Extracting Noun Phrases from Large-Scale Texts: A Hybrid Approach and its Automatic Evaluation

phrases. The partial parser is motivated by an intuition (Abney, 1991): To acquire noun phrases from running texts is useful for many applications, such as word grouping, terminology indexing, etc. The reported literatures adopt pure probabilistic approach, or pure rule-based noun phrases grammar to tackle this problem. In this paper, we apply a probabilistic chunker to deciding the implicit boundaries of constituents and utilize the linguistic knowledge to extract the noun phrases by a finite state mechanism. The test texts are SUSANNE Corpus and the results are evaluated by comparing the parse field of SUSANNE Corpus automatically. The results of this preliminary experiment are encouraging. (1) When we read a sentence, we read it chunk by chunk. Abney uses two level grammar rules to implement the parser through pure LR parsing technique. The first level grammar rule takes care of the chunking process. The second level grammar rule tackles the attachment problems among chunks. Historically, our statisticsbased partial parser is called chunker. The chunker receives tagged texts and outputs a linear chunk sequences. We assign a syntactic head and a semantic head to each chunk. Then, we extract the plausible maximal noun phrases according to the information of syntactic head and semantic head, and a finite state mechanism with only 8 states.