Facing the Facts of Fake: a Distributional Semantics and Corpus Annotation Approach Bert Cappelle, Pascal Denis, Mikaela Keller
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Semantic Computing
SEMANTIC COMPUTING Lecture 7: Introduction to Distributional and Distributed Semantics Dagmar Gromann International Center For Computational Logic TU Dresden, 30 November 2018 Overview • Distributional Semantics • Distributed Semantics – Word Embeddings Dagmar Gromann, 30 November 2018 Semantic Computing 2 Distributional Semantics Dagmar Gromann, 30 November 2018 Semantic Computing 3 Distributional Semantics Definition • meaning of a word is the set of contexts in which it occurs • no other information is used than the corpus-derived information about word distribution in contexts (co-occurrence information of words) • semantic similarity can be inferred from proximity in contexts • At the very core: Distributional Hypothesis Distributional Hypothesis “similarity of meaning correlates with similarity of distribution” - Harris Z. S. (1954) “Distributional structure". Word, Vol. 10, No. 2-3, pp. 146-162 meaning = use = distribution in context => semantic distance Dagmar Gromann, 30 November 2018 Semantic Computing 4 Remember Lecture 1? Types of Word Meaning • Encyclopaedic meaning: words provide access to a large inventory of structured knowledge (world knowledge) • Denotational meaning: reference of a word to object/concept or its “dictionary definition” (signifier <-> signified) • Connotative meaning: word meaning is understood by its cultural or emotional association (positive, negative, neutral conntation; e.g. “She’s a dragon” in Chinese and English) • Conceptual meaning: word meaning is associated with the mental concepts it gives access to (e.g. prototype theory) • Distributional meaning: “You shall know a word by the company it keeps” (J.R. Firth 1957: 11) John Rupert Firth (1957). "A synopsis of linguistic theory 1930-1955." In Special Volume of the Philological Society. Oxford: Oxford University Press. Dagmar Gromann, 30 November 2018 Semantic Computing 5 Distributional Hypothesis in Practice Study by McDonald and Ramscar (2001): • The man poured from a balack into a handleless cup. -
A Comparison of Word Embeddings and N-Gram Models for Dbpedia Type and Invalid Entity Detection †
information Article A Comparison of Word Embeddings and N-gram Models for DBpedia Type and Invalid Entity Detection † Hanqing Zhou *, Amal Zouaq and Diana Inkpen School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa ON K1N 6N5, Canada; [email protected] (A.Z.); [email protected] (D.I.) * Correspondence: [email protected]; Tel.: +1-613-562-5800 † This paper is an extended version of our conference paper: Hanqing Zhou, Amal Zouaq, and Diana Inkpen. DBpedia Entity Type Detection using Entity Embeddings and N-Gram Models. In Proceedings of the International Conference on Knowledge Engineering and Semantic Web (KESW 2017), Szczecin, Poland, 8–10 November 2017, pp. 309–322. Received: 6 November 2018; Accepted: 20 December 2018; Published: 25 December 2018 Abstract: This article presents and evaluates a method for the detection of DBpedia types and entities that can be used for knowledge base completion and maintenance. This method compares entity embeddings with traditional N-gram models coupled with clustering and classification. We tackle two challenges: (a) the detection of entity types, which can be used to detect invalid DBpedia types and assign DBpedia types for type-less entities; and (b) the detection of invalid entities in the resource description of a DBpedia entity. Our results show that entity embeddings outperform n-gram models for type and entity detection and can contribute to the improvement of DBpedia’s quality, maintenance, and evolution. Keywords: semantic web; DBpedia; entity embedding; n-grams; type identification; entity identification; data mining; machine learning 1. Introduction The Semantic Web is defined by Berners-Lee et al. -
A First Order Semantic Approach to Adjectival Inference
