Analyzing Discourse Processing Using a Simple Natural Language Processing Tool
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Why Is Language Typology Possible?
Why is language typology possible? Martin Haspelmath 1 Languages are incomparable Each language has its own system. Each language has its own categories. Each language is a world of its own. 2 Or are all languages like Latin? nominative the book genitive of the book dative to the book accusative the book ablative from the book 3 Or are all languages like English? 4 How could languages be compared? If languages are so different: What could be possible tertia comparationis (= entities that are identical across comparanda and thus permit comparison)? 5 Three approaches • Indeed, language typology is impossible (non- aprioristic structuralism) • Typology is possible based on cross-linguistic categories (aprioristic generativism) • Typology is possible without cross-linguistic categories (non-aprioristic typology) 6 Non-aprioristic structuralism: Franz Boas (1858-1942) The categories chosen for description in the Handbook “depend entirely on the inner form of each language...” Boas, Franz. 1911. Introduction to The Handbook of American Indian Languages. 7 Non-aprioristic structuralism: Ferdinand de Saussure (1857-1913) “dans la langue il n’y a que des différences...” (In a language there are only differences) i.e. all categories are determined by the ways in which they differ from other categories, and each language has different ways of cutting up the sound space and the meaning space de Saussure, Ferdinand. 1915. Cours de linguistique générale. 8 Example: Datives across languages cf. Haspelmath, Martin. 2003. The geometry of grammatical meaning: semantic maps and cross-linguistic comparison 9 Example: Datives across languages 10 Example: Datives across languages 11 Non-aprioristic structuralism: Peter H. Matthews (University of Cambridge) Matthews 1997:199: "To ask whether a language 'has' some category is...to ask a fairly sophisticated question.. -
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 -
Modeling Language Variation and Universals: a Survey on Typological Linguistics for Natural Language Processing
Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing Edoardo Ponti, Helen O ’Horan, Yevgeni Berzak, Ivan Vulic, Roi Reichart, Thierry Poibeau, Ekaterina Shutova, Anna Korhonen To cite this version: Edoardo Ponti, Helen O ’Horan, Yevgeni Berzak, Ivan Vulic, Roi Reichart, et al.. Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing. 2018. hal-01856176 HAL Id: hal-01856176 https://hal.archives-ouvertes.fr/hal-01856176 Preprint submitted on 9 Aug 2018 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing Edoardo Maria Ponti∗ Helen O’Horan∗∗ LTL, University of Cambridge LTL, University of Cambridge Yevgeni Berzaky Ivan Vuli´cz Department of Brain and Cognitive LTL, University of Cambridge Sciences, MIT Roi Reichart§ Thierry Poibeau# Faculty of Industrial Engineering and LATTICE Lab, CNRS and ENS/PSL and Management, Technion - IIT Univ. Sorbonne nouvelle/USPC Ekaterina Shutova** Anna Korhonenyy ILLC, University of Amsterdam LTL, University of Cambridge Understanding cross-lingual variation is essential for the development of effective multilingual natural language processing (NLP) applications. -
Urdu Treebank
Sci.Int.(Lahore),28(4),3581-3585, 2016 ISSN 1013-5316;CODEN: SINTE 8 3581 URDU TREEBANK Muddassira Arshad, Aasim Ali Punjab University College of Information Technology (PUCIT), University of the Punjab, Lahore [email protected], [email protected] (Presented at the 5th International. Multidisciplinary Conference, 29-31 Oct., at, ICBS, Lahore) ABSTRACT: Treebank is a parsed corpus of text annotated with the syntactic information in the form of tags that yield the phrasal information within the corpus. The first outcome of this study is to design a phrasal and functional tag set for Urdu by minimal changes in the tag set of Penn Treebank, that has become a de-facto standard. Our tag set comprises of 22 phrasal tags and 20 functional tags. Second outcome of this work is the development of initial Treebank for Urdu, which remains publically available to the research and development community. There are 500 sentences of Urdu translation of religious text with an average length of 12 words. Context free grammar containing 109 rules and 313 lexical entries, for Urdu has been extracted from this Treebank. An online parser is used to verify the coverage of grammar on 50 test sentences with 80% recall. However, multiple parse trees are generated against each test sentence. 1 INTRODUCTION grammars for parsing[5], where statistical models are used The term "Treebank" was introduced in 2003. It is defined as for broad-coverage parsing [6]. "linguistically annotated corpus that includes some In literature, various techniques have been applied for grammatical analysis beyond the part of speech level" [1]. -
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. -
Linguistic Profiles: a Quantitative Approach to Theoretical Questions
