text: This paper describes an experimental comparison of three unsupervised learning algorithms that distinguish the sense of an ambiguous word in untagged text. The methods described in this paper, McQuitty's similarity analysis, Ward's minimum-variance method, and the EM algorithm, assign each instance of an ambiguous word to a known sense definition based solely on the values of automatically identifiable features in text. These methods and feature sets are found to be more successful in disambiguating nouns rather than adjectives or verbs. Overall, the most accurate of these procedures is McQuitty's similarity analysis in combination with a high dimensional feature set.
  @cite_35: Word groupings useful for language processing tasks are increasingly available, as thesauri appear on-line, and as distributional word clustering techniques improve. However, for many tasks, one is interested in relationships among word senses, not words. This paper presents a method for automatic sense disambiguation of nouns appearing within sets of related nouns — the kind of data one finds in on-line thesauri, or as the output of distributional clustering algorithms. Disambiguation is performed with respect to WordNet senses, which are fairly fine-grained; however, the method also permits the assignment of higher-level WordNet categories rather than sense labels. The method is illustrated primarily by example, though results of a more rigorous evaluation are also presented.
  @cite_26: Publisher Summary This chapter presents a detailed description of a model for a learning process, which was proposed as an account of the learning of word classes by the child. This model is related to other theories and empirical findings to describe the results of a computer simulation, which uses recorded speech of some mothers to their children as the input corpus. It is not a complete theory of language acquisition, only an intended component of such a theory. The relationship of the proposed mechanism to other component subsystems, believed to take part in language acquisition, are indicated in the chapter. A detailed comparison is made between the model and other theoretical formulations, which finds that with the exception of the mediation theory, none of the formulations is capable of accounting for the earliest stage of word class learning. The model is related to empirical findings, which demonstrates that it can account for them. Particularly, the S-P shift is a natural consequence of the memory organization in the model. Analysis of this output from the program showed that it contains grammatically appropriate classes and exhibits certain aspects known to be characteristic for the word class systems of young children.
  @cite_1: 
  @cite_19: Syntactic information about a corpus of linguistic or pictorial data can be discovered by analyzing the statistics of the data. Given a corpus of text, one can measure the tendencies of pairs of words to occur in common contexts, and use these measurements to define clusters of words. Applied to basic English text, this procedure yields clusters which correspond very closely to the traditional parts of speech (nouns, verbs, articles, etc.). For FORTRAN text, the clusters obtained correspond to integers, operations, etc.; for English text regarded as a sequence of letters (or of phonemes) rather than words, the vowels and the consonants are obtained as clusters. Finally, applied to the gray shades in a digitized picture, the procedure yields slice levels which appear to be useful for figure extraction.
  @cite_34: We describe and experimentally evaluate a method for automatically clustering words according to their distribution in particular syntactic contexts. Deterministic annealing is used to find lowest distortion sets of clusters. As the annealing parameter increases, existing clusters become unstable and subdivide, yielding a hierarchical soft'' clustering of the data. Clusters are used as the basis for class models of word coocurrence, and the models evaluated with respect to held-out test data.
  @cite_25: 
summary: Clustering has most often been applied in natural language processing as a method for inducing syntactic or semantically related groupings of words (e.g., @cite_19 , @cite_26 , @cite_25 , @cite_34 , @cite_1 , @cite_35 ).