Bilingual Indexing for Information Retrieval with AUTINDEX
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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. -
Enhanced Thesaurus Terms Extraction for Document Indexing
Enhanced Thesaurus Terms Extraction for Document Indexing Frane ari¢, Jan najder, Bojana Dalbelo Ba²i¢, Hrvoje Ekli¢ Faculty of Electrical Engineering and Computing, University of Zagreb Unska 3, 10000 Zagreb, Croatia E-mail:{Frane.Saric, Jan.Snajder, Bojana.Dalbelo, Hrvoje.Eklic}@fer.hr Abstract. In this paper we present an mogeneous due to diverse background knowl- enhanced method for the thesaurus term edge and expertise of human indexers. The extraction regarded as the main support to task of building semi-automatic and auto- a semi-automatic indexing system. The matic systems, which aim to decrease the enhancement is achieved by neutralising burden of work borne by indexers, has re- the eect of language morphology applying cently attracted interest in the research com- lemmatisation on both the text and the munity [4], [13], [14]. Automatic indexing thesaurus, and by implementing an ecient systems still do not achieve the performance recursive algorithm for term extraction. of human indexers, so semi-automatic sys- Formal denition and statistical evaluation tems are widely used (CINDEX, MACREX, of the experimental results of the proposed MAI [10]). method for thesaurus term extraction are In this paper we present a method for the- given. The need for disambiguation methods saurus term extraction regarded as the main and the eect of lemmatisation in the realm support to semi-automatic indexing system. of thesaurus term extraction are discussed. Term extraction is a process of nding all ver- batim occurrences of all terms in the text. Keywords. Information retrieval, term Our method of term extraction is a part of extraction, NLP, lemmatisation, Eurovoc. -
An Evaluation of Machine Learning Approaches to Natural Language Processing for Legal Text Classification
Imperial College London Department of Computing An Evaluation of Machine Learning Approaches to Natural Language Processing for Legal Text Classification Supervisors: Author: Prof Alessandra Russo Clavance Lim Nuri Cingillioglu Submitted in partial fulfillment of the requirements for the MSc degree in Computing Science of Imperial College London September 2019 Contents Abstract 1 Acknowledgements 2 1 Introduction 3 1.1 Motivation .................................. 3 1.2 Aims and objectives ............................ 4 1.3 Outline .................................... 5 2 Background 6 2.1 Overview ................................... 6 2.1.1 Text classification .......................... 6 2.1.2 Training, validation and test sets ................. 6 2.1.3 Cross validation ........................... 7 2.1.4 Hyperparameter optimization ................... 8 2.1.5 Evaluation metrics ......................... 9 2.2 Text classification pipeline ......................... 14 2.3 Feature extraction ............................. 15 2.3.1 Count vectorizer .......................... 15 2.3.2 TF-IDF vectorizer ......................... 16 2.3.3 Word embeddings .......................... 17 2.4 Classifiers .................................. 18 2.4.1 Naive Bayes classifier ........................ 18 2.4.2 Decision tree ............................ 20 2.4.3 Random forest ........................... 21 2.4.4 Logistic regression ......................... 21 2.4.5 Support vector machines ...................... 22 2.4.6 k-Nearest Neighbours ....................... -
Using Lexico-Syntactic Ontology Design Patterns for Ontology Creation and Population
Using Lexico-Syntactic Ontology Design Patterns for ontology creation and population Diana Maynard and Adam Funk and Wim Peters Department of Computer Science University of Sheffield Regent Court, 211 Portobello S1 4DP, Sheffield, UK Abstract. In this paper we discuss the use of information extraction techniques involving lexico-syntactic patterns to generate ontological in- formation from unstructured text and either create a new ontology from scratch or augment an existing ontology with new entities. We refine the patterns using a term extraction tool and some semantic restrictions derived from WordNet and VerbNet, in order to prevent the overgener- ation that occurs with the use of the Ontology Design Patterns for this purpose. We present two applications developed in GATE and available as plugins for the NeOn Toolkit: one for general use on all kinds of text, and one for specific use in the fisheries domain. Key words: natural language processing, relation extraction, ontology generation, information extraction, Ontology Design Patterns 1 Introduction Ontology population is a crucial part of knowledge base construction and main- tenance that enables us to relate text to ontologies, providing on the one hand a customised ontology related to the data and domain with which we are con- cerned, and on the other hand a richer ontology which can be used for a variety of semantic web-related tasks such as knowledge management, information re- trieval, question answering, semantic desktop applications, and so on. Automatic ontology population is generally performed by means of some kind of ontology-based information extraction (OBIE) [1, 2]. This consists of identi- fying the key terms in the text (such as named entities and technical terms) and then relating them to concepts in the ontology. -
LASLA and Collatinus
