Shallow Parsing Based on Maximum Matching Method and Scoring Model

Shallow parsing is a very important task in natural language processing or text mining, and the partial syntactical information can help to solve many other natural language processing tasks. In this paper, we split the task of shallow parsing into two subtasks: (1) Seeking all the break points to divide a part-of-speech (POS) sequence into some groups; (2) Tagging a phrase type for each POS group. In the first, we present the break point seeking (BPS) algorithm,which is combination of scoring model (SM) and maximum matching method (MM), to solve the first subtask. Then,we used the Bayes classifier to tag the phrase structure type for each POS group. The result shows that although our method did not apply any syntactic rules, the BPS algorithm, which combined the MM with SM algorithm, exerted the strong point of the MM and SM algorithm, obtained a favorable performance.