Title: Improving Legal Information Retrieval by Distributional Composition with Term Order Probabilities,
Abstract: Legal professionals worldwide are currently trying to get up-to-pace with the
explosive growth in legal document availability through digital means. This
drives a need for high efficiency Legal Information Retrieval (IR) and Question
Answering (QA) methods. The IR task in particular has a set of unique
challenges that invite the use of semantic motivated NLP techniques. In this
work, a two-stage method for Legal Information Retrieval is proposed, combining
lexical statistics and distributional sentence representations in the context
of Competition on Legal Information Extraction/Entailment (COLIEE). The
combination is done with the use of disambiguation rules, applied over the
rankings obtained through n-gram statistics. After the ranking is done, its
results are evaluated for ambiguity, and disambiguation is done if a result is
decided to be unreliable for a given query. Competition and experimental
results indicate small gains in overall retrieval performance using the
proposed approach. Additionally, an analysis of error and improvement cases is
presented for a better understanding of the contributions.