and Slavic Studies University of Waterloo Date DefendedIApproved: SIMON FRASER UNIVERSITY PARTIAL COPYRIGHT LICENCE The author, whose copyright is declared on the title page of this work, has granted to Simon Fraser University the right to lend this thesis, project or extended essay to users of the Simon Fraser University Library, and to make partial or single copies only for such users or in response to a request from the library of any other university, or other educational institution, on its own behalf or for one of its users. The author has further granted permission to Simon Fraser University to keep or make a digital copy for use in its circulating collection.
    [Show full text]
  • Exploiting Wikipedia Semantics for Computing Word Associations
    Exploiting Wikipedia Semantics for Computing Word Associations by Shahida Jabeen A thesis submitted to the Victoria University of Wellington in fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science. Victoria University of Wellington 2014 Abstract Semantic association computation is the process of automatically quan- tifying the strength of a semantic connection between two textual units based on various lexical and semantic relations such as hyponymy (car and vehicle) and functional associations (bank and manager). Humans have can infer implicit relationships between two textual units based on their knowledge about the world and their ability to reason about that knowledge. Automatically imitating this behavior is limited by restricted knowledge and poor ability to infer hidden relations. Various factors affect the performance of automated approaches to com- puting semantic association strength. One critical factor is the selection of a suitable knowledge source for extracting knowledge about the im- plicit semantic relations. In the past few years, semantic association com- putation approaches have started to exploit web-originated resources as substitutes for conventional lexical semantic resources such as thesauri, machine readable dictionaries and lexical databases. These conventional knowledge sources suffer from limitations such as coverage issues, high construction and maintenance costs and limited availability. To overcome these issues one solution is to use the wisdom of crowds in the form of collaboratively constructed knowledge sources. An excellent example of such knowledge sources is Wikipedia which stores detailed information not only about the concepts themselves but also about various aspects of the relations among concepts. The overall goal of this thesis is to demonstrate that using Wikipedia for computing word association strength yields better estimates of hu- mans’ associations than the approaches based on other structured and un- structured knowledge sources.
    [Show full text]
  • Words in a Text
    Cross-lingual geo-parsing for non-structured data Judith Gelernter Wei Zhang Language Technologies Institute Language Technologies Institute School of Computer Science School of Computer Science Carnegie Mellon University Carnegie Mellon University [email protected] [email protected] ABSTRACT to cross-language geo-information retrieval. We will examine A geo-parser automatically identifies location words in a text. We Strötgen’s view that normalized location information is language- have generated a geo-parser specifically to find locations in independent [16]. Difficulties in cross-language experiments are unstructured Spanish text. Our novel geo-parser architecture exacerbated when the languages use different alphabets, and when combines the results of four parsers: a lexico-semantic Named the focus is on proper names, as in this case, the names of Location Parser, a rules-based building parser, a rules-based street locations. parser, and a trained Named Entity Parser. Each parser has Geo-parsing structured versus unstructured text requires different different strengths: the Named Location Parser is strong in recall, language processing tools. Twitter messages are challenging to and the Named Entity Parser is strong in precision, and building geo-parse because of their non-grammaticality. To handle non- and street parser finds buildings and streets that the others are not grammatical forms, we use a Twitter tokenizer rather than a word designed to do. To test our Spanish geo-parser performance, we tokenizer. We use an English part of speech tagger created for compared the output of Spanish text through our Spanish geo- tweets, which was not available to us in Spanish.
    [Show full text]
  • Automatic Spelling Correction Based on N-Gram Model
    International Journal of Computer Applications (0975 – 8887) Volume 182 – No. 11, August 2018 Automatic Spelling Correction based on n-Gram Model S. M. El Atawy A. Abd ElGhany Dept. of Computer Science Dept. of Computer Faculty of Specific Education Damietta University Damietta University, Egypt Egypt ABSTRACT correction. In this paper, we are designed, implemented and A spell checker is a basic requirement for any language to be evaluated an end-to-end system that performs spellchecking digitized. It is a software that detects and corrects errors in a and auto correction. particular language. This paper proposes a model to spell error This paper is organized as follows: Section 2 illustrates types detection and auto-correction that is based on n-gram of spelling error and some of related works. Section 3 technique and it is applied in error detection and correction in explains the system description. Section 4 presents the results English as a global language. The proposed model provides and evaluation. Finally, Section 5 includes conclusion and correction suggestions by selecting the most suitable future work. suggestions from a list of corrective suggestions based on lexical resources and n-gram statistics. It depends on a 2. RELATED WORKS lexicon of Microsoft words. The evaluation of the proposed Spell checking techniques have been substantially, such as model uses English standard datasets of misspelled words. error detection & correction. Two commonly approaches for Error detection, automatic error correction, and replacement error detection are dictionary lookup and n-gram analysis. are the main features of the proposed model. The results of the Most spell checkers methods described in the literature, use experiment reached approximately 93% of accuracy and acted dictionaries as a list of correct spellings that help algorithms similarly to Microsoft Word as well as outperformed both of to find target words [16].
