Learning New Compositions from Given Ones

Learning New Compositions from Given Ones Ji Donghong Dept. of Computer Science Tsinghua University Beijing, 100084 P. R. China j dh©sl000e, cs. t s±nghua, edu. cn He Jun Dept. of Computer Science Herbin Institute of Technology hj ©pact 518. hit. edu. cn Huang Changning Dept. of Computer Science Tsinghua University Beijing, 100084 P. R. China hcn@mail.tsinghua.edu.cn Abstract In this paper, we study the problem of "learning new compositions of words from given ones with a specific syntactic struc- ture, e.g., A-N or V-N structures. We first cluster words according to the given com- positions, then construct a cluster-based compositional frame for each word cluster, which contains both new and given compo- sitions relevant with the words in the clus- ter. In contrast to other methods, we don't pre-define the number of clusters, and for- malize the problem of clustering words as a non-linear optimization one, in which we specify the environments of words based on word clusters to be determined, rather than their neighboring words. To solve the prob- lem, we make use of a kind of cooperative evolution strategy to design an evolution- ary algorithm. 1 Introduction Word compositions have long been a concern in lex- icography(Benson et al. 1986; Miller et al. 1995), and now as a specific kind of lexical knowledge, it has been shown that they have an important role in many areas in natural language process- ing, e.g., parsing, generation, lexicon building, word sense disambiguation, and information retrieving, etc.(e.g., Abney 1989, 1990; Benson et al. 1986; Yarowsky 1995; Church and Hanks 1989; Church, Gale, Hans, and Hindle 1989). But due to the huge number of words, it is impossible to list all compositions between words by hand in dictionar- ies. So an urgent problem occurs: how to auto- matically acquire word compositions? In general, word compositions fall into two categories: free compositions and bound compositions, i.e., collo- cations. Free compositions refer to those in which words can be replaced by other similar ones, while in bound compositions, words cannot be replaced freely(Benson 1990). Free compositions are pre- dictable, i.e., their reasonableness can be determined according to the syntactic and semantic properties of the words in them. While bound compositions are not predictable, i.e., their reasonableness cannot be derived from the syntactic and semantic prop- erties of the words in them(Smadja 1993). Now with the availability of large-scale corpus, automatic acquisition of word compositions, especially word collocations from them have been extensively stud- ied(e.g., Choueka et al. 1988; Church and Hanks 1989; Smadja 1993). The key of their methods is to make use of some statistical means, e.g., frequen- cies or mutual information, to quantify the compo- sitional strength between words. These methods are more appropriate for retrieving bound compositions, while less appropriate for retrieving free ones. This is because in free compositions, words are related with each other in a more loose way, which may result in the invalidity of mutual information and other statistical means in distinguishing reasonable compositions from unreasonable ones. In this …

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