A Review on Wordnet and Vector Space Analysis for Short-Text Semantic Similarity
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A Way with Words: Recent Advances in Lexical Theory and Analysis: a Festschrift for Patrick Hanks
A Way with Words: Recent Advances in Lexical Theory and Analysis: A Festschrift for Patrick Hanks Gilles-Maurice de Schryver (editor) (Ghent University and University of the Western Cape) Kampala: Menha Publishers, 2010, vii+375 pp; ISBN 978-9970-10-101-6, e59.95 Reviewed by Paul Cook University of Toronto In his introduction to this collection of articles dedicated to Patrick Hanks, de Schryver presents a quote from Atkins referring to Hanks as “the ideal lexicographer’s lexicogra- pher.” Indeed, Hanks has had a formidable career in lexicography, including playing an editorial role in the production of four major English dictionaries. But Hanks’s achievements reach far beyond lexicography; in particular, Hanks has made numerous contributions to computational linguistics. Hanks is co-author of the tremendously influential paper “Word association norms, mutual information, and lexicography” (Church and Hanks 1989) and maintains close ties to our field. Furthermore, Hanks has advanced the understanding of the relationship between words and their meanings in text through his theory of norms and exploitations (Hanks forthcoming). The range of Hanks’s interests is reflected in the authors and topics of the articles in this Festschrift; this review concentrates more on those articles that are likely to be of most interest to computational linguists, and does not discuss some articles that appeal primarily to a lexicographical audience. Following the introduction to Hanks and his career by de Schryver, the collection is split into three parts: Theoretical Aspects and Background, Computing Lexical Relations, and Lexical Analysis and Dictionary Writing. 1. Part I: Theoretical Aspects and Background Part I begins with an unfinished article by the late John Sinclair, in which he begins to put forward an argument that multi-word units of meaning should be given the same status in dictionaries as ordinary headwords. -
Finetuning Pre-Trained Language Models for Sentiment Classification of COVID19 Tweets
Technological University Dublin ARROW@TU Dublin Dissertations School of Computer Sciences 2020 Finetuning Pre-Trained Language Models for Sentiment Classification of COVID19 Tweets Arjun Dussa Technological University Dublin Follow this and additional works at: https://arrow.tudublin.ie/scschcomdis Part of the Computer Engineering Commons Recommended Citation Dussa, A. (2020) Finetuning Pre-trained language models for sentiment classification of COVID19 tweets,Dissertation, Technological University Dublin. doi:10.21427/fhx8-vk25 This Dissertation is brought to you for free and open access by the School of Computer Sciences at ARROW@TU Dublin. It has been accepted for inclusion in Dissertations by an authorized administrator of ARROW@TU Dublin. For more information, please contact [email protected], [email protected]. This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 4.0 License Finetuning Pre-trained language models for sentiment classification of COVID19 tweets Arjun Dussa A dissertation submitted in partial fulfilment of the requirements of Technological University Dublin for the degree of M.Sc. in Computer Science (Data Analytics) September 2020 Declaration I certify that this dissertation which I now submit for examination for the award of MSc in Computing (Data Analytics), is entirely my own work and has not been taken from the work of others save and to the extent that such work has been cited and acknowledged within the test of my work. This dissertation was prepared according to the regulations for postgraduate study of the Technological University Dublin and has not been submitted in whole or part for an award in any other Institute or University. -
Lexical Analysis
From words to numbers Parsing, tokenization, extract information Natural Language Processing Piotr Fulmański Lecture goals • Tokenizing your text into words and n-grams (tokens) • Compressing your token vocabulary with stemming and lemmatization • Building a vector representation of a statement Natural Language Processing in Action by Hobson Lane Cole Howard Hannes Max Hapke Manning Publications, 2019 Terminology Terminology • A phoneme is a unit of sound that distinguishes one word from another in a particular language. • In linguistics, a word of a spoken language can be defined as the smallest sequence of phonemes that can be uttered in isolation with objective or practical meaning. • The concept