A Large-Scale Classification of English Verbs
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An Artificial Neural Network Approach for Sentence Boundary Disambiguation in Urdu Language Text
The International Arab Journal of Information Technology, Vol. 12, No. 4, July 2015 395 An Artificial Neural Network Approach for Sentence Boundary Disambiguation in Urdu Language Text Shazia Raj, Zobia Rehman, Sonia Rauf, Rehana Siddique, and Waqas Anwar Department of Computer Science, COMSATS Institute of Information Technology, Pakistan Abstract: Sentence boundary identification is an important step for text processing tasks, e.g., machine translation, POS tagging, text summarization etc., in this paper, we present an approach comprising of Feed Forward Neural Network (FFNN) along with part of speech information of the words in a corpus. Proposed adaptive system has been tested after training it with varying sizes of data and threshold values. The best results, our system produced are 93.05% precision, 99.53% recall and 96.18% f-measure. Keywords: Sentence boundary identification, feed forwardneural network, back propagation learning algorithm. Received April 22, 2013; accepted September 19, 2013; published online August 17, 2014 1. Introduction In such conditions it would be difficult for a machine to differentiate sentence terminators from Sentence boundary disambiguation is a problem in natural language processing that decides about the ambiguous punctuations. beginning and end of a sentence. Almost all language Urdu language processing is in infancy stage and processing applications need their input text split into application development for it is way slower for sentences for certain reasons. Sentence boundary number of reasons. One of these reasons is lack of detection is a difficult task as very often ambiguous Urdu text corpus, either tagged or untagged. However, punctuation marks are used in the text. -
A Comparison of Knowledge Extraction Tools for the Semantic Web
A Comparison of Knowledge Extraction Tools for the Semantic Web Aldo Gangemi1;2 1 LIPN, Universit´eParis13-CNRS-SorbonneCit´e,France 2 STLab, ISTC-CNR, Rome, Italy. Abstract. In the last years, basic NLP tasks: NER, WSD, relation ex- traction, etc. have been configured for Semantic Web tasks including on- tology learning, linked data population, entity resolution, NL querying to linked data, etc. Some assessment of the state of art of existing Knowl- edge Extraction (KE) tools when applied to the Semantic Web is then desirable. In this paper we describe a landscape analysis of several tools, either conceived specifically for KE on the Semantic Web, or adaptable to it, or even acting as aggregators of extracted data from other tools. Our aim is to assess the currently available capabilities against a rich palette of ontology design constructs, focusing specifically on the actual semantic reusability of KE output. 1 Introduction We present a landscape analysis of the current tools for Knowledge Extraction from text (KE), when applied on the Semantic Web (SW). Knowledge Extraction from text has become a key semantic technology, and has become key to the Semantic Web as well (see. e.g. [31]). Indeed, interest in ontology learning is not new (see e.g. [23], which dates back to 2001, and [10]), and an advanced tool like Text2Onto [11] was set up already in 2005. However, interest in KE was initially limited in the SW community, which preferred to concentrate on manual design of ontologies as a seal of quality. Things started changing after the linked data bootstrapping provided by DB- pedia [22], and the consequent need for substantial population of knowledge bases, schema induction from data, natural language access to structured data, and in general all applications that make joint exploitation of structured and unstructured content. -
ACL 2019 Social Media Mining for Health Applications (#SMM4H)
ACL 2019 Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task Proceedings of the Fourth Workshop August 2, 2019 Florence, Italy c 2019 The Association for Computational Linguistics Order copies of this and other ACL proceedings from: Association for Computational Linguistics (ACL) 209 N. Eighth Street Stroudsburg, PA 18360 USA Tel: +1-570-476-8006 Fax: +1-570-476-0860 [email protected] ISBN 978-1-950737-46-8 ii Preface Welcome to the 4th Social Media Mining for Health Applications Workshop and Shared Task - #SMM4H 2019. The total number of users of social media continues to grow worldwide, resulting in the generation of vast amounts of data. Popular social networking sites such as Facebook, Twitter and Instagram dominate this sphere. According to estimates, 500 million tweets and 4.3 billion Facebook messages are posted every day 1. The latest Pew Research Report 2, nearly half of adults worldwide and two- thirds of all American adults (65%) use social networking. The report states that of the total users, 26% have discussed health information, and, of those, 30% changed behavior based on this information and 42% discussed current medical conditions. Advances in automated data processing, machine learning and NLP present the possibility of utilizing this massive data source for biomedical and public health applications, if researchers address the methodological challenges unique to this media. In its fourth iteration, the #SMM4H workshop takes place in Florence, Italy, on August 2, 2019, and is co-located with the -
