The Selection of the Most Probable Dependency Structure in Japanese Using Mutual Information

The Selection of the Most Probable Dependency Structure in Japanese Using Mutual Information Eduardo de Paiva Alves University of Electro-Connnunications 1-5-1 Chofugaoka Chofushi Tokyo Japan ealves@phaeton.cs.uec.ac.jp Abstract We use a statistical method to select the most probable structure or parse for a given sentence. It takes as input the dependency structures generated for the sentence by a dependency grammar, finds all triple of modifier, particle and modificant relations, calculates mutual information of each re- lation and chooses the structure for which the product of the mutual information of its relations is the highest. 1 Introduction Computer Aided Instruction (CAI) systems are im- portant and effective tools, especially for teaching foreign languages. Many students of Japanese as a foreign language are aware of the Computer Assisted TEchnical Reading System (CATERS) that provides helpful information for reading texts in science and technology fields (Kano a~ld Yamamoto, 1995). One of the difficulties in learning Japanese lies in recognizing dependency relations in Japallese sen- tences. This is because the language allows relatively free word orders. Take an example from a leading newspaper: We would like to expect a prompt study of the causes, based on a national investigation To understand this sentence it is necessa~'y to know that ~1:~=~= (investigation) modifies ~o ~ -'3 ~,~$: (based) but not ~L~.=~ ~ (expect); ~0 ~"3~fa (based) modifies ~] (study) but not ~,~ (cause). CATERS is useful because it provides such infor- mation through several user-friendly functions. As effective as it is for foreign students, however, the texts in CATERS are fixed and the dependency structure of every sentence in them is all hand-coded. This inability to handle new text poses a serious problem in its general applicability and extensibility. This paper describes a method for selecting the right or most probable structure for a Japanese sen- tence among multiple probable structures generated by Restricted Dependency Grammar (RDG) (Fuku- moto, 1992). If this method works, then its results will be quite valuable for facilitating the develop- ment of new texts for CAI systems like CATERS. 2 Background As pointed out earlier, the dependency relation of elements in Japanese sentences are fairly compli- cated due to relatively free word orders. RDG is designed to determine dependency relations among words and phrases in sentences. To do so, it clas- sifies the phrases according to grammatical cate- gories and syntactic attributes. However, it fails to reject semantically unacceptable dependency struc- tures. The inevitable consequence is that RDG often produces multiple parses even for a simple sentence. Kurohashi and Nagao (1993) try to determine the dependency relations of a sentence by means of using sample sentences. When the sentence is structurally ambiguous, they determine its structure by compar- ing it to structurally similax patterns taken from a manually generated set of examples and calculating similarity values. Our method, on the contrary, uses a statistical ap- proach to select the most probable structure or parse of a given sentence. It takes as input dependency structures generated by RDG for a sentence, finds …

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