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Automatic Word Sense Disambiguation Using Cooccurrence and Hierarchical Information
[chapter]
2010
Lecture Notes in Computer Science
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We use that information to expand sense glosses of the senses in WordNet and compare the similarity between the contexts vectors and the word sense vectors in a way similar to that used by Yarowsky and ...
It is based on a combination of selectional preference measured over a large corpus and hierarchical information taken from WordNet, as well as some additional heuristics. ...
For Senseval-2, we have combined word cooccurrence and hierarchical information as sources of disambiguation evidence [5] . ...
doi:10.1007/978-3-642-13881-2_6
fatcat:imb7m6bghva3xdabar2z2uebi4
Combining Unsupervised Lexical Knowledge Methods for Word Sense Disambiguation
[article]
1997
arXiv
pre-print
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This paper presents a method to combine a set of unsupervised algorithms that can accurately disambiguate word senses in a large, completely untagged corpus. ...
Although most of the techniques for word sense resolution have been presented as stand-alone, it is our belief that full-fledged lexical ambiguity resolution should combine several information sources ...
Country and Horacio Rodríguez in Catalonia. ...
arXiv:cmp-lg/9704007v1
fatcat:qlqcgxm5czhn5e57y6kk4wmzye
Combining unsupervised lexical knowledge methods for word sense disambiguation
1997
Proceedings of the 35th annual meeting on Association for Computational Linguistics -
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This paper presents a method to combine a set of unsupervised algorithms that can accurately disambiguate word senses in a large, completely untagged corpus. ...
Although most of the techniques for word sense resolution have been presented as stand-alone, it is our belief that full-fledged lexical ambiguity resolution should combine several information sources ...
Country and Horacio Rodr~guez in Catalonia. ...
doi:10.3115/976909.979624
dblp:conf/acl/RigauAA97
fatcat:hyjbadnu4fbslmrwze2twpwuv4
Page 1997 of Computational Linguistics Vol. 24, Issue 1
[page]
1998
Computational Linguistics
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Using WordNet to disambiguate word senses for text retrieval. In Proceedings of SIGIR ‘93, pages 171-180.
Walker, Donald E. and Robert A. Amsler. 1986. ...
Introduction to Modern Information Retrieval. McGraw-Hill, New York.
Sanderson, Mark. 1994. Word sense disambiguation and information retrieval. In Proceedings of SIGIR ‘94, pages 142-151. ...
Using big data to support automatic Word Sense Disambiguation
2014
2014 International Conference on High Performance Computing & Simulation (HPCS)
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In this paper, we propose an approach to automatically build a generic sense inventory (called iSC) to be used as a reference for disambiguation. ...
Word Sense Disambiguation (WSD) usually relies on data structures built upon the words to be disambiguated. This is a time-consuming process that requires a huge computational effort. ...
AUTOMATIC WORD SENSE DISAMBIGUATION, LIMITS AND SOLUTIONS Automatic WSD addresses the problem of identifying which sense is more suitable for a polysemic word in a context, dividing the occurrences of ...
doi:10.1109/hpcsim.2014.6903701
dblp:conf/hpcs/SimoniniG14
fatcat:z64cfhmdzbcyheqrjyeioxuhei
Learning similarity-based word sense disambiguation from sparse data
[article]
1996
arXiv
pre-print
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We describe a method for automatic word sense disambiguation using a text corpus and a machine-readable dictionary (MRD). The method is based on word similarity and context similarity measures. ...
A new instance of a polysemous word is assigned the sense associated with the typical usage most similar to its context. ...
Introduction Word Sense Disambiguation (WSD) is the problem of assigning a sense to an ambiguous word, using its context. ...
arXiv:cmp-lg/9605009v2
fatcat:foj2lowcpnhe7mv56krunbhh2u
Word Sense Disambiguation using Conceptual Density
[article]
1996
arXiv
pre-print
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The results of the experiments have been automatically evaluated against SemCor, the sense-tagged version of the Brown Corpus. ...
This paper presents a method for the resolution of lexical ambiguity of nouns and its automatic evaluation over the Brown Corpus. ...
Acknowledgements We wish to thank all the staff of the Computer Research Laboratory and specially Jim Cowie, Joe Guthtrie, Louise Guthrie and David Farwell. ...
arXiv:cmp-lg/9606007v1
fatcat:4agvqv6p5jfw7p7qkmcg3ci6qu
Contextual word similarity and estimation from sparse data
1995
Computer Speech and Language
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A background survey is included, covering issues of lexical cooccurrence, data sparseness and smoothing, word similarity and clustering, and mutual information. ...
These analogies are based on the assumption that similar word cooccurrences have similar values of mutual information. ...