of "word" is usually distinguished from that of a morpheme. • Every word is composed of one or more morphemes. • A morphem is the smallest meaningful unit in a language even if it will not stand on its own. A morpheme is not necessarily the same as a word. The main difference between a morpheme and a word is that a morpheme sometimes does not stand alone, but a word, by definition, always stands alone. Terminology SEGMENTATION • Text segmentation is the process of dividing written text into meaningful units, such as words, sentences, or topics. • Word segmentation is the problem of dividing a string of written language into its component words. Text segmentation, retrieved 2020-10-20, https://en.wikipedia.org/wiki/Text_segmentation Terminology SEGMENTATION IS NOT SO EASY • Trying to resolve the question of what a word is and how to divide up text into words we can face many "exceptions": • Is “ice cream” one word or two to you? Don’t both words have entries in your mental dictionary that are separate from the compound word “ice cream”? • What about the contraction “don’t”? Should that string of characters be split into one or two “packets of meaning?” • The single statement “Don’t!” means “Don’t you do that!” or “You, do not do that!” That’s three hidden packets of meaning for a total of five tokens you’d like your machine to know about. -
Document and Topic Models: Plsa
10/4/2018 Document and Topic Models: pLSA and LDA Andrew Levandoski and Jonathan Lobo CS 3750 Advanced Topics in Machine Learning 2 October 2018 Outline • Topic Models • pLSA • LSA • Model • Fitting via EM • pHITS: link analysis • LDA • Dirichlet distribution • Generative process • Model • Geometric Interpretation • Inference 2 1 10/4/2018 Topic Models: Visual Representation Topic proportions and Topics Documents assignments 3 Topic Models: Importance • For a given corpus, we learn two things: 1. Topic: from full vocabulary set, we learn important subsets 2. Topic proportion: we learn what each document is about • This can be viewed as a form of dimensionality reduction • From large vocabulary set, extract basis vectors (topics) • Represent document in topic space (topic proportions) 푁 퐾 • Dimensionality is reduced from 푤푖 ∈ ℤ푉 to 휃 ∈ ℝ • Topic proportion is useful for several applications including document classification, discovery of semantic structures, sentiment analysis, object localization in images, etc. 4 2 10/4/2018 Topic Models: Terminology • Document Model • Word: element in a vocabulary set • Document: collection of words • Corpus: collection of documents • Topic Model • Topic: collection of words (subset of vocabulary) • Document is represented by (latent) mixture of topics • 푝 푤 푑 = 푝 푤 푧 푝(푧|푑) (푧 : topic) • Note: document is a collection of words (not a sequence) • ‘Bag of words’ assumption • In probability, we call this the exchangeability assumption • 푝 푤1, … , 푤푁 = 푝(푤휎 1 , … , 푤휎 푁 ) (휎: permutation) 5 Topic Models: Terminology (cont’d) • Represent each document as a vector space • A word is an item from a vocabulary indexed by {1, … , 푉}. We represent words using unit‐basis vectors. -
Lexical Sense Labeling and Sentiment Potential Analysis Using Corpus-Based Dependency Graph
mathematics Article Lexical Sense Labeling and Sentiment Potential Analysis Using Corpus-Based Dependency Graph Tajana Ban Kirigin 1,* , Sanda Bujaˇci´cBabi´c 1 and Benedikt Perak 2 1 Department of Mathematics, University of Rijeka, R. Matejˇci´c2, 51000 Rijeka, Croatia; [email protected] 2 Faculty of Humanities and Social Sciences, University of Rijeka, SveuˇcilišnaAvenija 4, 51000 Rijeka, Croatia; [email protected] * Correspondence: [email protected] Abstract: This paper describes a graph method for labeling word senses and identifying lexical sentiment potential by integrating the corpus-based syntactic-semantic dependency graph layer, lexical semantic and sentiment dictionaries. The method, implemented as ConGraCNet application on different languages and corpora, projects a semantic function onto a particular syntactical de- pendency layer and constructs a seed lexeme graph with collocates of high conceptual similarity. The seed lexeme graph is clustered into subgraphs that reveal the polysemous semantic nature of a lexeme in a corpus. The construction of the WordNet hypernym graph provides a set of synset labels that generalize the senses for each lexical cluster. By integrating sentiment dictionaries, we introduce graph propagation methods for sentiment analysis. Original dictionary sentiment values are integrated into ConGraCNet lexical graph to compute sentiment values of node lexemes and lexical clusters, and identify the sentiment potential of lexemes with respect to a corpus. The method can be used to resolve sparseness of sentiment dictionaries and enrich the sentiment evaluation of Citation: Ban Kirigin, T.; lexical structures in sentiment dictionaries by revealing the relative sentiment potential of polysemous Bujaˇci´cBabi´c,S.; Perak, B. Lexical Sense Labeling and Sentiment lexemes with respect to a specific corpus. -
