Automatically Acquiring a Semantic Network of Related Concepts
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Automatic Wordnet Mapping Using Word Sense Disambiguation*
Automatic WordNet mapping using word sense disambiguation* Changki Lee Seo JungYun Geunbae Leer Natural Language Processing Lab Natural Language Processing Lab Dept. of Computer Science and Engineering Dept. of Computer Science Pohang University of Science & Technology Sogang University San 31, Hyoja-Dong, Pohang, 790-784, Korea Sinsu-dong 1, Mapo-gu, Seoul, Korea {leeck,gblee }@postech.ac.kr seojy@ ccs.sogang.ac.kr bilingual Korean-English dictionary. The first Abstract sense of 'gwan-mog' has 'bush' as a translation in English and 'bush' has five synsets in This paper presents the automatic WordNet. Therefore the first sense of construction of a Korean WordNet from 'gwan-mog" has five candidate synsets. pre-existing lexical resources. A set of Somehow we decide a synset {shrub, bush} automatic WSD techniques is described for among five candidate synsets and link the sense linking Korean words collected from a of 'gwan-mog' to this synset. bilingual MRD to English WordNet synsets. As seen from this example, when we link the We will show how individual linking senses of Korean words to WordNet synsets, provided by each WSD method is then there are semantic ambiguities. To remove the combined to produce a Korean WordNet for ambiguities we develop new word sense nouns. disambiguation heuristics and automatic mapping method to construct Korean WordNet based on 1 Introduction the existing English WordNet. There is no doubt on the increasing This paper is organized as follows. In section 2, importance of using wide coverage ontologies we describe multiple heuristics for word sense for NLP tasks especially for information disambiguation for sense linking. -
Probabilistic Topic Modelling with Semantic Graph
Probabilistic Topic Modelling with Semantic Graph B Long Chen( ), Joemon M. Jose, Haitao Yu, Fajie Yuan, and Huaizhi Zhang School of Computing Science, University of Glasgow, Sir Alwyns Building, Glasgow, UK [email protected] Abstract. In this paper we propose a novel framework, topic model with semantic graph (TMSG), which couples topic model with the rich knowledge from DBpedia. To begin with, we extract the disambiguated entities from the document collection using a document entity linking system, i.e., DBpedia Spotlight, from which two types of entity graphs are created from DBpedia to capture local and global contextual knowl- edge, respectively. Given the semantic graph representation of the docu- ments, we propagate the inherent topic-document distribution with the disambiguated entities of the semantic graphs. Experiments conducted on two real-world datasets show that TMSG can significantly outperform the state-of-the-art techniques, namely, author-topic Model (ATM) and topic model with biased propagation (TMBP). Keywords: Topic model · Semantic graph · DBpedia 1 Introduction Topic models, such as Probabilistic Latent Semantic Analysis (PLSA) [7]and Latent Dirichlet Analysis (LDA) [2], have been remarkably successful in ana- lyzing textual content. Specifically, each document in a document collection is represented as random mixtures over latent topics, where each topic is character- ized by a distribution over words. Such a paradigm is widely applied in various areas of text mining. In view of the fact that the information used by these mod- els are limited to document collection itself, some recent progress have been made on incorporating external resources, such as time [8], geographic location [12], and authorship [15], into topic models. -
Semantic Memory: a Review of Methods, Models, and Current Challenges
Psychonomic Bulletin & Review https://doi.org/10.3758/s13423-020-01792-x Semantic memory: A review of methods, models, and current challenges Abhilasha A. Kumar1 # The Psychonomic Society, Inc. 2020 Abstract Adult semantic memory has been traditionally conceptualized as a relatively static memory system that consists of knowledge about the world, concepts, and symbols. Considerable work in the past few decades has challenged this static view of semantic memory, and instead proposed a more fluid and flexible system that is sensitive to context, task demands, and perceptual and sensorimotor information from the environment. This paper (1) reviews traditional and modern computational models of seman- tic memory, within the umbrella of network (free association-based), feature (property generation norms-based), and distribu- tional semantic (natural language corpora-based) models, (2) discusses the contribution of these models to important debates in the literature regarding knowledge representation (localist vs. distributed representations) and learning (error-free/Hebbian learning vs. error-driven/predictive learning), and (3) evaluates how modern computational models (neural network, retrieval- based, and topic models) are revisiting the traditional “static” conceptualization of semantic memory and tackling important challenges in semantic modeling such as addressing temporal, contextual, and attentional influences, as well as incorporating grounding and compositionality into semantic representations. The review also identifies new challenges -
Knowledge Graphs on the Web – an Overview Arxiv:2003.00719V3 [Cs
