Paper: Knowledge Graph and Corpus Driven Segmentation and Answer Inference for Telegraphic Entity-seeking Queries

ACL ID D14-1117
Title Knowledge Graph and Corpus Driven Segmentation and Answer Inference for Telegraphic Entity-seeking Queries
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2014
Authors

Much recent work focuses on formal in- terpretation of natural question utterances, with the goal of executing the resulting structured queries on knowledge graphs (KGs) such as Freebase. Here we address two limitations of this approach when ap- plied to open-domain, entity-oriented Web queries. First, Web queries are rarely well- formed questions. They are ?telegraphic?, with missing verbs, prepositions, clauses, case and phrase clues. Second, the KG is always incomplete, unable to directly an- swer many queries. We propose a novel technique to segment a telegraphic query and assign a coarse-grained purpose to each segment: a base entity e 1 , a rela- tion type r, a target entity type t 2 , and contextual words s. The query seeks en- tity e 2 ? t