Redalyc.Latent Dirichlet Allocation Complement in the Vector Space Model for Multi-Label Text Classification
International Journal of Combinatorial Optimization Problems and Informatics E-ISSN: 2007-1558 [email protected] International Journal of Combinatorial Optimization Problems and Informatics México Carrera-Trejo, Víctor; Sidorov, Grigori; Miranda-Jiménez, Sabino; Moreno Ibarra, Marco; Cadena Martínez, Rodrigo Latent Dirichlet Allocation complement in the vector space model for Multi-Label Text Classification International Journal of Combinatorial Optimization Problems and Informatics, vol. 6, núm. 1, enero-abril, 2015, pp. 7-19 International Journal of Combinatorial Optimization Problems and Informatics Morelos, México Available in: http://www.redalyc.org/articulo.oa?id=265239212002 How to cite Complete issue Scientific Information System More information about this article Network of Scientific Journals from Latin America, the Caribbean, Spain and Portugal Journal's homepage in redalyc.org Non-profit academic project, developed under the open access initiative © International Journal of Combinatorial Optimization Problems and Informatics, Vol. 6, No. 1, Jan-April 2015, pp. 7-19. ISSN: 2007-1558. Latent Dirichlet Allocation complement in the vector space model for Multi-Label Text Classification Víctor Carrera-Trejo1, Grigori Sidorov1, Sabino Miranda-Jiménez2, Marco Moreno Ibarra1 and Rodrigo Cadena Martínez3 Centro de Investigación en Computación1, Instituto Politécnico Nacional, México DF, México Centro de Investigación e Innovación en Tecnologías de la Información y Comunicación (INFOTEC)2, Ags., México Universidad Tecnológica de México3 – UNITEC MÉXICO [email protected], {sidorov,marcomoreno}@cic.ipn.mx, [email protected] [email protected] Abstract. In text classification task one of the main problems is to choose which features give the best results. Various features can be used like words, n-grams, syntactic n-grams of various types (POS tags, dependency relations, mixed, etc.), or a combinations of these features can be considered. -
Evaluating Vector-Space Models of Word Representation, Or, the Unreasonable Effectiveness of Counting Words Near Other Words
Evaluating Vector-Space Models of Word Representation, or, The Unreasonable Effectiveness of Counting Words Near Other Words Aida Nematzadeh, Stephan C. Meylan, and Thomas L. Griffiths University of California, Berkeley fnematzadeh, smeylan, tom griffi[email protected] Abstract angle between word vectors (e.g., Mikolov et al., 2013b; Pen- nington et al., 2014). Vector-space models of semantics represent words as continuously-valued vectors and measure similarity based on In this paper, we examine whether these constraints im- the distance or angle between those vectors. Such representa- ply that Word2Vec and GloVe representations suffer from the tions have become increasingly popular due to the recent de- same difficulty as previous vector-space models in capturing velopment of methods that allow them to be efficiently esti- mated from very large amounts of data. However, the idea human similarity judgments. To this end, we evaluate these of relating similarity to distance in a spatial representation representations on a set of tasks adopted from Griffiths et al. has been criticized by cognitive scientists, as human similar- (2007) in which the authors showed that the representations ity judgments have many properties that are inconsistent with the geometric constraints that a distance metric must obey. We learned by another well-known vector-space model, Latent show that two popular vector-space models, Word2Vec and Semantic Analysis (Landauer and Dumais, 1997), were in- GloVe, are unable to capture certain critical aspects of human consistent with patterns of semantic similarity demonstrated word association data as a consequence of these constraints. However, a probabilistic topic model estimated from a rela- in human word association data. -
Gensim Is Robust in Nature and Has Been in Use in Various Systems by Various People As Well As Organisations for Over 4 Years