A first order semantic approach to adjectival inference Marilisa Amoia Claire gardent INRIA/Universite´ de Nancy 1 & CNRS/Loria University of the Saarland Campus Scientifique BP 239 Saarbrucken,¨ Germany 54506 Vandoeuvre-les-Nancy, France [email protected] [email protected] Abstract der automated reasoners that could be put to use to reason about the meaning of higher-order formulae. As shown in the formal semantics litera- In this paper, we present a semantics for adjec- ture, adjectives can display very different tives that adopts an ontologically promiscuous ap- inferential patterns depending on whether proach and thereby supports first order inference for they are intersective, privative, subsective all types of adjectives including extensional ones. or plain non subsective. Moreover, many Indeed, traditional semantic classifications of ad- of these classes are often described using jectives such as (?; ?; ?) subdivide adjectives second order constructs. In this paper, we into two classes namely extensional vs. intensional adopt Hobbs’s ontologically promiscuous adjectives, the latter grouping together adjectives approach and present a first order treatment which intuitively denote functions from properties of adjective semantics which opens the way to properties i.e., second order objects. for a sophisticated treatment of adjectival We present a compositional semantics for ad- inference. The approach was implemented jectives which both (i) defines a first order repre- and tested using first order automated rea- sentation and (ii) integrates interactions with other soners. sources of linguistic information such as lexical se- mantics and morpho-derivational relations. We then 1 Introduction show that the proposed semantics correctly predicts As has often been observed, not all of natural lan- the inferential patterns observed to hold of the var- guage meaning can be represented by first order ious adjective subclasses identified in the literature logic. -
Distributional Semantics
Distributional Semantics Computational Linguistics: Jordan Boyd-Graber University of Maryland SLIDES ADAPTED FROM YOAV GOLDBERG AND OMER LEVY Computational Linguistics: Jordan Boyd-Graber UMD Distributional Semantics 1 / 19 j j From Distributional to Distributed Semantics The new kid on the block Deep learning / neural networks “Distributed” word representations Feed text into neural-net. Get back “word embeddings”. Each word is represented as a low-dimensional vector. Vectors capture “semantics” word2vec (Mikolov et al) Computational Linguistics: Jordan Boyd-Graber UMD Distributional Semantics 2 / 19 j j From Distributional to Distributed Semantics This part of the talk word2vec as a black box a peek inside the black box relation between word-embeddings and the distributional representation tailoring word embeddings to your needs using word2vec Computational Linguistics: Jordan Boyd-Graber UMD Distributional Semantics 3 / 19 j j word2vec Computational Linguistics: Jordan Boyd-Graber UMD Distributional Semantics 4 / 19 j j word2vec Computational Linguistics: Jordan Boyd-Graber UMD Distributional Semantics 5 / 19 j j word2vec dog cat, dogs, dachshund, rabbit, puppy, poodle, rottweiler, mixed-breed, doberman, pig sheep cattle, goats, cows, chickens, sheeps, hogs, donkeys, herds, shorthorn, livestock november october, december, april, june, february, july, september, january, august, march jerusalem tiberias, jaffa, haifa, israel, palestine, nablus, damascus katamon, ramla, safed teva pfizer, schering-plough, novartis, astrazeneca, glaxosmithkline, sanofi-aventis, mylan, sanofi, genzyme, pharmacia Computational Linguistics: Jordan Boyd-Graber UMD Distributional Semantics 6 / 19 j j Working with Dense Vectors Word Similarity Similarity is calculated using cosine similarity: dog~ cat~ sim(dog~ ,cat~ ) = dog~ · cat~ jj jj jj jj For normalized vectors ( x = 1), this is equivalent to a dot product: jj jj sim(dog~ ,cat~ ) = dog~ cat~ · Normalize the vectors when loading them. -
Sentiment, Stance and Applications of Distributional Semantics
Sentiment, stance and applications of distributional semantics Maria Skeppstedt Sentiment, stance and applications of distributional semantics Sentiment analysis (opinion mining) • Aims at determining the attitude of the speaker/ writer * In a chunk of text (e.g., a document or sentence) * Towards a certain topic • Categories: * Positive, negative, (neutral) * More detailed Movie reviews • Excruciatingly unfunny and pitifully unromantic. • The picture emerges as a surprisingly anemic disappointment. • A sensitive, insightful and beautifully rendered film. • The spark of special anime magic here is unmistak- able and hard to resist. • Maybe you‘ll be lucky , and there‘ll be a power outage during your screening so you can get your money back. Methods for sentiment analysis • Train a machine learning classifier • Use lexicon and rules • (or combine the methods) Train a machine learning classifier Annotating text