P. Pedersen and R. Bruce. Distinguishing word senses in untagged text. In Proceedings of the Second Conference on Empirical Methods in Natural Language Processing, 1997. Home/Search Document Details and Download
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Learning And Generalization In The Creation Of Information.. - Chai (1998) (Correct)
....and, to date, annotated text collections are only available for a small number of selected words with predefined senses. The lack of resources has led several researchers to explore the use of unannotated, raw corpora to perform unsupervised learning. One example is the work of Pedersen and Bruce [78] who compared three different unsupervised learning algorithms on 13 different words. Each algorithm has access to the number of senses for each word and split the instances of each word into the appropriate number of clusters. These clusters were then mapped onto the closest sense from the ....
T. Pedersen and R. Bruce. Distinguishing word senses in untagged text. Proceedings of the Second Conference on Empirical Methods in Natural Language Processing, 1997.
Evaluating High Accuracy Retrieval Techniques - Shah, Croft (2004) Self-citation (Bruce) (Correct)
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Pedersen, Ted, and Rebecca Bruce. Distinguishing word senses in untagged text. In Proceedings of the 2nd Conference on Empirical Methods in NLP (EMNLP-2), Providence, August 1997.
Evaluating High Accuracy Retrieval Techniques - Chirag Shah Bruce Self-citation (Bruce) (Correct)
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Pedersen, Ted, and Rebecca Bruce. Distinguishing word senses in untagged text. In Proceedings of the 2nd Conference on Empirical Methods in NLP (EMNLP-2), Providence, August 1997.
Word-Sense Distinguishability and Inter-Coder Agreement - Bruce, Wiebe (1998) (2 citations) Self-citation (Bruce) (Correct)
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Pedersen, T. & Bruce, R. (1997). Distinguishing Word Senses in Untagged Text. Proceedings of the Second Conference on Empirical Methods in Natural Language Processing (EMNLP-97), August 1997.
Recognizing Subjectivity: A Case Study of Manual Tagging - Bruce, al. (1999) (3 citations) Self-citation (Bruce) (Correct)
....probable latent category given the tags assigned by the judges. The EM algorithm takes as input the number of latent categories hypothesized, i.e. the number of values of L, and produces estimates of the parameters. For a description of this process, see Goodman (1974) Dawid Skene (1979) or Pedersen Bruce (1997). Three different versions of the latent class model, each specifying a different number of latent categories, are considered: the two category, the three category and the four category latent class models. The models are applied to all three data configurations. In all cases, the model contains ....
Pedersen, T. & Bruce, R. (1997). Distinguishing Word Senses in Untagged Text. Proceedings of the Second Conference on Empirical Methods in Natural Language Processing (EMNLP-97), pp. 197--207.
Knowledge Lean Word-Sense Disambiguation - Pedersen, Bruce (1998) (10 citations) Self-citation (Pedersen Bruce) (Correct)
....words. This result demonstrates the effectiveness of a small number of representative collocations as seeds in an iterative bootstrapping approach. A comparison of the EM algorithm and two agglomerative clustering algorithms as applied to unsupervised word sense disambiguation is discussed in (Pedersen Bruce 1997). Using the same data used in this study, Pedersen Bruce 1997) found that McQuitty s agglomerative algorithm is significantly more accurate for adjectives and verbs while the EM algorithm is significantly more accurate for nouns. These results indicate that McQuitty s analysis, which is based ....
....number of representative collocations as seeds in an iterative bootstrapping approach. A comparison of the EM algorithm and two agglomerative clustering algorithms as applied to unsupervised word sense disambiguation is discussed in (Pedersen Bruce 1997) Using the same data used in this study, (Pedersen Bruce 1997) found that McQuitty s agglomerative algorithm is significantly more accurate for adjectives and verbs while the EM algorithm is significantly more accurate for nouns. These results indicate that McQuitty s analysis, which is based on counts of dissimilar features, is most appropriate for highly ....
Pedersen, T., and Bruce, R. 1997. Distinguishing word senses in untagged text. In Proceedings of the Second Conference on Empirical Methods in Natural Language Processing, 197--207.
Integrating Natural Language Subtasks with Bayesian Belief Networks - Pedersen (1999) Self-citation (Pedersen) (Correct)
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Pedersen, T., and Bruce, R. 1997a. Distinguishing word senses in untagged text. In Proceedings of the Second Conference on Empirical Methods in Natural Language Processing, 197--207.
Information Retrieval by Means of Word Sense - Disambiguation Alfonso Ure (Correct)
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P. Pedersen and R. Bruce. Distinguishing word senses in untagged text. In Proceedings of the Second Conference on Empirical Methods in Natural Language Processing, 1997.
Is Word Sense Disambiguation just one more NLP task? - Wilks (1998) (4 citations) (Correct)
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Pedersen, T. and Bruce, R. (1997) Distinguishing Word Senses in Untagged Text, Proceedings of the Second Conference on Empirical Methods in Natural Language Processing, pp. 197-207, Providence, RI.
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