Gensim i Gensim About the Tutorial Gensim = “Generate Similar” is a popular open source natural language processing library used for unsupervised topic modeling. It uses top academic models and modern statistical machine learning to perform various complex tasks such as Building document or word vectors, Corpora, performing topic identification, performing document comparison (retrieving semantically similar documents), analysing plain-text documents for semantic structure. Audience This tutorial will be useful for graduates, post-graduates, and research students who either have an interest in Natural Language Processing (NLP), Topic Modeling or have these subjects as a part of their curriculum. The reader can be a beginner or an advanced learner. Prerequisites The reader must have basic knowledge about NLP and should also be aware of Python programming concepts. Copyright & Disclaimer Copyright 2020 by Tutorials Point (I) Pvt. Ltd. All the content and graphics published in this e-book are the property of Tutorials Point (I) Pvt. Ltd. The user of this e-book is prohibited to reuse, retain, copy, distribute or republish any contents or a part of contents of this e-book in any manner without written consent of the publisher. We strive to update the contents of our website and tutorials as timely and as precisely as possible, however, the contents may contain inaccuracies or errors. Tutorials Point (I) Pvt. Ltd. provides no guarantee regarding the accuracy, timeliness or completeness of our website or its contents including this tutorial. If you discover any errors on our website or in this tutorial, please notify us at [email protected] ii Gensim Table of Contents About the Tutorial .......................................................................................................................................... -
Deconstructing Word Embedding Models
Deconstructing Word Embedding Models Koushik K. Varma Ashoka University, [email protected] Abstract – Analysis of Word Embedding Models through Analysis, one must be certain that all linguistic aspects of a a deconstructive approach reveals their several word are captured within the vector representations because shortcomings and inconsistencies. These include any discrepancies could be further amplified in practical instability of the vector representations, a distorted applications. While these vector representations have analogical reasoning, geometric incompatibility with widespread use in modern natural language processing, it is linguistic features, and the inconsistencies in the corpus unclear as to what degree they accurately encode the data. A new theoretical embedding model, ‘Derridian essence of language in its structural, contextual, cultural and Embedding,’ is proposed in this paper. Contemporary ethical assimilation. Surface evaluations on training-test embedding models are evaluated qualitatively in terms data provide efficiency measures for a specific of how adequate they are in relation to the capabilities of implementation but do not explicitly give insights on the a Derridian Embedding. factors causing the existing efficiency (or the lack there of). We henceforth present a deconstructive review of 1. INTRODUCTION contemporary word embeddings using available literature to clarify certain misconceptions and inconsistencies Saussure, in the Course in General Linguistics, concerning them. argued that there is no substance in language and that all language consists of differences. In language there are only forms, not substances, and by that he meant that all 1.1 DECONSTRUCTIVE APPROACH apparently substantive units of language are generated by other things that lie outside them, but these external Derrida, a post-structualist French philosopher, characteristics are actually internal to their make-up. -
Automated Citation Sentiment Analysis Using High Order N-Grams: a Preliminary Investigation
Turkish Journal of Electrical Engineering & Computer Sciences Turk J Elec Eng & Comp Sci (2018) 26: 1922 – 1932 http://journals.tubitak.gov.tr/elektrik/ © TÜBİTAK Research Article doi:10.3906/elk-1712-24 Automated citation sentiment analysis using high order n-grams: a preliminary investigation Muhammad Touseef IKRAM1;∗,, Muhammad Tanvir AFZAL1, Naveed Anwer BUTT2, 1Department of Computer Science, Capital University of Science and Technology, Islamabad, Pakistan 2Department of Computer Science, University of Gujrat, Gujrat, Pakistan Received: 03.12.2017 • Accepted/Published Online: 02.02.2018 • Final Version: 27.07.2018 Abstract: Scientific papers hold an association with previous research contributions (i.e. books, journals or conference papers, and web resources) in the form of citations. Citations are deemed as a link or relatedness of the previous work to the cited work. The nature