2 research outputs found
Syntactic Patterns Improve Information Extraction for Medical Search
Medical professionals search the published literature by specifying the type
of patients, the medical intervention(s) and the outcome measure(s) of
interest. In this paper we demonstrate how features encoding syntactic patterns
improve the performance of state-of-the-art sequence tagging models (both
linear and neural) for information extraction of these medically relevant
categories. We present an analysis of the type of patterns exploited, and the
semantic space induced for these, i.e., the distributed representations learned
for identified multi-token patterns. We show that these learned representations
differ substantially from those of the constituent unigrams, suggesting that
the patterns capture contextual information that is otherwise lost
ASPER: Attention-based Approach to Extract Syntactic Patterns denoting Semantic Relations in Sentential Context
Semantic relationships, such as hyponym-hypernym, cause-effect,
meronym-holonym etc. between a pair of entities in a sentence are usually
reflected through syntactic patterns. Automatic extraction of such patterns
benefits several downstream tasks, including, entity extraction, ontology
building, and question answering. Unfortunately, automatic extraction of such
patterns has not yet received much attention from NLP and information retrieval
researchers. In this work, we propose an attention-based supervised deep
learning model, ASPER, which extracts syntactic patterns between entities
exhibiting a given semantic relation in the sentential context. We validate the
performance of ASPER on three distinct semantic relations -- hyponym-hypernym,
cause-effect, and meronym-holonym on six datasets. Experimental results show
that for all these semantic relations, ASPER can automatically identify a
collection of syntactic patterns reflecting the existence of such a relation
between a pair of entities in a sentence. In comparison to the existing
methodologies of syntactic pattern extraction, ASPER's performance is
substantially superior