    [Show full text]
  • Development of a Persian Syntactic Dependency Treebank
    Development of a Persian Syntactic Dependency Treebank Mohammad Sadegh Rasooli Manouchehr Kouhestani Amirsaeid Moloodi Department of Computer Science Department of Linguistics Department of Linguistics Columbia University Tarbiat Modares University University of Tehran New York, NY Tehran, Iran Tehran, Iran [email protected] [email protected] [email protected] Abstract tions in tasks such as machine translation. Depen- dency treebanks are collections of sentences with This paper describes the annotation process their corresponding dependency trees. In the last and linguistic properties of the Persian syn- decade, many dependency treebanks have been de- tactic dependency treebank. The treebank veloped for a large number of languages. There are consists of approximately 30,000 sentences at least 29 languages for which at least one depen- annotated with syntactic roles in addition to morpho-syntactic features. One of the unique dency treebank is available (Zeman et al., 2012). features of this treebank is that there are al- Dependency trees are much more similar to the hu- most 4800 distinct verb lemmas in its sen- man understanding of language and can easily rep- tences making it a valuable resource for ed- resent the free word-order nature of syntactic roles ucational goals. The treebank is constructed in sentences (Kubler¨ et al., 2009). with a bootstrapping approach by means of available tagging and parsing tools and man- Persian is a language with about 110 million ually correcting the annotations. The data is speakers all over the world (Windfuhr, 2009), yet in splitted into standard train, development and terms of the availability of teaching materials and test set in the CoNLL dependency format and annotated data for text processing, it is undoubt- is freely available to researchers.
    [Show full text]
  • Study on Spell-Checking System Using Levenshtein Distance Algorithm
    International Journal of Recent Development in Engineering and Technology Website: www.ijrdet.com (ISSN 2347-6435(Online) Volume 8, Issue 9, September 2019) Study on Spell-Checking System using Levenshtein Distance Algorithm Thi Thi Soe1, Zarni Sann2 1Faculty of Computer Science 2Faculty of Computer Systems and Technologies 1,2FUniversity of Computer Studies (Mandalay), Myanmar Abstract— Natural Language Processing (NLP) is one of If the word is not found, it is considered to be an error, the most important research area carried out in the world of and an attempt may be made to suggest that word. When a Artificial Intelligence (AI). NLP supports the tasks of a word which is not within the dictionary is encountered, portion of AI such as spell checking, machine translation, most spell checkers provide an option to add that word to a automatic text summarization, and information extraction, so list of known exceptions that should not be flagged. An on. Spell checking application presents valid suggestions to the user based on each mistake they encounter in the user’s adaptive spelling checker tool based on Ternary Search document. The user then either makes a selection from a list Tree data structure is described in [1]. A comprehensive of suggestions or accepts the current word as valid. Spell- spelling checker application presented a significant checking program is often integrated with word processing challenge in producing suggestions for a misspelled word that checks for the correct spelling of words in a document. when employing the traditional methods in [2]. It learned Each word is compared against a dictionary of correctly spelt the complex orthographic rules of Bangla.
    [Show full text]
  • Spell Checker in CET Designer
    Linköping University | Department of Computer Science Bachelor thesis, 16 ECTS | Datateknik 2016 | LIU-IDA/LITH-EX-G--16/069--SE Spell checker in CET Designer Rasmus Hedin Supervisor : Amir Aminifar Examiner : Zebo Peng Linköpings universitet SE–581 83 Linköping +46 13 28 10 00 , www.liu.se Upphovsrätt Detta dokument hålls tillgängligt på Internet – eller dess framtida ersättare – under 25 år från publiceringsdatum under förutsättning att inga extraordinära omständigheter uppstår. Tillgång till dokumentet innebär tillstånd för var och en att läsa, ladda ner, skriva ut enstaka kopior för enskilt bruk och att använda det oförändrat för ickekommersiell forskning och för undervisning. Överföring av upphovsrätten vid en senare tidpunkt kan inte upphäva detta tillstånd. All annan användning av dokumentet kräver upphovsmannens medgivande. För att garantera äktheten, säkerheten och tillgängligheten finns lösningar av teknisk och admin- istrativ art. Upphovsmannens ideella rätt innefattar rätt att bli nämnd som upphovsman i den omfattning som god sed kräver vid användning av dokumentet på ovan beskrivna sätt samt skydd mot att dokumentet ändras eller presenteras i sådan form eller i sådant sam- manhang som är kränkande för upphovsmannenslitterära eller konstnärliga anseende eller egenart. För ytterligare information om Linköping University Electronic Press se förlagets hemsida http://www.ep.liu.se/. Copyright The publishers will keep this document online on the Internet – or its possible replacement – for a period of 25 years starting from the date of publication barring exceptional circum- stances. The online availability of the document implies permanent permission for anyone to read, to download, or to print out single copies for his/hers own use and to use it unchanged for non-commercial research and educational purpose.
