A Resource for the Linking of Concept Lists
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The Thesaurus Delineates the Standard Terminology Used to Index And
DOCUMENT RESUME EC 070 639 AUTHOR Oldsen, Carl F.; And Others TITLr Instructional Materials Thesaurus for Special Education, Second Edition. Special Education IMC/RMC Network. INSTITUTION Special Education IMC/RMC Network, Arlington, Va. SPONS AGENCY Bureau of Education for the Handicapped (DHEW/OE), Washington, D.C. PUB DATE Jul 74 NOTE 42p. EDRS PRICE MF-$0.76 HC-$1.95 PLUS POSTAGE DESCRIPTORS Exceptional Child Education; *Handicapped Children; *Information Retrieval; *Instructional Materials; Instructional Materials Centers; National Programs; *Reference Books; *Thesauri ABSTRACT The thesaurus delineates the standard terminology used to index and retrieve instructional materials for exceptional children in the Special Education Instructional Materials Center/Regional Media Centers Network. The thesaurus is presentedin three formats: an alphabetical listing (word by word rather than, letter by letter), a rotated index, and a listing by category.The alphabetical listing of descriptors provides definitions for all terms, and scope notes which indicate the scope or boundaries of the descriptor for selected terms. Numerous cross referencesare provided. In the rotated index format, all key words excluding prepositions and articles from single and multiword formlt, each descriptor has been placed in one or more of 19 categorical groupings. (GW) b4:1 R Special Education c. Network Instructional Materials Centers -7CEIMRegional Media Centers i$1s.\ INSTRUCTIONAL THESAURUS FOR SPECIAL EpucATIo SECOND EDITION July, 1974 Printed & Distributed by the CEC Information Center on Exceptional Children The Council for Exceptional Children 1920 Association Drive Reston, -Virginia 22091 Member of the Special Education IMC /RMC Network US Office of EducationBureau of Education for the Handicapped Special Education IMC/RMC Network Instructional Materials Thesaurus for Special Education Second Edition July,1974 Thesaurus Committee Joan Miller Virginia Woods Carl F. -
Thesaurus, Thesaural Relationships, Lexical Relations, Semantic Relations, Information Storage and Retrieval
International Journal of Information Science and Management The Study of Thesaural Relationships from a Semantic Point of View J. Mehrad, Ph.D. F. Ahmadinasab, Ph.D. President of Regional Information Center Regional Information Center for Science and Technology, I. R. of Iran for Science and Technology, I. R. of Iran email: [email protected] Corresponding author: [email protected] Abstract Thesaurus is one, out of many, precious tool in information technology by which information specialists can optimize storage and retrieval of documents in scientific databases and on the web. In recent years, there has been a shift from thesaurus to ontology by downgrading thesaurus in favor of ontology. It is because thesaurus cannot meet the needs of information management because it cannot create a rich knowledge-based description of documents. It is claimed that the thesaural relationships are restricted and insufficient. The writers in this paper show that thesaural relationships are not inadequate and restricted as they are said to be but quite the opposite they cover all semantic relations and can increase the possibility of successful storage and retrieval of documents. This study shows that thesauri are semantically optimal and they cover all lexical relations; therefore, thesauri can continue as suitable tools for knowledge management. Keywords : Thesaurus, Thesaural Relationships, Lexical Relations, Semantic Relations, Information Storage and Retrieval. Introduction In the era of information explosion with the emergence of computers and internet and their important role in the storage and retrieval of information, every researcher has to do her/his scientific queries through scientific databases or the web. There are two common ways of query, one is free search which is done by keywords and the other is applying controlled vocabularies. -
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. -
Concepticon: a Resource for the Linking of Concept Lists
Concepticon: A Resource for the Linking of Concept Lists Johann-Mattis List1, Michael Cysouw2, Robert Forkel3 1CRLAO/EHESS and AIRE/UPMC, Paris, 2Forschungszentrum Deutscher Sprachatlas, Marburg, 3Max Planck Institute for the Science of Human History, Jena [email protected], [email protected], [email protected] Abstract We present an attempt to link the large amount of different concept lists which are used in the linguistic literature, ranging from Swadesh lists in historical linguistics to naming tests in clinical studies and psycholinguistics. This resource, our Concepticon, links 30 222 concept labels from 160 conceptlists to 2495 concept sets. Each concept set is given a unique identifier, a unique label, and a human-readable definition. Concept sets are further structured by defining different relations between the concepts. The resource can be used for various purposes. Serving as a rich reference for new and existing databases in diachronic and synchronic linguistics, it allows researchers a quick access to studies on semantic change, cross-linguistic polysemies, and semantic associations. Keywords: concepts, concept list, Swadesh list, naming test, word norms, cross-linguistically linked data 1. Introduction in the languages they were working on, or that they turned In 1950, Morris Swadesh (1909 – 1967) proposed the idea out to be not as stable and universal as Swadesh had claimed that certain parts of the lexicon of human languages are uni- (Matisoff, 1978; Alpher and Nash, 1999). Up to today, versal, stable over time, and rather resistant to borrowing. dozens of different concept lists have been compiled for var- As a result, he claimed that this part of the lexicon, which ious purposes. -
