Clustering for obtaining syntactic classes of words from automatically extracted LTAG grammars

We propose a method for obtaining syntactic classes of words from a lexicalized tree adjoining grammar (LTAG: Schabes, Abeille and Joshi (1988)) automatically extracted from a corpus. Since elementary trees in LTAG grammars represent syntactic roles of a word, we can obtain syntactic classes by clustering words having the similar elementary trees. With our method, automatically extracted LTAG grammars will be arranged according to the syntactic classes of words, and the grammars can be improved from various points of view. For example, we can improve the coverage of the grammars by supplementing to a word the elementary trees of the syntactic class of the word. An LTAG grammar consists of elementary trees, which determine the position where the word can be put in a sentence, that is, an elementary tree corresponds to a certain syntactic role. Hence, a syntactic class of a word is represented as a set of elementary trees assigned to the word. Since the words of the same syntactic class are expected to have similar elementary trees, we can obtain syntactic classes by clustering words having similar sets of elementary trees. We applied our clustering algorithm to an LTAG grammar automatically extracted from sections 02–21 of the Penn Treebank (Marcus, Santorini and Marcinkiewicz (1994)), and investigated the obtained clusters with changing the number of clusters. We successfully obtained some of the clusters that correspond to certain syntactic classes. On the other hand, we could not obtain some clusters, such as the one for ditransitive verbs, and obtained the clusters that we could not associate clearly with syntactic classes. This is because our method was strongly affected by the difference in the number of words in each part-of-speech class. We concluded that, although our clustering method needs to be improved for practical use, it is effective to automatically obtain syntactic classes of words. The XTAG English grammar (The XTAG Research Group (1995)) is a handmade LTAG grammar which is arranged according to syntactic classes of words, “tree families. ” Each tree family corresponds to a certain subcategorization frame, and determines elementary trees to be assigned to a word. Thanks to the tree families, the XTAG grammar is independent of a corpus. However, it needs considerable human effort to manually construct such a grammar. Automatically extracted LTAG grammars are superior to manually developed grammars in the sense that it takes much less costs to construct the grammars. Chiang (2000) and Xia (1999) gave the methods of automatically extracting LTAG grammars from a bracketed corpus. They first decided a trunk path of the tree structure of a bracketed sentence, and the relationship (substitution or adjunction) between the trunk and branches. The methods then cut off the branches of them according to the relationship. Because the sentences used for extraction are in real-world texts, extracted grammars are practical for natural language processing. However, automatically extracted grammars are not systematically arranged according to syntactic classes their anchors belong to, like the XTAG grammar. Because of this, automatically extracted grammars tend to be strongly dependent on the corpus. This limitation can be a critical disadvantage of such extracted grammars when the grammars are used for various applications. Then, we want to arrange an extracted grammar according to the syntactic classes of words, without loosing the benefit for the cost. Chen and Vijay-Shanker (2000) proposed the solution to the issue. To improve the coverage of an extracted LTAG grammar, they classified the extracted elementary trees according to the tree families in the XTAG English grammar. First, the method searches for a tree family that contains an elementary tree template of extracted elementary tree et. Next, the method collects other possible tree templates in the tree family and makes elementary trees with the anchor of et and the tree templates. By using tree families, the method can add only proper elementary trees that correspond to the syntactic class of anchors. Chen and Vijay-Shanker (2000) applied this method to an extracted LTAG grammar, and showed the improvement of the coverage of the grammar. Although their method showed the effectiveness of arranging a grammar according to syntactic classes, the method depends on