Word sense disambiguation: a survey <s> Selectional Preferences <s> Selectional preferences have been used by word sense disambiguation (WSD) systems as one source of disambiguating information. We evaluate WSD using selectional preferences acquired for English adjective-noun, subject, and direct object grammatical relationships with respect to a standard test corpus. The selectional preferences are specific to verb or adjective classes, rather than individual word forms, so they can be used to disambiguate the co-occurring adjectives and verbs, rather than just the nominal argument heads. We also investigate use of the one-sense-per-discourse heuristic to propagate a sense tag for a word to other occurrences of the same word within the current document in order to increase coverage. Although the preferences perform well in comparison with other unsupervised WSD systems on the same corpus, the results show that for many applications, further knowledge sources would be required to achieve an adequate level of accuracy and coverage. In addition to quantifying performance, we analyze the results to investigate the situations in which the selectional preferences achieve the best precision and in which the one-sense-per-discourse heuristic increases performance. <s> BIB001 </s> Word sense disambiguation: a survey <s> Selectional Preferences <s> This paper investigates whether multisemantic-role (MSR) based selectional preferences can be used to improve the performance of supervised verb sense disambiguation. Unlike conventional selectional preferences which are extracted from parse trees based on hand-crafted rules, and only include the direct subject or the direct object of the verbs, the MSR based selectional preferences to be presented in this paper are extracted from the output of a state-of-the-art semantic role labeler and incorporate a much richer set of semantic roles. The performance of the MSR based selectional preferences is evaluated on two distinct datasets: the verbs from the lexical sample task of SENSEVAL-2, and the verbs from a movie script corpus. We show that the MSR based features can indeed improve the performance of verb sense disambiguation. <s> BIB002
Selectional preferences [29] [30] BIB001 BIB002 find information of the likely relations of word types, and denote common sense using the knowledge source. For example, Modeling-dress, Walk-shoes are the words with semantic relationship. In this approach improper word senses are omitted and only those senses are selected which have harmony with common sense rules. The basic idea behind this approach is to count how many times this kind of word pair occurs in the corpus with syntactic relation. From this count, senses of words will be identified. There are other methods, which can find this kind of relation among words using conditional probability.