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. -
Detecting Personal Life Events from Social Media
Open Research Online The Open University’s repository of research publications and other research outputs Detecting Personal Life Events from Social Media Thesis How to cite: Dickinson, Thomas Kier (2019). Detecting Personal Life Events from Social Media. PhD thesis The Open University. For guidance on citations see FAQs. c 2018 The Author https://creativecommons.org/licenses/by-nc-nd/4.0/ Version: Version of Record Link(s) to article on publisher’s website: http://dx.doi.org/doi:10.21954/ou.ro.00010aa9 Copyright and Moral Rights for the articles on this site are retained by the individual authors and/or other copyright owners. For more information on Open Research Online’s data policy on reuse of materials please consult the policies page. oro.open.ac.uk Detecting Personal Life Events from Social Media a thesis presented by Thomas K. Dickinson to The Department of Science, Technology, Engineering and Mathematics in partial fulfilment of the requirements for the degree of Doctor of Philosophy in the subject of Computer Science The Open University Milton Keynes, England May 2019 Thesis advisor: Professor Harith Alani & Dr Paul Mulholland Thomas K. Dickinson Detecting Personal Life Events from Social Media Abstract Social media has become a dominating force over the past 15 years, with the rise of sites such as Facebook, Instagram, and Twitter. Some of us have been with these sites since the start, posting all about our personal lives and building up a digital identify of ourselves. But within this myriad of posts, what actually matters to us, and what do our digital identities tell people about ourselves? One way that we can start to filter through this data, is to build classifiers that can identify posts about our personal life events, allowing us to start to self reflect on what we share online. -
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. -
'Face' in Vietnamese
Body part extensions with mặt ‘face’ in Vietnamese Annika Tjuka 1 Introduction In many languages, body part terms have multiple meanings. The word tongue, for example, refers not only to the body part but also to a landscape or object feature, as in tongue of the ocean or tongue of flame. These examples illustrate that body part terms are commonly mapped to concrete objects. In addition, body part terms can be transferred to abstract domains, including the description of emotional states (e.g., Yu 2002). The present study investigates the mapping of the body part term face to the concrete domain of objects. Expressions like mouth of the river and foot of the bed reveal the different analogies for extending human body parts to object features. In the case of mouth of the river, the body part is extended on the basis of the function of the mouth as an opening. In contrast, the expression foot of the bed refers to the part of the bed where your feet are if you lie in it. Thus, the body part is mapped according to the spatial alignment of the body part and the object. Ambiguous words challenge our understanding of how the mental lexicon is structured. Two processes could lead to the activation of a specific meaning: i) all meanings of a word are stored in one lexical entry and the relevant one is retrieved, ii) only a core meaning is stored and the other senses are generated by lexical rules (for a description of both models, see, Pustejovsky 1991). In the following qualitative analysis, I concentrate on the different meanings of the body part face and discuss the implications for its representation in the mental lexicon. -
Unsupervised, Knowledge-Free, and Interpretable Word Sense Disambiguation
Unsupervised, Knowledge-Free, and Interpretable Word Sense Disambiguation Alexander Panchenkoz, Fide Martenz, Eugen Ruppertz, Stefano Faralliy, Dmitry Ustalov∗, Simone Paolo Ponzettoy, and Chris Biemannz zLanguage Technology Group, Department of Informatics, Universitat¨ Hamburg, Germany yWeb and Data Science Group, Department of Informatics, Universitat¨ Mannheim, Germany ∗Institute of Natural Sciences and Mathematics, Ural Federal University, Russia fpanchenko,marten,ruppert,[email protected] fsimone,[email protected] [email protected] Abstract manually in one of the underlying resources, such as Wikipedia. Unsupervised knowledge-free ap- Interpretability of a predictive model is proaches, e.g. (Di Marco and Navigli, 2013; Bar- a powerful feature that gains the trust of tunov et al., 2016), require no manual labor, but users in the correctness of the predictions. the resulting sense representations lack the above- In word sense disambiguation (WSD), mentioned features enabling interpretability. For knowledge-based systems tend to be much instance, systems based on sense embeddings are more interpretable than knowledge-free based on dense uninterpretable vectors. Therefore, counterparts as they rely on the wealth of the meaning of a sense can be interpreted only on manually-encoded elements representing the basis of a list of related senses. word senses, such as hypernyms, usage We present a system that brings interpretability examples, and images. We present a WSD of the knowledge-based sense representations into system that bridges the gap between these the world of unsupervised knowledge-free WSD two so far disconnected groups of meth- models. The contribution of this paper is the first ods. Namely, our system, providing access system for word sense induction and disambigua- to several state-of-the-art WSD models, tion, which is unsupervised, knowledge-free, and aims to be interpretable as a knowledge- interpretable at the same time. -