January 2020 Knowledge Graphs on the Web – an Overview Nicolas HEIST, Sven HERTLING, Daniel RINGLER, and Heiko PAULHEIM Data and Web Science Group, University of Mannheim, Germany Abstract. Knowledge Graphs are an emerging form of knowledge representation. While Google coined the term Knowledge Graph first and promoted it as a means to improve their search results, they are used in many applications today. In a knowl- edge graph, entities in the real world and/or a business domain (e.g., people, places, or events) are represented as nodes, which are connected by edges representing the relations between those entities. While companies such as Google, Microsoft, and Facebook have their own, non-public knowledge graphs, there is also a larger body of publicly available knowledge graphs, such as DBpedia or Wikidata. In this chap- ter, we provide an overview and comparison of those publicly available knowledge graphs, and give insights into their contents, size, coverage, and overlap. Keywords. Knowledge Graph, Linked Data, Semantic Web, Profiling 1. Introduction Knowledge Graphs are increasingly used as means to represent knowledge. Due to their versatile means of representation, they can be used to integrate different heterogeneous data sources, both within as well as across organizations. [8,9] Besides such domain-specific knowledge graphs which are typically developed for specific domains and/or use cases, there are also public, cross-domain knowledge graphs encoding common knowledge, such as DBpedia, Wikidata, or YAGO. [33] Such knowl- edge graphs may be used, e.g., for automatically enriching data with background knowl- arXiv:2003.00719v3 [cs.AI] 12 Mar 2020 edge to be used in knowledge-intensive downstream applications. -
Large Semantic Network Manual Annotation 1 Introduction
Large Semantic Network Manual Annotation V´aclav Nov´ak Institute of Formal and Applied Linguistics Charles University, Prague [email protected] Abstract This abstract describes a project aiming at manual annotation of the content of natural language utterances in a parallel text corpora. The formalism used in this project is MultiNet – Multilayered Ex- tended Semantic Network. The annotation should be incorporated into Prague Dependency Treebank as a new annotation layer. 1 Introduction A formal specification of the semantic content is the aim of numerous semantic approaches such as TIL [6], DRT [9], MultiNet [4], and others. As far as we can tell, there is no large “real life” text corpora manually annotated with such markup. The projects usually work only with automatically generated annotation, if any [1, 6, 3, 2]. We want to create a parallel Czech-English corpora of texts annotated with the corresponding semantic network. 1.1 Prague Dependency Treebank From the linguistic viewpoint there language resources such as Prague Dependency Treebank (PDT) which contain a deep manual analysis of texts [8]. PDT contains annotations of three layers, namely morpho- logical, analytical (shallow dependency syntax) and tectogrammatical (deep dependency syntax). The units of each annotation level are linked with corresponding units on the preceding level. The morpho- logical units are linked directly with the original text. The theoretical basis of the treebank lies in the Functional Gener- ative Description of language system [7]. PDT 2.0 is based on the long-standing Praguian linguistic tradi- tion, adapted for the current computational-linguistics research needs. The corpus itself is embedded into the latest annotation technology. -
Universal Or Variation? Semantic Networks in English and Chinese
Universal or variation? Semantic networks in English and Chinese Understanding the structures of semantic networks can provide great insights into lexico- semantic knowledge representation. Previous work reveals small-world structure in English, the structure that has the following properties: short average path lengths between words and strong local clustering, with a scale-free distribution in which most nodes have few connections while a small number of nodes have many connections1. However, it is not clear whether such semantic network properties hold across human languages. In this study, we investigate the universal structures and cross-linguistic variations by comparing the semantic networks in English and Chinese. Network description To construct the Chinese and the English semantic networks, we used Chinese Open Wordnet2,3 and English WordNet4. The two wordnets have different word forms in Chinese and English but common word meanings. Word meanings are connected not only to word forms, but also to other word meanings if they form relations such as hypernyms and meronyms (Figure 1). 1. Cross-linguistic comparisons Analysis The large-scale structures of the Chinese and the English networks were measured with two key network metrics, small-worldness5 and scale-free distribution6. Results The two networks have similar size and both exhibit small-worldness (Table 1). However, the small-worldness is much greater in the Chinese network (σ = 213.35) than in the English network (σ = 83.15); this difference is primarily due to the higher average clustering coefficient (ACC) of the Chinese network. The scale-free distributions are similar across the two networks, as indicated by ANCOVA, F (1, 48) = 0.84, p = .37. -
Structure at Every Scale: a Semantic Network Account of the Similarities Between Very Unrelated Concepts
