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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. -
Lexical Semantics I
Semantic Theory: Lexical Semantics I Summer 2007 M.Pinkal/ S. Thater • 03-07-07 Lecture: Lexical Semantics I • 05-07-07 Lecture: Lexical Semantics II • 10-07-07 Lecture: Everything else • 12-07-07 Exercise: lexical Semantics • 17-07-07 Question time, Sample exam • 19-07-07 Individual question time • 25-07-07 Final exam, 11:00 (s.t.) Semantic Theory, SS 2007 © M. Pinkal, S. Thater 2 Structure of this course • Sentence semantics • Discourse semantics • Lexical semantics Semantic Theory, SS 2007 © M. Pinkal, S. Thater 3 Dolphins in First-order Logic Dolphins are mammals, not fish. !d (dolphin'(d)"mammal'(d) #¬fish'(d)) Dolphins live-in pods. !d (dolphin'(d)" $x (pod'(p)live-in'(d,p)) Dolphins give birth to one baby at a time. !d (dolphin'(d)" !x !y !t (give-birth-to' (d,x,t)give-birth-to' (d,y,t) " x=y) Semantic Theory, SS 2007 © M. Pinkal, S. Thater 4 Dolphins in First-order Logic Dolphins are mammals, not fish. !d (dolphin'(d)"mammal'(d) #¬fish'(d)) Dolphins live-in pods. !d (dolphin'(d)" $x (pod'(p)live-in'(d,p)) Dolphins give birth to one baby at a time. !d (dolphin'(d)" !x !y !t (give-birth-to' (d,x,t)give-birth-to' (d,y,t) " x=y) Semantic Theory, SS 2007 © M. Pinkal, S. Thater 5 The dolphin text Dolphins are mammals, not fish. They are warm blooded like man, and give birth to one baby called a calf at a time. At birth a bottlenose dolphin calf is about 90-130 cms long and will grow to approx. -
LEXADV - a Multilingual Semantic Lexicon for Adverbs
LEXADV - a multilingual semantic Lexicon for Adverbs Sanni Nimb Center for Sprogteknologi (CST) – Københavns Universitet Njalsgade 80, Copenhagen, Denmark [email protected] Abstract The LEXADV-project is a Scandinavian research project (2004-2006, financed by Nordplus Sprog) with the aim of extending three Scandinavian semantic lexicons building on the SIMPLE lexicon model (Lenci et al., 2000) with the word class of adverbs. In the lexicons of approx. 400 Danish, Norwegian and Swedish adverbs the different senses are described with a semantic type and a set of semantic features. A classification covering the many meanings that adverbs can have has been established and integrated in the original SIMPLE ontology. Similarly new features have been added to the model in order to describe the adverb senses. The working method of the project builds on the fact that the vocabularies of Danish, Norwegian and Swedish are closely related. An encoding tool has been developed with the special purpose of permitting easy transfer of semantic types and features between entries in the three languages. The Danish adverb senses have been described first, based on the definition in a modern, comprehensive Danish dictionary. Afterwards the lemmas have been translated and the semantic data have been copied into the Swedish as well as into the Norwegian equivalent entry. Finally these copies have been evaluated and when necessary adjusted by native speakers. ’Telic’, ’Agentive’ and ’Constitutive’ has been extended 1. The Scandinavian SIMPLE Lexicons with three more semantic types. The first one, ’Functional’, covers adverb senses of 1.1. Background which the meaning must be described by logic expressions referring to sets, either in the context or in The Danish and the Swedish SIMPLE lexicons are the discourse. -
A New Semantic Lexicon and Similarity Measure in Bangla
A New Semantic Lexicon and Similarity Measure in Bangla Manjira Sinha, Abhik Jana, Tirthankar Dasgupta, Anupam Basu Indian Institute of Technology Kharagpur {manjira87, abhikjana1, iamtirthankar, anupambas}@gmail.com ABSTRACT The Mental Lexicon (ML) refers to the organization of lexical entries of a language in the human mind.A clear knowledge of the structure of ML will help us to understand how the human brain processes language. The knowledge of semantic association among the words in ML is essential to many applications. Although, there are works on the representation of lexical entries based on their semantic association in the form of a lexicon in English and other languages, such works of Bangla is in a nascent stage. In this paper, we have proposed a distinct lexical organization based on semantic association between Bangla words which can be accessed efficiently by different applications. We have developed a novel approach of measuring the semantic similarity between words and verified it against user study. Further, a GUI has been designed for easy and efficient access. KEYWORDS : Bangla Lexicon, Synset, Semantic Similarity, Hierarchical Graph 1 Introduction The lexicon of a language is a collection of lexical entries consisting of information regarding words and expressions, comprising both form and meaning (Levelt,). Form refers to the orthography, phonology and morphology of the lexical item and Meaning refers to its syntactic and semantic information. The term Mental Lexicon refers to the organization and interaction of lexical entries of a language in the human mind. Depending on the definition of word , an adult knows and uses around 40000 to 150000 words. -
Text and Data Mining: Technologies Under Construction
Text and Data Mining: Technologies Under Construction WHO’S INSIDE Accenture Elsevier Science Europe American Institute of Figshare SciTech Strategies Biological Sciences General Electric SPARC Battelle IBM Sparrho Bristol-Myers Squibb Komatsu Spotfire Clinerion Linguamatics Limited Talix Columbia Pipeline Group MedAware UnitedHealth Group Copyright Clearance Mercedes-Benz Verisk Center, Inc. Meta VisTrails CrossRef Novartis Wellcome Trust Docear OMICtools Copyright Clearance Center, Inc. (CCC) has licensed this report from Outsell, Inc., with the right to distribute it for marketing and market education purposes. CCC did not commission this report, nor is this report a fee-for-hire white paper. Outsell’s fact-based research, analysis and rankings, and all aspects of our opinion were independently derived and CCC had no influence or involvement on the design or findings within this report. For questions, please contact Outsell at [email protected]. Market Advancing the Business of Information Performance January 22, 2016 Table of Contents Why This Topic 3 Methodology 3 How It Works 3 Applications 7 Scientific Research 7 Healthcare 10 Engineering 13 Challenges 15 Implications 17 10 to Watch 18 Essential Actions 21 Imperatives for Information Managers 22 Related Research 24 Table of Contents for Figures & Tables Table 1. Providers of TDM-Related Functions 5 © 2016 Outsell, Inc. 2 Why This Topic Text and data mining (TDM), also referred to as content mining, is a major focus for academia, governments, healthcare, and industry as a way to unleash the potential for previously undiscovered connections among people, places, things, and, for the purpose of this report, scientific, technical, and healthcare information. Although there have been continuous advances in methodologies and technologies, the power of TDM has not yet been fully realized, and technology struggles to keep up with the ever-increasing flow of content. -
