Automatic Acquisition of Subcategorization Frames From Untagged Text

AUTOMATIC ACQUISITION OF SUBCATEGORIZATION FRAMES FROM UNTAGGED TEXT Michael R. Brent MIT AI Lab 545 Technology Square Cambridge, Massachusetts 02139 michael@ai.mit.edu ABSTRACT This paper describes an implemented program that takes a raw, untagged text corpus as its only input (no open-class dictionary) and gener- ates a partial list of verbs occurring in the text and the subcategorization frames (SFs) in which they occur. Verbs are detected by a novel tech- nique based on the Case Filter of Rouvret and Vergnaud (1980). The completeness of the output list increases monotonically with the total number of occurrences of each verb in the corpus. False positive rates are one to three percent of observa- tions. Five SFs are currently detected and more are planned. Ultimately, I expect to provide a large SF dictionary to the NLP community and to train dictionaries for specific corpora. 1 INTRODUCTION This paper describes an implemented program that takes an untagged text corpus and generates a partial list of verbs occurring in it and the sub- categorization frames (SFs) in which they occur. So far, it detects the five SFs shown in Table 1. SF Good Example Bad Example Description direct object direct object & clause direct object & infinitive clause infinitive greet them tell him he's a fool want him to attend know I'll attend hope to attend *arrive them *hope him he's a fool *hope him to attend *want I'll attend *greet to attend Table 1: The five subcategorization frames (SFs) detected so far The SF acquisition program has been tested on a corpus of 2.6 million words of the Wall Street Journal (kindly provided by the Penn Tree Bank project). On this corpus, it makes 5101 observa- tions about 2258 orthographically distinct verbs. False positive rates vary from one to three percent of observations, depending on the SF. 1.1 WHY IT MATTERS Accurate parsing requires knowing the sub- categorization frames of verbs, as shown by (1). (1) a. I expected [nv the man who smoked NP] to eat ice-cream h. I doubted [NP the man who liked to eat ice-cream NP] Current high-coverage parsers tend to use either custom, hand-generated lists of subcategorization frames (e.g., Hindle, 1983), or published, hand- generated lists like the Ozford Advanced Learner's Dictionary of Contemporary English, Hornby and Covey (1973) (e.g., DeMarcken, 1990). In either case, such lists are expensive to build and to main- tain in the face of evolving usage. In addition, they tend not to include rare usages or specialized vocabularies like financial or military jargon. Fur- ther, they are often incomplete in arbitrary ways. For example, Webster's Ninth New Collegiate Dic- tionary lists the sense of strike meaning 'go occur to", as in "it struck him that... ", but it does not list that same sense of hit. (My program discov- ered both.) 1.2 WHY IT'S HARD The initial priorities in this research were: . Generality (e.g., minimal assumptions about the text) . Accuracy in identifying SF occurrences • Simplicity of design and speed Efficient use …

Content truncated to 500 words