A. Fujii, K. Inui, T. Tokunaga, and H. Tanaka. Selective Sampling for Example--based Word Sense Disambiguation. Computational Linguistics, 24(4):573--598, 1998.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....training set. 9 [55, 14, 56, 15] PoS tagging [61, 60, 90] PP attachment disambiguation [246] shallow parsing [227] and smoothing of probability estimates [245] The work of other authors include applications to partial parsing (chunking) and context sensitive parsing [210, 7, 33] WSD [159, 157, 84, 73], text categorization [183, 238, 237, 239] semantic interpretation [38] machine translation [100] and lexical acquisition by analogy [76, 75] 2.2.6 Inductive Logic Programming (ILP) This is a discipline devoted to the inductive learning of Prolog programs from examples. The most relevant ....

.... WSD [240, 150] 26, 150] 86, 150, 112] 67] 203, 166] Text categorization and filtering [117, 69, 230] 117, 190, 119, 142, 196] 162, 163] Dialogue act tagging [192] 193, 192] Co reference and anaphora resolution [5, 143] Cue phrase identification [122] IBL NNs EC SVM Clust WSD [159, 157, 84, 73] [150, 224] 182, 72, 74, 165] 202] Text categorization and filtering [183, 238, 237, 239] 233] 198, 196, 13] 98, 69, 99] Co reference and anaphora resol. 39] 148, 147] 35, 34] Rocchio RI ILP LSM GAs ME WSD [74] Text categorization and filtering [185, 91, 118, 196, 64] 49, ....

[Article contains additional citation context not shown here]

A. Fujii, K. Inui, T. Tokunaga, and H. Tanaka. Selective Sampling for Example--based Word Sense Disambiguation. Computational Linguistics, 24(4):573--598, 1998.

....words, or arti cial pseudo words. Many standard ML algorithms for supervised learning have been applied, such as: Decision Lists (Yarowsky, 1994; Agirre and Martinez, 2000) Neural Networks (Towell and Voorhees, 1998) Bayesian learning (Bruce and Wiebe, 1999) Exemplar Based learning (Ng, 1997a; Fujii et al. 1998), Boosting (Escudero et al. 2000a) etc. Unfortunately, there have been very few direct comparisons between alternative methods for WSD. In general, supervised learning presumes that the training examples are somehow re ective of the task that will be performed by the trainee on other data. ....

A. Fujii, K. Inui, T. Tokunaga, and H. Tanaka. 1998. Selective Sampling for Example{based Word Sense Disambiguation. Computational Linguistics, 24(4):573-598.

....semantically annotated corpus) have obtained better results than unsupervised methods on small sets of selected highly ambiguous words, or artificial pseudo words. Many standard ML algorithms for supervised learning have been applied, such as: Bayesian learning [16, 19] Exemplar based learning [18, 16, 5], Decision Lists [21] Neural Networks [20] etc. Further, Mooney [15] provides a comparative experiment on a very 1 TALP Research Center, Software Department, Technical University of Catalonia, Jordi Girona Salgado 1 3, Barcelona E 08034, Catalonia, email: fescudero, lluism, g.rigaug lsi.upc.es ....

....questioned. Due to this fact, recent works have focused on reducing the acquisition cost as well as the need for supervision of corpus based methods for WSD. Consequently, the following three lines of research are currently being studied: 1) The design of efficient example sampling methods [4, 5]; 2) The use of lexical resources, such as WordNet [13] and WWW search engines to automatically obtain from Internet accurate and arbitrarily large word sense samples [8, 12] 3) The use of unsupervised EM like algorithms for estimating the statistical model parameters [19] It is our belief ....

[Article contains additional citation context not shown here]

A. Fujii, K. Inui, T. Tokunaga, and H. Tanaka, `Selective Sampling for Example--based Word Sense Disambiguation', Computational Linguistics, 24(4), 573--598, (1998).

.... s BASURDE project TIC98 0423 C06) and by the Catalan Research Department (CIRIT s consolidated research group 1999SGR 150, CREL s Catalan WordNet project and CIRIT s grant 1999FI 00773) Many standard ML algorithms for supervised learning have been applied, such as: Naive Bayes [19, 22] [19, 10], Exemplar based learning Decision Lists [28] Neural Networks [27] etc. Further, Mooney [17] has also compared all previously cited methods on a very restricted domain and including Decision Trees and Rule Induction algorithms. Unfortunately, there have been very few direct comparisons of ....

