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Simple Search
Hits 1 – 12 of 12
1
SemEval-2010 Task 17: All-words Word Sense . . .
Eneko Agirre
;
Oier Lopez De Lacalle
;
Christiane Fellbaum
...
In: http://aclweb.org/anthology-new/W/W09/W09-2420.pdf (2009)
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2
Exploring feature set combinations for WSD
Eneko Agirre
;
Oier Lopez De Lacalle
;
David Martínez
In: http://ixa.si.ehu.es/Ixa/Argitalpenak/Artikuluak/1153322678/publikoak/oiersepln_camready.final.pdf (2006)
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3
Exploring feature set combinations for WSD
Eneko Agirre
;
Oier Lopez De Lacalle
;
David Martínez
In: http://www.sepln.org/revistaSEPLN/revista/37/35.pdf (2006)
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4
Clustering wordnet word senses
Eneko Agirre
;
Oier Lopez De Lacalle
In: http://ixa.si.ehu.es/Ixa/Argitalpenak/Artikuluak/1059533082/publikoak/agirre-lopez-clustering-ranlp-final.pdf (2003)
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5
IXA NLP Group University of the Basque Country
Eneko Agirre
;
David Martínez
;
Oier López De Lacalle
...
In: http://ixa.si.ehu.es/Ixa/Argitalpenak/Artikuluak/1149260582/publikoak/emnlp.pdf
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6
EHU-ALM: Similarity-Feature Based Approach for Student Response Analysis
Itziar Aldabe
;
Montse Maritxalar
;
Oier Lopez De Lacalle
In: https://www.cs.york.ac.uk/semeval-2013/accepted/19_Paper.pdf
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7
EHU-ALM: Similarity-Feature Based Approach for Student Response Analysis
Itziar Aldabe
;
Montse Maritxalar
;
Oier Lopez De Lacalle
In: http://www.aclweb.org/anthology/S/S13/S13-2097.pdf
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8
SemEval-2010 Task 17: All-words Word Sense . . .
Eneko Agirre
;
Oier Lopez De Lacalle
;
Christiane Fellbaum
...
In: http://ixa.si.ehu.es/Ixa/Argitalpenak/Artikuluak/1283775102/publikoak/semeval/
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9
IXA NLP Group University of the Basque Country
Eneko Agirre
;
David Martínez
;
Oier López De Lacalle
...
In: http://www.aclweb.org/anthology-new/W/W06/W06-1669.pdf
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10
On the Use of Automatically Acquired Examples for All-Nouns Word Sense Disambiguation
David Martinez
;
Oier Lopez De Lacalle
;
Eneko Agirre
In: http://www.jair.org/media/2395/live-2395-4056-jair.pdf
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11
Publicly available topic signatures for all WordNet nominal senses
Eneko Agirre
;
Oier Lopez De Lacalle
In: http://ixa.si.ehu.es/Ixa/Argitalpenak/Artikuluak/1078831291/publikoak/topic_signatures_lrec.ps
Abstract:
Topic signatures are context vectors built for word senses and concepts. They can be automatically acquired from the web for any concept hierarchy using the "monosemous relative" method. Topic signatures have been shown to be useful in Word Sense Disambiguation, for modeling similarity between word senses, classifying new terms in hierarchies and also building hierarchical clusters of word senses for a given word. In this work we present a publicly available resource which comprises both automatically extracted examples for all WordNet 1.6 noun senses and topic signatures built based on those examples. We gathered around 700 sentences per each noun in WordNet. When the monosemous relatives are used to build a sense corpus for polysemous words, they comprise an average of around 3,500 sentences per word sense. The size of the topic signatures thus constructed is of around 4,500 words per word sense.
URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.6.4229
http://ixa.si.ehu.es/Ixa/Argitalpenak/Artikuluak/1078831291/publikoak/topic_signatures_lrec.ps
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12
IXA NLP Group
Eneko Agirre
;
Oier Lopez De Lacalle
In: http://www.lrec-conf.org/proceedings/lrec2004/pdf/753.pdf
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