and training the model Excruciatingly Excruciatingly unfunny unfunny Applying the model pitifully unromantic. The model pitifully unromantic. Examples of machine learning Frameworks • Scikit-learn • NLTK (natural language toolkit) • CRF++ Methods for sentiment analysis • Train a machine learning classifier * See for instance Socher et al. (2013) that classified English movie review sentences into positive and negative sentiment. * Trained their classifier on 11,855 manually labelled sentences. (R. Socher, A. Perelygin, J. Wu, J. Chuang, C. D. Manning, A. Y. Ng, C. Potts, Recursive deep models for semantic composition- ality over a sentiment treebank) Methods based on lexicons and rules • Lexicons for different polarities • Sometimes with different strength on the words * Does not need the large amount of training data that is typically required for machine learning methods * More flexible (e.g, to add new sentiment poles and new languages) Commercial use of sentiment analysis I want to retrieve everything positive and negative that is said about the products I sell. -
Building Web-Interfaces for Vector Semantic Models with the Webvectors Toolkit
Building Web-Interfaces for Vector Semantic Models with the WebVectors Toolkit Andrey Kutuzov Elizaveta Kuzmenko University of Oslo Higher School of Economics Oslo, Norway Moscow, Russia [email protected] eakuzmenko [email protected] Abstract approaches, which allowed to train distributional models with large amounts of raw linguistic data We present WebVectors, a toolkit that very fast. The most established word embedding facilitates using distributional semantic algorithms in the field are highly efficient Continu- models in everyday research. Our toolkit ous Skip-Gram and Continuous Bag-of-Words, im- has two main features: it allows to build plemented in the famous word2vec tool (Mikolov web interfaces to query models using a et al., 2013b; Baroni et al., 2014), and GloVe in- web browser, and it provides the API to troduced in (Pennington et al., 2014). query models automatically. Our system Word embeddings represent the meaning of is easy to use and can be tuned according words with dense real-valued vectors derived from to individual demands. This software can word co-occurrences in large text corpora. They be of use to those who need to work with can be of use in almost any linguistic task: named vector semantic models but do not want to entity recognition (Siencnik, 2015), sentiment develop their own interfaces, or to those analysis (Maas et al., 2011), machine translation who need to deliver their trained models to (Zou et al., 2013; Mikolov et al., 2013a), cor- a large audience. WebVectors features vi- pora comparison (Kutuzov and Kuzmenko, 2015), sualizations for various kinds of semantic word sense frequency estimation for lexicogra- queries. -
Distributional Semantics for Understanding Spoken Meal Descriptions
DISTRIBUTIONAL SEMANTICS FOR UNDERSTANDING SPOKEN MEAL DESCRIPTIONS Mandy Korpusik, Calvin Huang, Michael Price, and James Glass∗ MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA 02139, USA fkorpusik, calvinh, pricem, [email protected] ABSTRACT This paper presents ongoing language understanding experiments conducted as part of a larger effort to create a nutrition dialogue system that automatically extracts food concepts from a user’s spo- ken meal description. We first discuss the technical approaches to understanding, including three methods for incorporating word vec- tor features into conditional random field (CRF) models for seman- tic tagging, as well as classifiers for directly associating foods with properties. We report experiments on both text and spoken data from an in-domain speech recognizer. On text data, we show that the ad- dition of word vector features significantly improves performance, achieving an F1 test score of 90.8 for semantic tagging and 86.3 for food-property association. On speech, the best model achieves an F1 test score of 87.5 for semantic tagging and 86.0 for association. Fig. 1. A diagram of the flow of the nutrition system [4]. Finally, we conduct an end-to-end system evaluation through a user study with human ratings of 83% semantic tagging accuracy. Index Terms— CRF, Word vectors, Semantic tagging • We demonstrate a significant improvement in semantic tag- ging performance by adding word embedding features to a classifier. We explore features for both the raw vector values 1. INTRODUCTION and similarity to prototype words from each category. In ad- dition, improving semantic tagging performance benefits the For many patients with obesity, diet tracking is often time- subsequent task of associating foods with properties. -