....have been seriously questioned. Due to this fact, recent works have focused on reducing the acquisition cost as well as the need for supervision in corpus based methods for WSD. Consequently, the following three lines of research can be found: 1) The design of efficient example sampling methods [6, 10]; 2) The use of lexical resources, such as WordNet [16] and WWW search engines to automatically obtain from Internet arbitrarily large samples of word senses [12, 15] 3) The use of unsupervised EM like algorithms for estimating the statistical model parameters [22] It is also our belief that ....

Fujii, A., Inui, K., Tokunaga, T. and Tanaka, H.: Selective Sampling for Example-- based Word Sense Disambiguation. Computational Linguistics, 24(4), ACL, 1998.

....ambiguous words, or artificial pseudo words. Many standard ML algorithms for supervised learning have been applied to WSD, including: Decision Lists (Yarowsky, 1994) Neural Networks (Towell and Voorhees, 1998) Bayesian learning (Bruce and Wiebe, 1999) Exemplar based learning (Ng, 1997a; Fujii et al. 1998), and Boosting (Escudero et al. 2000a) Further, in (Mooney, 1996) some of the previously cited methods are compared, jointly with Decision Trees and Rule Induction algorithms, on a very restricted domain. The performance of supervised ML based systems is usually calculated by testing the ....

....recent works have focused on reducing the acquisition cost, the need for supervision, and the computational requirements in corpus based methods for WSD. Consequently, the following four lines of research are being explored: 1. The design of efficient sampling methods (Engelson and Dagan, 1996; Fujii et al. 1998); 2. The use of external lexical resources, such as WordNet (Miller et al. 1990) and Web search engines to automatically obtain from Internet arbitrarily large samples of word senses (Leacock et al. 1998; Mihalcea and Moldovan, 1999) 3. The use of unsupervised EM like algorithms for ....

A. Fujii, K. Inui, T. Tokunaga, and H. Tanaka. 1998. Selective Sampling for Example--based Word Sense Disambiguation. Computational Linguistics, 24(4):573--598.

....semantically annotated corpus) have obtained better results than unsupervised methods on small sets of selected highly ambiguous words, or artificial pseudo words. Many standard ML algorithms for supervised learning have been applied, such as: Bayesian learning [15, 18] Exemplar based learning [17, 15, 5], Decision Lists [20] Neural Networks [19] etc. 1 TALP Research Center, Software Department, Technical University of Catalonia, Jordi Girona Salgado 1 3, Barcelona E 08034, Catalonia, email: fescudero, lluism, g.rigaug lsi.upc.es Further, Mooney [14] provides a comparative experiment on a very ....

....questioned. Due to this fact, recent works have focused on reducing the acquisition cost as well as the need for supervision of corpus based methods for WSD. Consequently, the following three lines of research are currently being studied: 1) The design of efficient example sampling methods [4, 5]; 2) The use of lexical resources, such as WordNet [12] and WWW search engines to automatically obtain from Internet accurate and arbitrarily large word sense samples [8, 11] 3) The use of unsupervised EM like algorithms for estimating the statistical model parameters [18] It is also our ....

[Article contains additional citation context not shown here]

A. Fujii, K. Inui, T. Tokunaga, and H. Tanaka, `Selective Sampling for Example--based Word Sense Disambiguation', Computational Linguistics, 24(4), 573--598, (1998).

....semantically annotated corpus) have obtained better results than unsupervised methods on small sets of selected highly ambiguous words, or artificial pseudo words. Many standard ML algorithms for supervised learning have been applied, such as: Naive Bayes [21, 23] Exemplar based learning [20, 21, 10], Decision Lists [31] Neural Networks [28] etc. Further, Mooney [19] provides a comparative experiment on a very restricted domain between all previously cited methods but also including Decision Trees and Rule Induction algorithms. Unfortunately, there have been very few direct comparisons of ....

....have been seriously questioned. Due to this fact, recent works have focused on reducing the acquisition cost as well as the need for supervision of corpus based methods for WSD. Consequently, the following three lines of research can be found: 1) The design of efficient example sampling methods [7, 10]; 2) The use of lexical resources, such as WordNet [18] and WWW search engines to automatically obtain from Internet arbitrarily large samples for word senses [13, 17] 3) The use of unsupervised EM like algorithms for estimating the statistical model parameters [23] It is also our belief that ....

Fujii, A., Inui, K., Tokunaga, T. and Tanaka, H.: Selective Sampling for Example--based Word Sense Disambiguation. Computational Linguistics, 24(4):573-598, 1998.

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Atsushi Fujii, Kentaro Inui, Takenobu Tokunaga, and Hozumi Tanaka. 1998. Selective sampling for example-based word sense disambiguation.

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