De Deyne, S., Navarro D. J., Perfors, A. and Storms, G. (2016). Structure at every scale: A semantic network account of the similarities between very unrelated concepts. Journal of Experimental Psychology: General, 145, 1228-1254 https://doi.org/10.1037/xge0000192 Structure at every scale: A semantic network account of the similarities between unrelated concepts Simon De Deyne, Danielle J. Navarro, Amy Perfors University of Adelaide Gert Storms University of Leuven Word Count: 19586 Abstract Similarity plays an important role in organizing the semantic system. However, given that similarity cannot be defined on purely logical grounds, it is impor- tant to understand how people perceive similarities between different entities. Despite this, the vast majority of studies focus on measuring similarity between very closely related items. When considering concepts that are very weakly re- lated, little is known. In this paper we present four experiments showing that there are reliable and systematic patterns in how people evaluate the similari- ties between very dissimilar entities. We present a semantic network account of these similarities showing that a spreading activation mechanism defined over a word association network naturally makes correct predictions about weak sim- ilarities and the time taken to assess them, whereas, though simpler, models based on direct neighbors between word pairs derived using the same network cannot. Keywords: word associations, similarity, semantic networks, random walks. This work was supported by a research grant funded by the Research Foundation - Flanders (FWO), ARC grant DE140101749 awarded to the first author, and by the interdisciplinary research project IDO/07/002 awarded to Dirk Speelman, Dirk Geeraerts, and Gert Storms. -
March 21–25, 2016
FORTY-SEVENTH LUNAR AND PLANETARY SCIENCE CONFERENCE PROGRAM OF TECHNICAL SESSIONS MARCH 21–25, 2016 The Woodlands Waterway Marriott Hotel and Convention Center The Woodlands, Texas INSTITUTIONAL SUPPORT Universities Space Research Association Lunar and Planetary Institute National Aeronautics and Space Administration CONFERENCE CO-CHAIRS Stephen Mackwell, Lunar and Planetary Institute Eileen Stansbery, NASA Johnson Space Center PROGRAM COMMITTEE CHAIRS David Draper, NASA Johnson Space Center Walter Kiefer, Lunar and Planetary Institute PROGRAM COMMITTEE P. Doug Archer, NASA Johnson Space Center Nicolas LeCorvec, Lunar and Planetary Institute Katherine Bermingham, University of Maryland Yo Matsubara, Smithsonian Institute Janice Bishop, SETI and NASA Ames Research Center Francis McCubbin, NASA Johnson Space Center Jeremy Boyce, University of California, Los Angeles Andrew Needham, Carnegie Institution of Washington Lisa Danielson, NASA Johnson Space Center Lan-Anh Nguyen, NASA Johnson Space Center Deepak Dhingra, University of Idaho Paul Niles, NASA Johnson Space Center Stephen Elardo, Carnegie Institution of Washington Dorothy Oehler, NASA Johnson Space Center Marc Fries, NASA Johnson Space Center D. Alex Patthoff, Jet Propulsion Laboratory Cyrena Goodrich, Lunar and Planetary Institute Elizabeth Rampe, Aerodyne Industries, Jacobs JETS at John Gruener, NASA Johnson Space Center NASA Johnson Space Center Justin Hagerty, U.S. Geological Survey Carol Raymond, Jet Propulsion Laboratory Lindsay Hays, Jet Propulsion Laboratory Paul Schenk, -
Untangling Semantic Similarity: Modeling Lexical Processing Experiments with Distributional Semantic Models
Untangling Semantic Similarity: Modeling Lexical Processing Experiments with Distributional Semantic Models. Farhan Samir Barend Beekhuizen Suzanne Stevenson Department of Computer Science Department of Language Studies Department of Computer Science University of Toronto University of Toronto, Mississauga University of Toronto ([email protected]) ([email protected]) ([email protected]) Abstract DSMs thought to instantiate one but not the other kind of sim- Distributional semantic models (DSMs) are substantially var- ilarity have been found to explain different priming effects on ied in the types of semantic similarity that they output. Despite an aggregate level (as opposed to an item level). this high variance, the different types of similarity are often The underlying conception of semantic versus associative conflated as a monolithic concept in models of behavioural data. We apply the insight that word2vec’s representations similarity, however, has been disputed (Ettinger & Linzen, can be used for capturing both paradigmatic similarity (sub- 2016; McRae et al., 2012), as has one of the main ways in stitutability) and syntagmatic similarity (co-occurrence) to two which it is operationalized (Gunther¨ et al., 2016). In this pa- sets of experimental findings (semantic priming and the effect of semantic neighbourhood density) that have previously been per, we instead follow another distinction, based on distri- modeled with monolithic conceptions of DSM-based seman- butional properties (e.g. Schutze¨ & Pedersen, 1993), namely, tic similarity. Using paradigmatic and syntagmatic similarity that between syntagmatically related words (words that oc- based on word2vec, we show that for some tasks and types of items the two types of similarity play complementary ex- cur in each other’s near proximity, such as drink–coffee), planatory roles, whereas for others, only syntagmatic similar- and paradigmatically related words (words that can be sub- ity seems to matter. -
