Yu, L.-C.; Wu, C.-H.; Chang, R.-Y.; Liu, C.-H.; Hovy, E.H.: Annotation and verification of sense pools in OntoNotes (2010)
0.02
0.016424898 = product of:
0.032849796 = sum of:
0.032849796 = product of:
0.06569959 = sum of:
0.06569959 = weight(_text_:n in 4236) [ClassicSimilarity], result of:
0.06569959 = score(doc=4236,freq=4.0), product of:
0.19504215 = queryWeight, product of:
4.3116565 = idf(docFreq=1611, maxDocs=44218)
0.045236014 = queryNorm
0.33684817 = fieldWeight in 4236, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
4.3116565 = idf(docFreq=1611, maxDocs=44218)
0.0390625 = fieldNorm(doc=4236)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Abstract
- The paper describes the OntoNotes, a multilingual (English, Chinese and Arabic) corpus with large-scale semantic annotations, including predicate-argument structure, word senses, ontology linking, and coreference. The underlying semantic model of OntoNotes involves word senses that are grouped into so-called sense pools, i.e., sets of near-synonymous senses of words. Such information is useful for many applications, including query expansion for information retrieval (IR) systems, (near-)duplicate detection for text summarization systems, and alternative word selection for writing support systems. Although a sense pool provides a set of near-synonymous senses of words, there is still no knowledge about whether two words in a pool are interchangeable in practical use. Therefore, this paper devises an unsupervised algorithm that incorporates Google n-grams and a statistical test to determine whether a word in a pool can be substituted by other words in the same pool. The n-gram features are used to measure the degree of context mismatch for a substitution. The statistical test is then applied to determine whether the substitution is adequate based on the degree of mismatch. The proposed method is compared with a supervised method, namely Linear Discriminant Analysis (LDA). Experimental results show that the proposed unsupervised method can achieve comparable performance with the supervised method.