L.A.S.L.A. and Collatinus: a convergence in lexica Philippe Verkerk, Yves Ouvrard, Margherita Fantoli, Dominique Longrée To cite this version: Philippe Verkerk, Yves Ouvrard, Margherita Fantoli, Dominique Longrée. L.A.S.L.A. and Collatinus: a convergence in lexica. Studi e saggi linguistici, ETS, In press. hal-02399878v1 HAL Id: hal-02399878 https://hal.archives-ouvertes.fr/hal-02399878v1 Submitted on 9 Dec 2019 (v1), last revised 14 May 2020 (v2) 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. L.A.S.L.A. and Collatinus: a convergence in lexica Philippe Verkerk, Yves Ouvrard, Margherita Fantoli and Dominique Longrée L.A.S.L.A. (Laboratoire d'Analyse Statistique des Langues Anciennes, University of Liège, Belgium) has begun in 1961 a project of lemmatisation and morphosyntactic tagging of Latin texts. This project is still running with new texts lemmatised each year. The resulting files have been recently opened to the interested scholars and they now count approximatively 2.500.000 words, the lemmatisation of which has been checked by a philologist. In the early 2.000's, Collatinus has been developed by Yves Ouvrard for teaching. -
Experiments in Clustering Urban-Legend Texts
How to Distinguish a Kidney Theft from a Death Car? Experiments in Clustering Urban-Legend Texts Roman Grundkiewicz, Filip Gralinski´ Adam Mickiewicz University Faculty of Mathematics and Computer Science Poznan,´ Poland [email protected], [email protected] Abstract is much easier to tap than the oral one, it becomes feasible to envisage a system for the machine iden- This paper discusses a system for auto- tification and collection of urban-legend texts. In matic clustering of urban-legend texts. Ur- this paper, we discuss the first steps into the cre- ban legend (UL) is a short story set in ation of such a system. We concentrate on the task the present day, believed by its tellers to of clustering of urban legends, trying to reproduce be true and spreading spontaneously from automatically the results of manual categorisation person to person. A corpus of Polish UL of urban-legend texts done by folklorists. texts was collected from message boards In Sec. 2 a corpus of 697 Polish urban-legend and blogs. Each text was manually as- texts is presented. The techniques used in pre- signed to one story type. The aim of processing the corpus texts are discussed in Sec. 3, the presented system is to reconstruct the whereas the clustering process – in Sec. 4. We manual grouping of texts. It turned out that present the clustering experiment in Sec. 5 and the automatic clustering of UL texts is feasible results – in Sec. 6. but it requires techniques different from the ones used for clustering e.g. news ar- 2 Corpus ticles. -
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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. -
Removing Boilerplate and Duplicate Content from Web Corpora
Masaryk University Faculty}w¡¢£¤¥¦§¨ of Informatics !"#$%&'()+,-./012345<yA| Removing Boilerplate and Duplicate Content from Web Corpora Ph.D. thesis Jan Pomik´alek Brno, 2011 Acknowledgments I want to thank my supervisor Karel Pala for all his support and encouragement in the past years. I thank LudˇekMatyska for a useful pre-review and also for being so kind and cor- recting typos in the text while reading it. I thank the KrdWrd team for providing their Canola corpus for my research and namely to Egon Stemle for his help and technical support. I thank Christian Kohlsch¨utterfor making the L3S-GN1 dataset publicly available and for an interesting e-mail discussion. Special thanks to my NLPlab colleagues for creating a great research environment. In particular, I want to thank Pavel Rychl´yfor inspiring discussions and for an interesting joint research. Special thanks to Adam Kilgarriff and Diana McCarthy for reading various parts of my thesis and for their valuable comments. My very special thanks go to Lexical Computing Ltd and namely again to the director Adam Kilgarriff for fully supporting my research and making it applicable in practice. Last but not least I thank my family for all their love and support throughout my studies. Abstract In the recent years, the Web has become a popular source of textual data for linguistic research. The Web provides an extremely large volume of texts in many languages. However, a number of problems have to be resolved in order to create collections (text corpora) which are appropriate for application in natural language processing. In this work, two related problems are addressed: cleaning a boilerplate and removing duplicate and near-duplicate content from Web data. -
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. -
The State of (Full) Text Search in Postgresql 12
Event / Conference name Location, Date The State of (Full) Text Search in PostgreSQL 12 FOSDEM 2020 Jimmy Angelakos Senior PostgreSQL Architect Twitter: @vyruss https://www.2ndQuadrant.com FOSDEM Brussels, 2020-02-02 Contents ● (Full) Text Search ● Indexing ● Operators ● Non-natural text ● Functions ● Collation ● Dictionaries ● Other “text” types ● Examples ● Maintenance https://www.2ndQuadrant.com FOSDEM Brussels, 2020-02-02 Your attention please Allergy advice ● This presentation contains linguistics, NLP, Markov chains, Levenshtein distances, and various other confounding terms. ● These have been known to induce drowsiness and inappropriate sleep onset in lecture theatres. https://www.2ndQuadrant.com FOSDEM Brussels, 2020-02-02 What is Text? (Baby don’t hurt me) ● PostgreSQL character types – CHAR(n) – VARCHAR(n) – VARCHAR, TEXT ● Trailing spaces: significant (e.g. for LIKE / regex) ● Storage – Character Set (e.g. UTF-8) – 1+126 bytes → 4+n bytes – Compression, TOAST https://www.2ndQuadrant.com FOSDEM Brussels, 2020-02-02 What is Text Search? ● Information retrieval → Text retrieval ● Search on metadata – Descriptive, bibliographic, tags, etc. – Discovery & identification ● Search on parts of the text – Matching – Substring search – Data extraction, cleaning, mining https://www.2ndQuadrant.com FOSDEM Brussels, 2020-02-02 Text search operators in PostgreSQL ● LIKE, ILIKE (~~, ~~*) ● ~, ~* (POSIX regex) ● regexp_match(string text, pattern text) ● But are SQL/regular expressions enough? – No ranking of results – No concept of language – Cannot be indexed ● Okay okay, can be somewhat indexed* ● SIMILAR TO → best forget about this one https://www.2ndQuadrant.com FOSDEM Brussels, 2020-02-02 What is Full Text Search (FTS)? ● Information retrieval → Text retrieval → Document retrieval ● Search on words (on tokens) in a database (all documents) ● No index → Serial search (e.g.