NL Assistant: a Toolkit for Developing Natural Language: Applications
NL Assistant: A Toolkit for Developing Natural Language Applications Deborah A. Dahl, Lewis M. Norton, Ahmed Bouzid, and Li Li Unisys Corporation Introduction scale lexical resources have been integrated with the toolkit. These include Comlex and WordNet We will be demonstrating a toolkit for as well as additional, internally developed developing natural language-based applications resources. The second strategy is to provide easy and two applications. The goals of this toolkit to use editors for entering linguistic information. are to reduce development time and cost for natural language based applications by reducing Servers the amount of linguistic and programming work needed. Linguistic work has been reduced by Lexical information is supplied by four external integrating large-scale linguistics resources--- servers which are accessed by the natural Comlex (Grishman, et. al., 1993) and WordNet language engine during processing. Syntactic (Miller, 1990). Programming work is reduced by information is supplied by a lexical server based automating some of the programming tasks. The on the 50K word Comlex dictionary available toolkit is designed for both speech- and text- from the Linguistic Data Consortium. Semantic based interface applications. It runs in a information comes from two servers, a KB Windows NT environment. Applications can server based on the noun portion of WordNet run in either Windows NT or Unix. (70K concepts), and a semantics server containing case frame information for 2500 System Components English verbs. A denotations server using unique concept names generated for each The NL Assistant toolkit consists of WordNet synset at ISI links the words in the lexicon to the concepts in the KB. -
Similarity Detection Using Latent Semantic Analysis Algorithm Priyanka R
International Journal of Emerging Research in Management &Technology Research Article August ISSN: 2278-9359 (Volume-6, Issue-8) 2017 Similarity Detection Using Latent Semantic Analysis Algorithm Priyanka R. Patil Shital A. Patil PG Student, Department of Computer Engineering, Associate Professor, Department of Computer Engineering, North Maharashtra University, Jalgaon, North Maharashtra University, Jalgaon, Maharashtra, India Maharashtra, India Abstract— imilarity View is an application for visually comparing and exploring multiple models of text and collection of document. Friendbook finds ways of life of clients from client driven sensor information, measures the S closeness of ways of life amongst clients, and prescribes companions to clients if their ways of life have high likeness. Roused by demonstrate a clients day by day life as life records, from their ways of life are separated by utilizing the Latent Dirichlet Allocation Algorithm. Manual techniques can't be utilized for checking research papers, as the doled out commentator may have lacking learning in the exploration disciplines. For different subjective views, causing possible misinterpretations. An urgent need for an effective and feasible approach to check the submitted research papers with support of automated software. A method like text mining method come to solve the problem of automatically checking the research papers semantically. The proposed method to finding the proper similarity of text from the collection of documents by using Latent Dirichlet Allocation (LDA) algorithm and Latent Semantic Analysis (LSA) with synonym algorithm which is used to find synonyms of text index wise by using the English wordnet dictionary, another algorithm is LSA without synonym used to find the similarity of text based on index. -
Introduction to Wordnet: an On-Line Lexical Database
Introduction to WordNet: An On-line Lexical Database George A. Miller, Richard Beckwith, Christiane Fellbaum, Derek Gross, and Katherine Miller (Revised August 1993) WordNet is an on-line lexical reference system whose design is inspired by current psycholinguistic theories of human lexical memory. English nouns, verbs, and adjectives are organized into synonym sets, each representing one underlying lexical concept. Different relations link the synonym sets. Standard alphabetical procedures for organizing lexical information put together words that are spelled alike and scatter words with similar or related meanings haphazardly through the list. Unfortunately, there is no obvious alternative, no other simple way for lexicographers to keep track of what has been done or for readers to ®nd the word they are looking for. But a frequent objection to this solution is that ®nding things on an alphabetical list can be tedious and time-consuming. Many people who would like to refer to a dictionary decide not to bother with it because ®nding the information would interrupt their work and break their train of thought. In this age of computers, however, there is an answer to that complaint. One obvious reason to resort to on-line dictionariesÐlexical databases that can be read by computersÐis that computers can search such alphabetical lists much faster than people can. A dictionary entry can be available as soon as the target word is selected or typed into the keyboard. Moreover, since dictionaries are printed from tapes that are read by computers, it is a relatively simple matter to convert those tapes into the appropriate kind of lexical database.