Full-Text Processing: Improving a Practical NLP System Based on Surface Information Within the Context
Full-text processing: improving a practical NLP system based on surface information within the context Tetsuya Nasukawa. IBM Research Tokyo Resem~hLaborat0ry t623-14, Shimotsurum~, Yimmt;0¢sl{i; I<almgawa<kbn 2421,, J aimn • nasukawa@t,rl:, vnet;::ibm icbm Abstract text. Without constructing a i):recige filodel of the eohtext through, deep sema~nfiCamtlys~is, our frmne= Rich information fl)r resolving ambigui- "work-refers .to a set(ff:parsed trees. (.r~sltlt~ 9 f syn- ties m sentence ~malysis~ including vari- : tacti(" miaiysis)ofeach sexitencd in t.li~;~i'ext as (:on- ous context-dependent 1)rol)lems. can be ob- text ilfformation, Thus. our context model consists tained by analyzing a simple set of parsed of parse(f trees that are obtained 1)y using mi exlst- ~rces of each senten('e in a text withom il!g g¢lwral syntactic parser. Excel)t for information constructing a predse model of the contex~ ()It the sequence of senl;,en('es, olIr framework does nol tl(rough deep senmntic.anMysis. Th.us. pro- consider any discourse stru(:~:ure mwh as the discourse cessmg• ,!,' a gloup" of sentem' .., '~,'(s togethel. i ': makes.' • " segmenm, focus space stack, or dominant hierarclty it.,p.(,)ss{t?!e .t.9 !~npl:ovel-t]le ~ccui'a~'Y (?f a :: :.it~.(.fi.ii~idin:.(cfi.0szufid, Sht/!er, dgs6)i.Tli6refbi.e, om< ,,~ ~-. ~t.ehi- - ....t sii~ 1 "~g;. ;, ~-ni~chin¢''ti-mslat{b~t'@~--..: .;- . .? • . : ...... - ~,',". ..........;-.preaches ":........... 'to-context pr0cessmg,,' • and.m" "tier-ran : ' le d at. •tern ; ::'Li:In i. thin.j.;'P'..p) a (r ~;.!iwe : .%d,es(tib~, ..i-!: .... -
Natural Language Processing
Chowdhury, G. (2003) Natural language processing. Annual Review of Information Science and Technology, 37. pp. 51-89. ISSN 0066-4200 http://eprints.cdlr.strath.ac.uk/2611/ This is an author-produced version of a paper published in The Annual Review of Information Science and Technology ISSN 0066-4200 . This version has been peer-reviewed, but does not include the final publisher proof corrections, published layout, or pagination. Strathprints is designed to allow users to access the research output of the University of Strathclyde. Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Users may download and/or print one copy of any article(s) in Strathprints to facilitate their private study or for non-commercial research. You may not engage in further distribution of the material or use it for any profitmaking activities or any commercial gain. You may freely distribute the url (http://eprints.cdlr.strath.ac.uk) of the Strathprints website. Any correspondence concerning this service should be sent to The Strathprints Administrator: [email protected] Natural Language Processing Gobinda G. Chowdhury Dept. of Computer and Information Sciences University of Strathclyde, Glasgow G1 1XH, UK e-mail: [email protected] Introduction Natural Language Processing (NLP) is an area of research and application that explores how computers can be used to understand and manipulate natural language text or speech to do useful things. NLP researchers aim to gather knowledge on how human beings understand and use language so that appropriate tools and techniques can be developed to make computer systems understand and manipulate natural languages to perform the desired tasks. -
A Study on English Collocations and Delexical Verbs in English Curriculum
Pramana Research Journal ISSN NO: 2249-2976 CORPUS-BASED LINGUISTIC AND INSTRUCTIVE ANALYSIS: A STUDY ON ENGLISH COLLOCATIONS AND DELEXICAL VERBS IN ENGLISH CURRICULUM Dr. G. Shravan Kumar, Dr. Gomatam Mohana Charyulu Professor of English & Head, Associate Professor of English Controller of Examinations, VFSTR Deemed to be University PJTS Agricultural University, Vadlamudi, A.P. India Rajendranagar, Hyderabad. TS Abstract: The influence of native language on the learners of the English language is not a new issue. Moreover, expressions of colloquial native expressions into English language are also common in English speaking people in India. Introducing English Collocations into the curriculum through practice exercises at the end of the lessons at primary and secondary level is a general practice of curriculums designers to improve the skills of language learners. There is a vast scope of research on the English collocations in lessons introduced to learners at the