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 -
The Diachronic Semantic Lexicon of Dutch As Linked Open Data
The Diachronic Semantic Lexicon of Dutch as Linked Open Data Katrien Depuydt, Jesse de Does Instituut voor de Nederlandse Taal Rapenburg 61, 2311GJ Leiden, The Netherlands [email protected], [email protected] Abstract This paper describes the Linked Open Data (LOD) model for the diachronic semantic lexicon DiaMaNT, currently under development at the Instituut voor de Nederlandse Taal (INT; Dutch Language Institute). The lexicon is part of a digital historical language infrastructure for Dutch at INT. This infrastructure, for which the core data is formed by the four major historical dictionaries of Dutch covering Dutch language from ca. 500 - ca 1976, currently consists of three modules: a dictionary portal, giving access to the historical dictionaries, a computational lexicon GiGaNT, providing information on words, their inflectional and spelling variation, and DiaMaNT, aimed at providing information on diachronic lexical variation (both semasiological and onomasiological). The DiaMaNT lexicon is built by adding a semantic layer to the word form lexicon GiGaNT, using the semantic information in the historical dictionaries. Ontolex-Lemon is a good point of departure for the LOD model, but we need extensions to be able to deal with the historical dictionary content incorporated in our lexicon. Keywords: Linked Open Data, Ontolex, Diachronic Lexicon, Semantic Lexicon, Historical Lexicography, Language Resources 1. Background digital, based on a closed corpus, in digital format. Hav- Even though Dutch lexicography1 can be dated back to the ing four scholarly dictionaries of Dutch in digital format 13th century with the glossarium Bernense, a Latin-Middle opened up opportunities for further exploitation of the con- Dutch word list, we had to wait until the 19th century for tents of these dictionaries. -
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-!: .... -
1 Application of Text Mining to Biomedical Knowledge Extraction: Analyzing Clinical Narratives and Medical Literature
Amy Neustein, S. Sagar Imambi, Mário Rodrigues, António Teixeira and Liliana Ferreira 1 Application of text mining to biomedical knowledge extraction: analyzing clinical narratives and medical literature Abstract: One of the tools that can aid researchers and clinicians in coping with the surfeit of biomedical information is text mining. In this chapter, we explore how text mining is used to perform biomedical knowledge extraction. By describing its main phases, we show how text mining can be used to obtain relevant information from vast online databases of health science literature and patients’ electronic health records. In so doing, we describe the workings of the four phases of biomedical knowledge extraction using text mining (text gathering, text preprocessing, text analysis, and presentation) entailed in retrieval of the sought information with a high accuracy rate. The chapter also includes an in depth analysis of the differences between clinical text found in electronic health records and biomedical text found in online journals, books, and conference papers, as well as a presentation of various text mining tools that have been developed in both university and commercial settings. 1.1 Introduction The corpus of biomedical information is growing very rapidly. New and useful results appear every day in research publications, from journal articles to book chapters to workshop and conference proceedings. Many of these publications are available online through journal citation databases such as Medline – a subset of the PubMed interface that enables access to Medline publications – which is among the largest and most well-known online databases for indexing profes- sional literature. Such databases and their associated search engines contain important research work in the biological and medical domain, including recent findings pertaining to diseases, symptoms, and medications. -
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. -
Linking Wordnet to the SIL Semantic Domains
Bringing together over- and under-represented languages: Linking Wordnet to the SIL Semantic Domains Muhammad Zulhelmy bin Mohd Rosman Francis Bond and Frantisekˇ Kratochv´ıl Linguistics and Multilingual Studies, Nanyang Technological University, Singapore [email protected], [email protected], [email protected] Abstract English, Japanese and Finnish (Bond and Paik, 2012). We have created an open-source mapping SD is designed for rapid construction and in- between the SIL’s semantic domains (used tuitive organization of lexicons, not primarily for for rapid lexicon building and organiza- the analysis of the resulting data. As a result, tion for under-resourced languages) and many potentially interesting relationships are only WordNet, the standard resource for lexical implicitly realized. By linking SD to WN we semantics in natural language processing. can take advantage of the relationships modeled in We show that the resources complement WN to make more of these explicit. For example, each other, and suggest ways in which the the semantic relations in WN would be a useful mapping can be improved even further. input into SD while the domains hierarchy would The semantic domains give more general enforce the existing WN relations. This will al- domain and associative links, which word- low more quantitative computational modeling of net still has few of, while wordnet gives under-resourced languages. explicit semantic relations between senses, It is currently an exciting time for field lexicog- which the domains lack. raphy with better tools and hardware allowing for rapid digitization of lexical resources. Typically, 1 Introduction linguists tag text soon after they collect it.