Acknowledgements We w ould like to thank Alon Itai for help in initiating this research, and Ken Church for helpful comments on an earlier draft of this paper. ...
doi:10.1006/csla.1995.0008
fatcat:3sblkcheqbbkldgtlyy4fk5mgy
Contextual word similarity and estimation from sparse data
1993
Proceedings of the 31st annual meeting on Association for Computational Linguistics -
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A background survey is included, covering issues of lexical cooccurrence, data sparseness and smoothing, word similarity and clustering, and mutual information. ...
These analogies are based on the assumption that similar word cooccurrences have similar values of mutual information. ...
Acknowledgements We w ould like to thank Alon Itai for help in initiating this research, and Ken Church for helpful comments on an earlier draft of this paper. ...
doi:10.3115/981574.981596
dblp:conf/acl/DaganMM93
fatcat:rv3mhgxbzbh3xg5o5n6bpyuuay
Similarity-Based Models of Word Cooccurrence Probabilities
[article]
1998
arXiv
pre-print
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We also compare four similarity-based estimation methods against back-off and maximum-likelihood estimation methods on a pseudo-word sense disambiguation task in which we controlled for both unigram and ...
In this work we propose a method for estimating the probability of such previously unseen word combinations using available information on "most similar" words. ...
model, and Andrej Ljolje and Michael Riley for providing word lattices for our speech recognition evaluation. ...
arXiv:cs/9809110v1
fatcat:hb3aaoyt3rfz3drt27lhrvxm6q
Exploiting Thesauri and Hierarchical Categories in Cross-Language Information Retrieval
[chapter]
2002
Lecture Notes in Computer Science
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We propose a model using multiple sources for query reformulation and expansion to select expansion terms and retrieve information needed by a user. ...
A combination to a dictionary-based translation and statistical-based disambiguation is indispensable to overcome translation's ambiguity. ...
disambiguation and select best target translations, • Adding domain keywords to the original query and then selecting thesaurus word senses, to avoid wrong sense disambiguation, is considered as an effective ...
doi:10.1007/3-540-46154-x_18
fatcat:23tmssl6pfcl5probpxxr44c6i
Word Sense Disambiguation by Relative Selection
[chapter]
2005
Lecture Notes in Computer Science
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Only one cooccurrence frequency matrix is utilized to efficiently disambiguate senses of many target words. ...
This paper describes a novel method for a word sense disambiguation that utilizes relatives (i.e. synonyms, hypernyms, meronyms, etc in WordNet) of a target word and raw corpora. ...
The corpora, which have sense information of all words, have been built recently, but are not large enough to provide sufficient disambiguation information of the all words. ...
doi:10.1007/11562214_80
fatcat:esazdknon5avzivpvfq5mw2jhe
An Insight into Word Sense Disambiguation Techniques
2015
International Journal of Computer Applications
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This paper presents various techniques used in the area of Word Sense Disambiguation (WSD). ...
previously semantically annotated corpus; Unsupervised approaches that form cluster occurrences of words. ...
INTRODUCTION Word Sense Disambiguation is the determination of sense of a particular word used in a particular context. ...
doi:10.5120/20888-3666
fatcat:igoikbgvavfetfptwinwbz5z7y
Biomedical word sense disambiguation with ontologies and metadata: automation meets accuracy
2009
BMC Bioinformatics
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Another useful source of information for disambiguation are metadata. Here, we systematically compare three approaches to word sense disambiguation, which use ontologies and metadata, respectively. ...
Classical approaches to word sense disambiguation use co-occurring words or terms. ...
An implementation of the Porter stemmer is used [44] and as features we select n-tuples of word stems and meta information of the document, such as the journal and title words and the publication period ...
doi:10.1186/1471-2105-10-28
pmid:19159460
pmcid:PMC2663782
fatcat:gv64eqpyvna6jmfzxugdltml3m
Evaluating Feature Extraction Methods for Knowledge-based Biomedical Word Sense Disambiguation
2017
BioNLP 2017
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In this paper, we present an analysis of feature extraction methods via dimensionality reduction for the task of biomedical Word Sense Disambiguation (WSD). ...
We modify the vector representations in the 2-MRD WSD algorithm, and evaluate four dimensionality reduction methods: Word Embeddings using Continuous Bag of Words and Skip Gram, Singular Value Decomposition ...
Introduction W ord Sense Disambiguation (WSD) is the task of automatically identifying the intended sense (or concept) of an ambiguous word based on the context in which the word is used. ...
doi:10.18653/v1/w17-2334
dblp:conf/bionlp/HenryCM17
fatcat:s27v3blmy5ebtepd73peqe54vm
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