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Books on the topic 'Linguistic Extraction'

Author: Grafiati

Published: 4 June 2021

Last updated: 14 February 2022

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1

Three investigations of extraction. MIT Press, 1998.

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2

Xu, Fei-Yu. Bootstrapping relation extraction from semantic seeds. German Research Center for Artificial Intelligence, 2008.

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3

Multiple Wh-fronting. John Benjamins Pub., 2003.

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4

Peter, Jackson. Natural language processing for online applications: Text retrieval, extraction and categorization. 2nd ed. John Benjamins Pub. Company, 2007.

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5

Müller, Sonja. (Un)informativität und Grammatik: Extraktion aus Nebensätzen im Deutschen. Stauffenburg, 2011.

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6

Paliouras, Georgios. Knowledge-Driven Multimedia Information Extraction and Ontology Evolution: Bridging the Semantic Gap. Springer Berlin Heidelberg, 2011.

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7

Movement in language: Interactions and architectures. Oxford University Press, 2001.

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8

Sabourin, Conrad. Optical character recognition and document segmentation: Character preprocessing, thinning, isolation, segmentation, feature extraction, cursive and multi-font recognition, writer/scriptor identification : bibliography. Infolingua, 1994.

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9

The in-situ approach to sluicing. John Benjamins Publishing Company, 2015.

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10

Sabourin, Conrad. Computational linguistics in information science: Information retrieval (full-text or conceptual), automatic indexing, text abstraction, content analysis, information extraction, query languages : bibliography. Infolingua, 1994.

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11

Robert, Levine. The unity of unbounded dependency constructions. CSLI Publications, 2006.

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12

Robert, Levine. The unity of unbounded dependency constructions. Center for the Study of Language and Information, 2004.

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13

SCIE-97, (1997 Frascati Italy). Information extraction: A multidisciplinary approach to an emerging information technology : international summer school, SCIE-97, Frascati, Italy, July 14-18, 1997. Springer, 1997.

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14

Merchant, Jason. The syntax of silence: Sluicing, islands, and the theory of ellipsis. Oxford University Press, 2001.

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15

Grishman, Ralph. Information Extraction. Edited by Ruslan Mitkov. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199276349.013.0030.

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Information extraction (IE) is the automatic identification of selected types of entities, relations, or events in free text. This article appraises two specific strands of IE — name identification and classification, and event extraction. Conventional treatment of languages pays little attention to proper names, addresses etc. Presentations of language analysis generally look up words in a dictionary and identify them as nouns etc. The incessant presence of names in a text, makes linguistic analysis of the same difficult, in the absence of the names being identified by their types and as ling

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16

Saugera, Valérie. Methodology. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190625542.003.0002.

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This chapter presents and justifies the use of both a dictionary corpus and a newspaper corpus. The dictionary corpus is used because of the role of the dictionary as linguistic authority in France, and the stable status of the Anglicisms included in it. For the newspaper data, the study benefited from the French text-mining tool named Sulci, originally designed for the corpus and thesaurus analysis of the daily newspaper Libération, which allowed extraction of all the dictionary-unattested forms in one year’s issues. In fact, this study is the first to use an electronic corpus to analyze the

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17

Cedric, Boeckx, and Grohmann Kleanthes K, eds. Multiple Wh-fronting. John Benjamins Pub., 2003.

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18

Multiple Wh-fronting. Benjamins, 2001.

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19

Matsumoto, Yuji. Lexical Knowledge Acquisition. Edited by Ruslan Mitkov. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199276349.013.0021.

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This article deals with the acquisition of lexical knowledge, instrumental in complementing the ambiguous process of NLP (natural language processing). Imprecise in nature, lexical representations are mostly simple and superficial. The thesaurus would be an apt example. Two primary tools for acquiring lexical knowledge are ‘corpora’ and ‘machine-readable dictionary’ (MRD). The former are mostly domain specific, monolingual, while the definitions in MRD are generally described by a ‘genus term’ followed by a set of differentiae. Auxiliary technical nuances of the acquisition process, find menti

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20

SyntaxBased Collocation Extraction Text Speech and Language Technology. Springer, 2010.

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21

Jackson, Peter, and Isabelle Moulinier. Natural Language Processing for Online Applications: Text Retrieval, Extraction and Categorization (Natural Language Processing). 2nd ed. John Benjamins Pub Co, 2007.

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22

Recycling Translations: Extraction of Lexical Data from Parallel Corpora & Their Application in Natural Language Processing (Studia Linguistica Upsaliensia, 1). Uppsala Universitet, 2003.

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23

Mori, Renato De, and Gokhan Tur. Spoken Language Understanding: Systems for Extracting Semantic Information from Speech. Wiley & Sons, Incorporated, John, 2011.

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24

Mori, Renato De, and Gokhan Tur. Spoken Language Understanding: Systems for Extracting Semantic Information from Speech. Wiley & Sons, Incorporated, John, 2011.

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25

Spoken Language Understanding: Systems for Extracting Semantic Information from Speech. Wiley, 2011.

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26

Mori, Renato De, and Gokhan Tur. Spoken Language Understanding: Systems for Extracting Semantic Information from Speech. Wiley & Sons, Incorporated, John, 2011.

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27

Mori, Renato De, and Gokhan Tur. Spoken Language Understanding: Systems for Extracting Semantic Information from Speech. Wiley & Sons, Incorporated, John, 2011.

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28

Natural Language Processing for Online Applications: Text retrieval, extraction and categorization - Second revised edition (Natural Language Processing). 2nd ed. John Benjamins Publishing Co, 2007.

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29

Mooney, Raymond J. Machine Learning. Edited by Ruslan Mitkov. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199276349.013.0020.

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This article introduces the type of symbolic machine learning in which decision trees, rules, or case-based classifiers are induced from supervised training examples. It describes the representation of knowledge assumed by each of these approaches and reviews basic algorithms for inducing such representations from annotated training examples and using the acquired knowledge to classify future instances. Machine learning is the study of computational systems that improve performance on some task with experience. Most machine learning methods concern the task of categorizing examples described b

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30

Longenbaugh, Nicholas, and Maria Polinsky. Experimental Approaches to Ergative Languages. Edited by Jessica Coon, Diane Massam, and Lisa Demena Travis. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780198739371.013.29.

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This chapter summarizes major results in the domain of experimental approaches to ergativity, focusing on three major topics. First, it discusses studies that explore the competition between accusative and ergative alignment, where researchers have attempted to derive the typological preference for accusative alignment from processing- and learnability based constraints. Next, it examines studies concerning the interrelated issues of long-distance dependencies and agreement. The unique dissociation of case and argument-hood in ergative languages has afforded researchers new means of testing co

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31

Teresa, Pazienza Maria, and International Summer School on Information Extraction (1997 : Frascati, Italy), eds. Information extraction: A multidisciplinary approach to an emerging information technology : International Summer School, SCIE-97, Frascati, Italy July 14-18, 1997. Springer, 1997.

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