of the cited material could be supportive (positive), contrastive (negative), or objective (neutral). Extraction of the author’s sentiment towards the cited scientific articles is an emerging research discipline due to various linguistic differences between the citation sentences and other domains of sentiment analysis. In this paper, we propose a technique for the identification of the sentiment of the citing author towards the cited paper by extracting unigram, bigram, trigram, and pentagram adjective and adverb patterns from the citation text. After POS tagging of the citation text, we use the sentence parser for the extraction of linguistic features comprising adjectives, adverbs, and n-grams from the citation text. A sentiment score is then assigned to distinguish them as positive, negative, and neutral. In addition, the proposed technique is compared with manually classified citation text and 2 commercial tools, namely SEMANTRIA and THEYSAY, to determine their applicability to the citation corpus. -
A Framework for Representing Lexical Resources
A framework for representing lexical resources Fabrice Issac LDI Universite´ Paris 13 Abstract demonstration of the use of lexical resources in different languages. Our goal is to propose a description model for the lexicon. We describe a software 2 Context framework for representing the lexicon and its variations called Proteus. Various Whatever the writing system of a language (logo- examples show the different possibilities graphic, syllabic or alphabetic), it seems that the offered by this tool. We conclude with word is a central concept. Nonetheless, the very a demonstration of the use of lexical re- definition of a word is subject to variation depend- sources in complex, real examples. ing on the language studied. For some Asian lan- guages such as Mandarin or Vietnamese, the no- 1 Introduction tion of word delimiter does not exist ; for others, such as French or English, the space is a good in- Natural language processing relies as well on dicator. Likewise, for some languages, preposi- methods, algorithms, or formal models as on lin- tions are included in words, while for others they guistic resources. Processing textual data involves form a separate unit. a classic sequence of steps : it begins with the nor- malisation of data and ends with their lexical, syn- Languages can be classified according to their tactic and semantic analysis. Lexical resources are morphological mechanisms and their complexity ; the first to be used and must be of excellent qual- for instance, the morphological systems of French ity. or English are relatively simple compared to that Traditionally, a lexical resource is represented of Arabic or Greek. -
Vector Space Models for Words and Documents Statistical Natural Language Processing ELEC-E5550 Jan 30, 2019 Tiina Lindh-Knuutila D.Sc
Vector space models for words and documents Statistical Natural Language Processing ELEC-E5550 Jan 30, 2019 Tiina Lindh-Knuutila D.Sc. (Tech) Lingsoft Language Services & Aalto University tiina.lindh-knuutila -at- aalto dot fi Material also by Mari-Sanna Paukkeri (mari-sanna.paukkeri -at- utopiaanalytics dot com) Today’s Agenda • Vector space models • word-document matrices • word vectors • stemming, weighting, dimensionality reduction • similarity measures • Count models vs. predictive models • Word2vec • Information retrieval (Briefly) • Course project details You shall know the word by the company it keeps • Language is symbolic in nature • Surface form is in an arbitrary relation with the meaning of the word • Hat vs. cat • One substitution: Levenshtein distance of 1 • Does not measure the semantic similarity of the words • Distributional semantics • Linguistic items which appear in similar contexts in large samples of language data tend to have similar meanings Firth, John R. 1957. A synopsis of linguistic theory 1930–1955. In Studies in linguistic analysis, 1–32. Oxford: Blackwell. George Miller and Walter Charles. 1991. Contextual correlates of semantic similarity. Language and Cognitive Processes, 6(1):1–28. Vector space models (VSM) • The use of a high-dimensional space of documents (or words) • Closeness in the vector space resembles closeness in the semantics or structure of the documents (depending on the features extracted). • Makes the use of data mining possible • Applications: – Document clustering/classification/… • Finding similar documents • Finding similar words – Word disambiguation – Information retrieval • Term discrimination: ranking keywords in the order of usefulness Vector space models (VSM) • Steps to build a vector space model 1. Preprocessing 2.