TECHNIQUES FOR UNDERSTANDING THE ABOUTNESS OF TEXT BASED ON SEMANTIC ANALYSIS

摘要

In one embodiment of the present invention, a semantic analyzer translates a text segment into a structured representation that conveys the meaning of the text segment. Notably, the semantic analyzer leverages a semantic network to perform word sense disambiguation operations that map text words included in the text segment into concepts—word senses with a single, specific meaning—that are interconnected with relevance ratings. A topic generator then creates topics on-the-fly that includes one or more mapped concepts that are related within the context of the text segment. In this fashion, the topic generator tailors the semantic network to the text segment. A topic analyzer processes this tailored semantic network, generating a relevance-ranked list of topics as a meaningful proxy for the text segment. Advantageously, operating at the level of concepts and topics reduces the misinterpretations attributable to key word and statistical analysis methods.

主权项

1. A computer-implemented method for interpreting text segments based on word sense, the method comprising:
parsing a text segment to generate one or more text-based words and related syntactic information; mapping each of the one or more text-based words to at least one concept based on a semantic network that includes the at least one concept and one or more relevance ratings associated with the at least one concept, wherein each concept included in the semantic network is associated with a meaning and at least one word; generating a plurality of topics based on the mappings and the syntactic information, wherein each topics includes one or more of the concepts included in the semantic network; for each topic included in the plurality of topics, calculating a topic relevance rating between the topic and at least another topic included in the plurality of topics based on the relevance ratings between the one or more concepts included in the topic and one or more concepts included in the another topic; and ranking the plurality of topics based on the topic relevance ratings.