Information Extraction using Non-consecutive Word Sequences

We address an important deficiency in existing machine learning approaches for in- formation extraction from natural language texts. Existing techniques for information extraction employ rules that exploit properties of consecutive word sequences. We argue that sequences of non-consecutive words capturing long range contextual correlations are vital features for informa- tion extraction from natural language text. We propose an efficient method that extends the a-priori algorithm to mine frequently occurring non-consecutive word sequences from a given corpus. We also perform a simplistic aggregation of feature information across multiple mentions of an en- tity in a document to avoid independent classification of the multiple occurrences of the entity. Experiments on some standard data sets show substantial improvements over previously reported results.