Part-of-speech tagging

Results: 456



#Item
51POS Tagging Problem •  Given a sentence W1…Wn and a tagset of lexical categories, find the most likely tag T1..Tn for each word in the sentence •  Example Secretariat/NNP is/VBZ expected/VBN to/TO race/VB tomor

POS Tagging Problem •  Given a sentence W1…Wn and a tagset of lexical categories, find the most likely tag T1..Tn for each word in the sentence •  Example Secretariat/NNP is/VBZ expected/VBN to/TO race/VB tomor

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Source URL: cl.indiana.edu

Language: English - Date: 2016-02-17 11:43:10
52Collaborative development of annotation guidelines with application to Universal Dependencies Sampo Pyysalo, Filip Ginter Department of Information Technology University of Turku, Finland , filip.ginter@

Collaborative development of annotation guidelines with application to Universal Dependencies Sampo Pyysalo, Filip Ginter Department of Information Technology University of Turku, Finland , filip.ginter@

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Source URL: www2.lingfil.uu.se

Language: English - Date: 2014-10-16 02:36:09
53Part-of-Speech Tagging Guidelines for the Penn Treebank Project  Beatrice Santorini March 15, 1991

Part-of-Speech Tagging Guidelines for the Penn Treebank Project Beatrice Santorini March 15, 1991

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Source URL: www.cis.uni-muenchen.de

Language: English - Date: 2013-04-10 11:16:47
54Aranea: Yet Another Family of (Comparable) Web Corpora Vladimír Benko1,2 1 Slovak Academy of Sciences, Ľ. Štúr Institute of Linguistics  Panská 26, SKBratislava, Slovakia

Aranea: Yet Another Family of (Comparable) Web Corpora Vladimír Benko1,2 1 Slovak Academy of Sciences, Ľ. Štúr Institute of Linguistics Panská 26, SKBratislava, Slovakia

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Source URL: www.tsdconference.org

Language: English - Date: 2014-09-02 15:15:07
55FUSION OF ACOUSTIC, LINGUISTIC AND PSYCHOLINGUISTIC FEATURES FOR SPEAKER PERSONALITY TRAITS RECOGNITION Firoj Alam, Giuseppe Riccardi Department of Information Engineering and Computer Science, University of Trento, Ital

FUSION OF ACOUSTIC, LINGUISTIC AND PSYCHOLINGUISTIC FEATURES FOR SPEAKER PERSONALITY TRAITS RECOGNITION Firoj Alam, Giuseppe Riccardi Department of Information Engineering and Computer Science, University of Trento, Ital

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Source URL: www.sensei-conversation.eu

Language: English - Date: 2014-08-04 05:20:11
56A corpus based morphological analyzer for unvocalized Modern Hebrew

A corpus based morphological analyzer for unvocalized Modern Hebrew

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Source URL: mt-archive.info

Language: English - Date: 2008-07-01 04:10:01
57Creating a dual-purpose treebank Eiríkur Rögnvaldsson, Anton Karl Ingason Einar Freyr Sigurðsson & Joel Wallenberg www.linguist.is/icelandic_treebank University of Iceland, University of Pennsylvania, Newcastle Univer

Creating a dual-purpose treebank Eiríkur Rögnvaldsson, Anton Karl Ingason Einar Freyr Sigurðsson & Joel Wallenberg www.linguist.is/icelandic_treebank University of Iceland, University of Pennsylvania, Newcastle Univer

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Source URL: linguist.is

Language: English - Date: 2012-01-04 07:35:06
58Pattern Dictionary of English Prepositions Ken Litkowski CL Research 9208 Gue Road Damascus, MarylandUSA

Pattern Dictionary of English Prepositions Ken Litkowski CL Research 9208 Gue Road Damascus, MarylandUSA

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Source URL: www.clres.com

Language: English - Date: 2016-06-06 12:56:43
59AMRITA@FIRE-2014: Morpheme Extraction for Tamil using Machine Learning Anand Kumar M, S Rajendran and K.P Soman Centre for Excellence in Computational Engineering and Networking Amrita School of Engineering Amrita Vishwa

AMRITA@FIRE-2014: Morpheme Extraction for Tamil using Machine Learning Anand Kumar M, S Rajendran and K.P Soman Centre for Excellence in Computational Engineering and Networking Amrita School of Engineering Amrita Vishwa

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Source URL: www.isical.ac.in

Language: English - Date: 2014-12-05 23:51:05
60Assignment 4 L445 / L545 / B659 Due Tuesday, March 1 1. Using your knowledge of n-grams, explain to a layperson what n-grams models are, what they do, what they don’t do well, and why smoothing is necessary. You should

Assignment 4 L445 / L545 / B659 Due Tuesday, March 1 1. Using your knowledge of n-grams, explain to a layperson what n-grams models are, what they do, what they don’t do well, and why smoothing is necessary. You should

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Source URL: cl.indiana.edu

Language: English - Date: 2016-02-18 19:43:03