initial stages in Telugu speakers of native India. In fact, it is a neglected area of investigation that knowledge of collocation can also improve the language competency. This paper investigates the English collocations and delexical verbs used by English learners. In order to make a perfect investigative study on the topic, this attempt was undertaken two groups of rural Ranga Reddy District of Telangana State English learners of different proficiencies who belonged to 8,9 and 10th class levels. This investigation proved that the English learners of the Rural Students in Telangana depended on three major important things. They are 1. Native language Transfer 2. Synonymy 3. Over generalization. This study also showed that the high and low proficiency learners were almost familiar with collocations and delexical verbs. -
6 the Major Parts of Speech
6 The Major Parts of Speech KEY CONCEPTS Parts of Speech Major Parts of Speech Nouns Verbs Adjectives Adverbs Appendix: prototypes INTRODUCTION In every language we find groups of words that share grammatical charac- teristics. These groups are called “parts of speech,” and we examine them in this chapter and the next. Though many writers onlanguage refer to “the eight parts of speech” (e.g., Weaver 1996: 254), the actual number of parts of speech we need to recognize in a language is determined by how fine- grained our analysis of the language is—the more fine-grained, the greater the number of parts of speech that will be distinguished. In this book we distinguish nouns, verbs, adjectives, and adverbs (the major parts of speech), and pronouns, wh-words, articles, auxiliary verbs, prepositions, intensifiers, conjunctions, and particles (the minor parts of speech). Every literate person needs at least a minimal understanding of parts of speech in order to be able to use such commonplace items as diction- aries and thesauruses, which classify words according to their parts (and sub-parts) of speech. For example, the American Heritage Dictionary (4th edition, p. xxxi) distinguishes adjectives, adverbs, conjunctions, definite ar- ticles, indefinite articles, interjections, nouns, prepositions, pronouns, and verbs. It also distinguishes transitive, intransitive, and auxiliary verbs. Writ- ers and writing teachers need to know about parts of speech in order to be able to use and teach about style manuals and school grammars. Regardless of their discipline, teachers need this information to be able to help students expand the contexts in which they can effectively communicate. -
Phrasal Verbs As Learning Material in Business English Courses For
Phrasal verbs as learning material in Business English courses for students majoring in Linguistics Phrasal verbs as learning material in Business English by Alexander V. Litvinov, Svetlana A. Burikova and Dmitry S. Khramchenko courses for students majoring in Linguistics by Alexander V. Litvinov, Svetlana A. Burikova and Dmitry S. Khramchenko enough to sound convincingly authentic. It is ‘Phrasal verbs can serve as a rhetorical skills and ability for sophisticated good example of the kind of Alexander V. Litvinov Peoples’ Friendship University of Russia (RUDN University) [email protected] communication that help impress British and Svetlana A. Burikova Peoples’ Friendship University of Russia (RUDN University) [email protected] problem Russians and other American partners through expression of thoughts Dmitry S. Khramchenko Tula State Lev Tolstoy Pedagogical University [email protected] and ideas in a clear way and get all necessary nationality non-native speakers Published in Training, Language and Culture Vol 1 Issue 4 (2017) pp. 84-98 doi: 10.29366/2017tlc.1.4.6 messages across. Years of teaching practice prove of English face’ Recommended citation format: Litvinov, A. V., Burikova, S. A., & Khramchenko, D. S. (2017). An acoustic that main problems for EFL students can be analysis of the production of word-initial stop /p/ by late Arab bilinguals. Training, Language and Culture, 1(4), classified into several categories: (1) English linguistics and pragmatics, most notably by 84-98. doi: 10.29366/2017tlc.1.4.6 linguistic phenomena that have direct equivalents Professor Evgeniya Ponomarenko and Professor The study highlights the existing views on the nature of English phrasal verbs and their theoretical grounding in Russian in the learners’ native tongue; (2) English language Elena Malyuga (Ponomarenko & Malyuga, 2012; and English linguistics. -
Entity Extraction: from Unstructured Text to Dbpedia RDF Triples Exner