Distributional Semantics Today Introduction to the Special Issue Cécile Fabre, Alessandro Lenci
Distributional Semantics Today Introduction to the special issue Cécile Fabre, Alessandro Lenci To cite this version: Cécile Fabre, Alessandro Lenci. Distributional Semantics Today Introduction to the special issue. Revue TAL, ATALA (Association pour le Traitement Automatique des Langues), 2015, Sémantique distributionnelle, 56 (2), pp.7-20. hal-01259695 HAL Id: hal-01259695 https://hal.archives-ouvertes.fr/hal-01259695 Submitted on 26 Jan 2016 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. Distributional Semantics Today Introduction to the special issue Cécile Fabre* — Alessandro Lenci** * University of Toulouse, CLLE-ERSS ** University of Pisa, Computational Linguistics Laboratory ABSTRACT. This introduction to the special issue of the TAL journal on distributional seman- tics provides an overview of the current topics of this field and gives a brief summary of the contributions. RÉSUMÉ. Cette introduction au numéro spécial de la revue TAL consacré à la sémantique dis- tributionnelle propose un panorama des thèmes de recherche actuels dans ce champ et fournit un résumé succinct des contributions acceptées. KEYWORDS: Distributional Semantic Models, vector-space models, corpus-based semantics, se- mantic proximity, semantic relations, evaluation of distributional ressources. MOTS-CLÉS : Sémantique distributionnelle, modèles vectoriels, proximité sémantique, relations sémantiques, évaluation de ressources distributionnelles. -
Distributional Semantics
Distributional semantics Distributional semantics is a research area that devel- by populating the vectors with information on which text ops and studies theories and methods for quantifying regions the linguistic items occur in; paradigmatic sim- and categorizing semantic similarities between linguis- ilarities can be extracted by populating the vectors with tic items based on their distributional properties in large information on which other linguistic items the items co- samples of language data. The basic idea of distributional occur with. Note that the latter type of vectors can also semantics can be summed up in the so-called Distribu- be used to extract syntagmatic similarities by looking at tional hypothesis: linguistic items with similar distributions the individual vector components. have similar meanings. The basic idea of a correlation between distributional and semantic similarity can be operationalized in many dif- ferent ways. There is a rich variety of computational 1 Distributional hypothesis models implementing distributional semantics, includ- ing latent semantic analysis (LSA),[8] Hyperspace Ana- The distributional hypothesis in linguistics is derived logue to Language (HAL), syntax- or dependency-based from the semantic theory of language usage, i.e. words models,[9] random indexing, semantic folding[10] and var- that are used and occur in the same contexts tend to ious variants of the topic model. [1] purport similar meanings. The underlying idea that “a Distributional semantic models differ primarily with re- word is characterized by the company it keeps” was pop- spect to the following parameters: ularized by Firth.[2] The Distributional Hypothesis is the basis for statistical semantics. Although the Distribu- • tional Hypothesis originated in linguistics,[3] it is now re- Context type (text regions vs. -
A Vector Space for Distributional Semantics for Entailment ∗
A Vector Space for Distributional Semantics for Entailment ∗ James Henderson and Diana Nicoleta Popa Xerox Research Centre Europe [email protected] and [email protected] unk f g f Abstract ⇒ ¬ unk 1 0 0 0 Distributional semantics creates vector- f 1 1 0 0 space representations that capture many g 1 0 1 0 forms of semantic similarity, but their re- f 1 0 0 1 ¬ lation to semantic entailment has been less clear. We propose a vector-space model Table 1: Pattern of logical entailment between which provides a formal foundation for a nothing known (unk), two different features f and g known, and the complement of f ( f) known. distributional semantics of entailment. Us- ¬ ing a mean-field approximation, we de- velop approximate inference procedures ness with a distributional-semantic model of hy- and entailment operators over vectors of ponymy