Laura A. Janda USA – Norway, Th e University of Tromsø – Th e Arctic University of Norway Linguistic profi les: A quantitative approach to theoretical questions Key words: cognitive linguistics, linguistic profi les, statistical analysis, theoretical linguistics Ключевые слова: когнитивная лингвистика, лингвистические профили, статисти- ческий анализ, теоретическая лингвистика Abstract A major challenge in linguistics today is to take advantage of the data and sophisticated analytical tools available to us in the service of questions that are both theoretically inter- esting and practically useful. I offer linguistic profi les as one way to join theoretical insight to empirical research. Linguistic profi les are a more narrowly targeted type of behavioral profi ling, focusing on a specifi c factor and how it is distributed across language forms. I present case studies using Russian data and illustrating three types of linguistic profi ling analyses: grammatical profi les, semantic profi les, and constructional profi les. In connection with each case study I identify theoretical issues and show how they can be operationalized by means of profi les. The fi ndings represent real gains in our understanding of Russian grammar that can also be utilized in both pedagogical and computational applications. 1. Th e quantitative turn in linguistics and the theoretical challenge I recently conducted a study of articles published since the inauguration of the journal Cognitive Linguistics in 1990. At the year 2008, Cognitive Linguistics took a quanti- tative turn; since that point over 50% of the scholarly articles that journal publishes have involved quantitative analysis of linguistic data [Janda 2013: 4–6]. Though my sample was limited to only one journal, this trend is endemic in linguistics, and it is motivated by a confl uence of historic factors. -
Morphological Processing in the Brain: the Good (Inflection), the Bad (Derivation) and the Ugly (Compounding)
Morphological processing in the brain: the good (inflection), the bad (derivation) and the ugly (compounding) Article Published Version Creative Commons: Attribution 4.0 (CC-BY) Open Access Leminen, A., Smolka, E., Duñabeitia, J. A. and Pliatsikas, C. (2019) Morphological processing in the brain: the good (inflection), the bad (derivation) and the ugly (compounding). Cortex, 116. pp. 4-44. ISSN 0010-9452 doi: https://doi.org/10.1016/j.cortex.2018.08.016 Available at http://centaur.reading.ac.uk/78769/ It is advisable to refer to the publisher’s version if you intend to cite from the work. See Guidance on citing . To link to this article DOI: http://dx.doi.org/10.1016/j.cortex.2018.08.016 Publisher: Elsevier All outputs in CentAUR are protected by Intellectual Property Rights law, including copyright law. Copyright and IPR is retained by the creators or other copyright holders. Terms and conditions for use of this material are defined in the End User Agreement . www.reading.ac.uk/centaur CentAUR Central Archive at the University of Reading Reading’s research outputs online cortex 116 (2019) 4e44 Available online at www.sciencedirect.com ScienceDirect Journal homepage: www.elsevier.com/locate/cortex Special issue: Review Morphological processing in the brain: The good (inflection), the bad (derivation) and the ugly (compounding) Alina Leminen a,b,*,1, Eva Smolka c,1, Jon A. Dunabeitia~ d,e and Christos Pliatsikas f a Cognitive Science, Department of Digital Humanities, Faculty of Arts, University of Helsinki, Finland b Cognitive Brain Research -
Inflection), the Bad (Derivation) and the Ugly (Compounding)
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Central Archive at the University of Reading Morphological processing in the brain: the good (inflection), the bad (derivation) and the ugly (compounding) Article Published Version Creative Commons: Attribution 4.0 (CC-BY) Open Access Leminen, A., Smolka, E., Duñabeitia, J. A. and Pliatsikas, C. (2019) Morphological processing in the brain: the good (inflection), the bad (derivation) and the ugly (compounding). Cortex, 116. pp. 4-44. ISSN 0010-9452 doi: https://doi.org/10.1016/j.cortex.2018.08.016 Available at http://centaur.reading.ac.uk/78769/ It is advisable to refer to the publisher's version if you intend to cite from the work. See Guidance on citing . To link to this article DOI: http://dx.doi.org/10.1016/j.cortex.2018.08.016 Publisher: Elsevier All outputs in CentAUR are protected by Intellectual Property Rights law, including copyright law. Copyright and IPR is retained by the creators or other copyright holders. Terms and conditions for use of this material are defined in the End User Agreement . www.reading.ac.uk/centaur CentAUR Central Archive at the University of Reading Reading's research outputs online cortex 116 (2019) 4e44 Available online at www.sciencedirect.com ScienceDirect Journal homepage: www.elsevier.com/locate/cortex Special issue: Review Morphological processing in the brain: The good (inflection), the bad (derivation) and the ugly (compounding) Alina Leminen a,b,*,1, Eva Smolka c,1, Jon A. Dunabeitia~ d,e and Christos Pliatsikas -
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.