Entity extraction: From unstructured text to DBpedia RDF triples Exner, Peter; Nugues, Pierre Published in: Proceedings of the Web of Linked Entities Workshop in conjuction with the 11th International Semantic Web Conference (ISWC 2012) 2012 Link to publication Citation for published version (APA): Exner, P., & Nugues, P. (2012). Entity extraction: From unstructured text to DBpedia RDF triples. In Proceedings of the Web of Linked Entities Workshop in conjuction with the 11th International Semantic Web Conference (ISWC 2012) (pp. 58-69). CEUR. http://ceur-ws.org/Vol-906/paper7.pdf Total number of authors: 2 General rights Unless other specific re-use rights are stated the following general rights apply: Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal Read more about Creative commons licenses: https://creativecommons.org/licenses/ Take down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. LUND UNIVERSITY PO Box 117 221 00 Lund +46 46-222 00 00 Entity Extraction: From Unstructured Text to DBpedia RDF Triples Peter Exner and Pierre Nugues Department of Computer science Lund University [email protected] [email protected] Abstract. -
Tenses and Conjugation (Pdf)
Created by the Evergreen Writing Center Library 3407 867-6420 Tenses and Conjugation Using correct verb forms is crucial to communicating coherently. Understanding how to apply different tenses and properly conjugate verbs will give you the tools with which to craft clear, effective sentences. Conjugations A conjugation is a list of verb forms. It catalogues the person, number, tense, voice, and mood of a verb. Knowing how to conjugate verbs correctly will help you match verbs with their subjects, and give you a firmer grasp on how verbs function in different sentences. Here is a sample conjugation table: Present Tense, Active Voice, Indicative Mood: Jump Person Singular Plural 1st Person I jump we jump 2nd Person you jump you jump 3rd Person he/she/it jumps they jump Person: Person is divided into three categories (first, second, and third person), and tells the reader whether the subject is speaking, is spoken to, or is spoken about. Each person is expressed using different subjects: first person uses I or we; second person uses you; and third person uses he/she/it or they. Keep in mind that these words are not the only indicators of person; for example in the sentence “Shakespeare uses images of the divine in his sonnets to represent his own delusions of grandeur”, the verb uses is in the third person because Shakespeare could be replaced by he, an indicator of the third person. Number: Number refers to whether the verb is singular or plural. Tense: Tense tells the reader when the action of a verb takes place. -
Mining Text Data
Mining Text Data Charu C. Aggarwal • ChengXiang Zhai Editors Mining Text Data Editors Charu C. Aggarwal ChengXiang Zhai IBM T.J. Watson Research Center University of Illinois at Urbana-Champaign Yorktown Heights, NY, USA Urbana, IL, USA [email protected] [email protected] ISBN 978-1-4614-3222-7 e-ISBN 978-1-4614-3223-4 DOI 10.1007/978-1-4614-3223-4 Springer New York Dordrecht Heidelberg London Library of Congress Control Number: 2012930923 © Springer Science+Business Media, LLC 2012 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com) Contents 1 An Introduction to Text Mining 1 Charu C. Aggarwal and ChengXiang Zhai 1. Introduction 1 2. Algorithms for Text Mining 4 3. Future Directions 8 References 10 2 Information Extraction from Text 11 Jing Jiang 1. Introduction 11 2. Named Entity Recognition 15 2.1 Rule-based Approach 16 2.2 Statistical Learning Approach 17 3. -
Constructions and Result: English Phrasal Verbs As Analysed in Construction Grammar
CONSTRUCTIONS AND RESULT: ENGLISH PHRASAL VERBS AS ANALYSED IN CONSTRUCTION GRAMMAR by ANNA L. OLSON A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF ARTS in THE FACULTY OF GRADUATE STUDIES Master of Arts in Linguistics, Analytical Stream We accept this thesis as conforming to the required standard ............................................................................... Dr. Emma Pavey, PhD; Thesis Supervisor ................................................................................ Dr. Sean Allison, Ph.D.; Second Reader ................................................................................ Dr. David Weber, Ph.D.; External Examiner TRINITY WESTERN UNIVERSITY September 2013 © Anna L. Olson i Abstract This thesis explores the difference between separable and non-separable transitive English phrasal verbs, focusing on finding a reason for the non-separable verbs’ lack of compatibility with the word order alternation which is present with the separable phrasal verbs. The analysis is formed from a synthesis of ideas based on the work of Bolinger (1971) and Gorlach (2004). A simplified version of Cognitive Construction Grammar is used to analyse and categorize the phrasal verb constructions. The results indicate that separable and non-separable transitive English phrasal verbs are similar but different constructions with specific syntactic reasons for the incompatibility of the word order alternation with the non-separable verbs. ii Table of Contents Abstract ...........................................................................................................................................