Automatic Pattern Acquisition for Japanese Information Extraction

Automatic Pattern Acquisition for Japanese Information Extraction Kiyoshi Sudo Computer Science Department New York University 715 Broadway, 7th floor, New York, NY 10003 USA sudo@cs.nyu.edu Satoshi Sekine Computer Science Department New York University 715 Broadway, 7th floor, New York, NY 10003 USA sekine@cs.nyu.edu Ralph Grishman Computer Science Department New York University 715 Broadway, 7th floor, New York, NY 10003 USA grishman@cs.nyu.edu ABSTRACT One of the central issues for information extraction is the cost of customization from one scenario to another. Research on the auto- mated acquisition of patterns is important for portability and scala- bility. In this paper, we introduce Tree-Based Pattern representation where a pattern is denoted as a path in the dependency tree of a sen- tence. We outline the procedure to acquire Tree-Based Patterns in Japanese from un-annotated text. The system extracts the relevant sentences from the training data based on TF/IDF scoring and the common paths in the parse tree of relevant sentences are taken as extracted patterns. Keywords Information Extraction, Pattern Acquisition 1. INTRODUCTION Information Extraction (IE) systems today are commonly based on pattern matching. New patterns need to be written when we customize an IE system for a new scenario (extraction task); this is costly if done by hand. This has led to recent research on automated acquisition of patterns from text with minimal pre-annotation. Riloff [4] reported a successful result for her procedure that needs only a pre-classified corpus. Yangarber [6] developed a procedure for unannotated natural language texts. One of their common assumption is that the relevant documents include good patterns. Riloff implemented this idea by applying the pre-defined heuristic rules to pre-classified (relevant) documents and Yangarber advanced further so that the system can classify the documents by itself given seed patterns specific to a scenario and then find the best patterns from the relevant document set. Considering how they represent the patterns, we can see that, in general, Riloff and Yangarber relied on the sentence structure of English. Riloff’s predefined heuristic rules are based on syn- tactic structures, such as “<subj> active-verb” and “active-verb . <dobj>”. Yangarber used triples of a predicate and some of its arguments, such as “<pred> <subj> <obj>”. The Challenges Our careful examination of Japanese revealed some of the chal- lenges for automated acquisition of patterns and information ex- traction on Japanese(-like) language and other challenges which arise regardless of the languages. Free Word-ordering Free word order is one of the most significant problems in analyz- ing Japanese. To capture all the possible patterns given a predicate and its arguments, we need to permute the arguments and list all the patterns separately. For example, for “<subj> <dobj> <iobj> <predicate>” with the constraint that the predicate comes last in the sentence, there would be six possible patterns (permutations of three arguments). The number of patterns to cover even simple facts would rise unacceptably high. Flexible case marking system There is also a difficulty in a language with a flexible case marking system, like Japanese. In particular, we found …

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