Ten Years of Babelnet: a Survey
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI-21) Survey Track Ten Years of BabelNet: A Survey Roberto Navigli1 , Michele Bevilacqua1 , Simone Conia1 , Dario Montagnini2 and Francesco Cecconi2 1Sapienza NLP Group, Sapienza University of Rome, Italy 2Babelscape, Italy froberto.navigli, michele.bevilacqua, [email protected] fmontagnini, [email protected] Abstract to integrate symbolic knowledge into neural architectures [d’Avila Garcez and Lamb, 2020]. The rationale is that the The intelligent manipulation of symbolic knowl- use of, and linkage to, symbolic knowledge can not only en- edge has been a long-sought goal of AI. How- able interpretable, explainable and accountable AI systems, ever, when it comes to Natural Language Process- but it can also increase the degree of generalization to rare ing (NLP), symbols have to be mapped to words patterns (e.g., infrequent meanings) and promote better use and phrases, which are not only ambiguous but also of information which is not explicit in the text. language-specific: multilinguality is indeed a de- Symbolic knowledge requires that the link between form sirable property for NLP systems, and one which and meaning be made explicit, connecting strings to repre- enables the generalization of tasks where multiple sentations of concepts, entities and thoughts. Historical re- languages need to be dealt with, without translat- sources such as WordNet [Miller, 1995] are important en- ing text. In this paper we survey BabelNet, a pop- deavors which systematize symbolic knowledge about the ular wide-coverage lexical-semantic knowledge re- words of a language, i.e., lexicographic knowledge, not only source obtained by merging heterogeneous sources in a machine-readable format, but also in structured form, into a unified semantic network that helps to scale thanks to the organization of concepts into a semantic net- tasks and applications to hundreds of languages. -
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Advances in Social Science, Education and Humanities Research (ASSEHR), volume 312 International Conference "Topical Problems of Philology and Didactics: Interdisciplinary Approach in Humanities and Social Sciences" (TPHD 2018) On Some Basic Methods of Analysis of Content and Structure of Lexical-Semantic Fields Alexander Zhouravlev Victoria Dobrova Department of Foreign Languages Department of Foreign Languages Samara State Technical University Samara State Technical University Samara, Russia Samara, Russia [email protected] [email protected] Lilia Nurtdinova Department of Foreign Languages Samara State Technical University Samara, Russia [email protected] Abstract—In the paper, two basic methods of analysis of the meaning of lexical units and a general structure of lexical- II. COMPONENTIAL ANALYSIS semantic fields are considered. The main subject is to understand the essence of the componential analysis method and the field A. Introduction of the method approach. The analysis of their definitions provided by various One can hardly overestimate the importance of the researchers, their main terms and notions, as well as prospects of componential analysis method for semantics research. In I. these methods are presented. The methodology of the research is Kobozeva’s opinion, a “method of componential analysis is the analysis of various types of scientific papers dealing with one of basic methods for description of lexical meaning” [1]. history, theoretical basis and practical application of the One of the reasons for this is that a meaning as a collection of componential analysis method and the field approach. The authors also present the evolution of the point of view on the role semes possesses a certain hierarchy instead of being an of these methods in the study of the meaning of lexical items, unorganized array. -
Georgia Performance Standards K-3 ELA Kindergarten 1St Grade 2Nd Grade 3Rd Grade
Georgia Performance Standards K-3 ELA Kindergarten 1st Grade 2nd Grade 3rd Grade Reading CONCEPTS OF PRINT CONCEPTS OF PRINT ELAKR1 The student demonstrates ELA1R1 The student demonstrates knowledge of concepts of print. knowledge of concepts of print. The student The student a. Recognizes that print and a. Understands that there are pictures (signs and labels, correct spellings for words. newspapers, and informational books) can inform, entertain, and b. Identifies the beginning and end persuade. of a paragraph. b. Demonstrates that print has c. Demonstrates an understanding meaning and represents spoken that punctuation and capitalization language in written form. are used in all written sentences. c. Tracks text read from left to right and top to bottom. d. Distinguishes among written letters, words, and sentences. e. Recognizes that sentences in print are made up of separate words. f. Begins to understand that punctuation and capitalization are used in all written sentences. PHONOLOGICAL AWARENESS PHONOLOGICAL AWARENESS ELAKR2 The student demonstrates ELA1R2 The student demonstrates the ability to identify and orally the ability to identify and orally manipulate words and manipulate words and individual sounds within those individual sounds within those spoken words. The student spoken words. The student a. Identifies and produces rhyming a. Isolates beginning, middle, and words in response to an oral ending sounds in single-syllable prompt and distinguishes rhyming words. and non-rhyming words. Page 1 of 11 Georgia Performance Standards K-3 ELA Kindergarten 1st Grade 2nd Grade 3rd Grade b. Identifies onsets and rhymes in b. Identifies component sounds spoken one-syllable words. (phonemes and combinations of phonemes) in spoken words.