detection. probabilities of features being known (ver- Unlike previous vector-space models of entail- sus unknown). We use this framework ment, the proposed framework explicitly models to reinterpret an existing distributional- what information is unknown. This is a crucial semantic model (Word2Vec) as approxi- property, because entailment reflects what infor- mating an entailment-based model of the mation is and is not known; a representation y en- distributions of words in contexts, thereby tails a representation x if and only if everything predicting lexical entailment relations. In that is known given x is also known given y. Thus, both unsupervised and semi-supervised we model entailment in a vector space where each experiments on hyponymy detection, we dimension represents something we might know. -
A Hybrid Distributional and Knowledge-Based Model of Lexical Semantics
A Hybrid Distributional and Knowledge-based Model of Lexical Semantics Nikolaos Aletras Mark Stevenson Department of Computer Science, Department of Computer Science, University College London, University of Sheffield, Gower Street, Regent Court, 211 Portobello, London WC1E 6BT Sheffield S1 4DP United Kingdom United Kingdom [email protected] [email protected] Abstract which makes it straightforward to identify ones that are ambiguous. For example, these resources would A range of approaches to the representa- include multiple meanings for the word ball includ- tion of lexical semantics have been explored ing the ‘event’ and ‘sports equipment’ senses. How- within Computational Linguistics. Two of the ever, the fact that there are multiple meanings as- most popular are distributional and knowledge- sociated with ambiguous lexical items can also be based models. This paper proposes hybrid models of lexical semantics that combine the problematic since it may not be straightforward to advantages of these two approaches. Our mod- identify which one is being used for an instance of an els provide robust representations of synony- ambiguous word in text. This issue has lead to signif- mous words derived from WordNet. We also icant exploration of the problem of Word Sense Dis- make use of WordNet’s hierarcy to refine the ambiguation (Ide and Veronis,´ 1998; Navigli, 2009). synset vectors. The models are evaluated on More recently distributional semantics has become two widely explored tasks involving lexical semantics: lexical similarity and Word Sense a popular approach to representing lexical semantics Disambiguation. The hybrid models are found (Turney and Pantel, 2010; Erk, 2012). These ap- to perform better than standard distributional proaches are based on the premise that the semantics models and have the additional benefit of mod- of lexical items can be modelled by their context elling polysemy. -
Person Features: Deriving the Inventory of Persons
Chapter 2 Person Features: Deriving the Inventory of Persons 1. Introduction One long-standing aim of research into person has been to achieve a deeper understanding of the inventory of personal pronouns by decomposing them into a limited set of person features. The main aim of this chapter is to make a contribution to this enterprise. We will argue that a set of two privative person features is sufficient to derive the full inventory of attested pronouns and their interpretations, without generating non-attested pronouns. In chapter 1 we showed that the cross-linguistic inventory of attested persons is a small subset of the set of theoretically possible persons. The theoretically possible persons are those that can be formed by freely combining the speaker (for which we use the symbol i), associates of the speaker (ai), the addressee (u), associates of the addressee (au), and others (o). The generalizations that distinguish between the attested and non-attested persons are the following (repeated from (9) in chapter 1): (1) a. There is no person that contains associates of a participant but not the participant itself. b. There is no person that consists of others and only speaker(s) or of others and only addressee(s). c. No person system distinguishes pluralities containing a participant but not its associates from pluralities containing that participant as well as its associates. d. First and second person plural pronouns show an ‘associative effect’: any element contained in them other than i or u must be ai or au and cannot be o. 31 This leaves us with three persons with a singular interpretation and four persons with a plural interpretation.