    [Show full text]
  • Discourse-Aware Statistical Machine Translation As a Context-Sensitive Spell Checker
    Discourse-aware Statistical Machine Translation as a Context-Sensitive Spell Checker Behzad Mirzababaei, Heshaam Faili and Nava Ehsan School of Electrical and Computer Engineering College of Engineering University of Tehran, Tehran, Iran {b.mirzababaei,hfaili,n.ehsan}@ut.ac.ir Phrase-based SMT is weak in handling long- Abstract distance dependencies between the sentence words. In order to capture this kind of Real-word errors or context sensitive spelling dependencies, which affects detecting the correct errors, are misspelled words that have been candidate word, mentioned SMT is augmented wrongly converted into another word of with a discourse-aware reranking method for vocabulary. One way to detect and correct reranking the N-best results of SMT. real-word errors is using Statistical Machine Our work can be regarded as an extension of Translation (SMT), which translates a text containing some real-word errors into a the method introduced by Ehsan and Faili correct text of the same language. In this (2013), in which they use SMT to detect and paper, we improve the results of mentioned correct the spelling errors of a document. But SMT system by employing some discourse- here, we use the N-best results of SMT as a aware features into a log-linear reranking candidate list for each erroneous word and rerank method. Our experiments on a real-world test the list by using a discourse-aware reranking data in Persian show an improvement of system which is just a log-linear ranker. about 9.5% and 8.5% in the recall of Shortly, the contributions of this paper can be detection and correction respectively.
    [Show full text]
  • Using the Hunspell Spell Checker
    integrated translation environment Using the Hunspell Spell Checker © 2004-2014 Kilgray Translation Technologies. All rights reserved. Using the Hunspell Spell Checker in memoQ Contents Contents ...................................................................................................................................... 2 Setting up the Hunspell spell checker for your language ................................................................ 3 Using the Hunspell spell checker during translation ....................................................................... 6 This guide covers memoQ 2014 and higher settings for Hunspell. It contains text items from the English user interface of the program. These items are under constant verification and are subject to change without prior notification. memoQ integrated translation environment Page 2 of 6 Using the Hunspell Spell Checker in memoQ Setting up the Hunspell spell checker for your language memoQ installs the Hunspell spell checker, an open-source application used in OpenOffice.org and Mozilla Firefox/Thunderbird. The most important benefits of using Hunspell are the following: . You no longer need to use Microsoft Word’s spell checker, which is not always available.1 . Spell checking becomes significantly faster in memoQ. You can check spelling in practically any language. You can spell check as you type. memoQ uses Hunspell for on-the-fly spelling when you have configured a Hunspell dictionary for your target language in Options > Spell settings. memoQ does not include any particular spelling dictionaries for Hunspell. Before you can use Hun- spell for a language of your choice, you need to set up the appropriate dictionaries. To set up dic- tionaries for a particular language, follow the steps below: 1. Start memoQ. 2. From the Tools menu or in the Quick Access Toolbar in memoQ 2014 R2, choose Options. In the category list to the left, click Spelling and grammar.
    [Show full text]
  • Analyzing the Impact of Spelling Errors on POS-Tagging and Chunking in Learner English
    Analyzing the Impact of Spelling Errors on POS-Tagging and Chunking in Learner English Tomoya Mizumoto1 and Ryo Nagata2 1 1 RIKEN Center for Advanced Intelligence Project 2 Konan University [email protected], nagata-ijcnlp @hyogo-u.ac.jp Abstract The heavy dependence on POS-tagging and chunking suggests that failures could degrade the Part-of-speech (POS) tagging and chunk- performance of NLP systems and linguistic analy- ing have been used in tasks targeting ses (Han et al., 2006; Sukkarieh and Blackmore, learner English; however, to the best 2009). For example, failure to recognize noun our knowledge, few studies have eval- phrases in a sentence could lead to failure in cor- uated their performance and no stud- recting related errors in article use and noun num- ies have revealed the causes of POS- ber. More importantly, such failures make it more tagging/chunking errors in detail. There- difficult to simply count the number of POSs and fore, we investigate performance and an- chunks, thereby causing inaccurate estimates of alyze the causes of failure. We focus their distributions. Note that such estimates are of- on spelling errors that occur frequently ten employed in linguistic analysis, including the in learner English. We demonstrate that above-mentioned studies. spelling errors reduced POS-tagging per- Despite its importance in related tasks, we also formance by 0.23% owing to spelling er- note that few studies have focused on performance rors, and that a spell checker is not neces- evaluations of POS-tagging and chunking. Only a sary for POS-tagging/chunking of learner few studies, including Nagata et al.(2011), Berzak English.
    [Show full text]