Mining Semantic Structures from Syntactic Structures in Free Text Documents

The Web has made possible many advanced text-mining applications, such as news summarization, essay grading, question answering, and semantic search. For many of such applications, statistical text-mining techniques are ineffective since they do not utilize the morphological structure of the text. Thus, many approaches use NLP-based techniques, that parse the text and use patterns to mine and analyze the parse trees which are often unnecessarily complex. Therefore, we propose a weighted-graph representation of text, called Text Graphs, which captures the grammatical and semantic relations between words and terms in the text. Text Graphs are generated using a new text mining framework which is the main focus of this paper. Our framework, SemScape, uses a statistical parser to generate few of the most probable parse trees for each sentence and employs a novel two-step pattern-based technique to extract from parse trees candidate terms and their grammatical relations. Moreover, SemScape resolves co references by a novel technique, generates domain-specific Text Graphs by consulting ontologies, and provides a SPARQL-like query language and an optimized engine for semantically querying and mining Text Graphs.