• Publications
  • Influence
Syntactic Search by Example
TLDR
A light-weight query language is introduced that does not require the user to know the details of the underlying syntactic representations, and instead to query the corpus by providing an example sentence coupled with simple markup. Expand
Interactive Extractive Search over Biomedical Corpora
TLDR
A light-weight query language is introduced that does not require the user to know the details of the underlying linguistic representations, and instead to query the corpus by providing an example sentence coupled with simple markup, allowing for rapid exploration, development and refinement of user queries. Expand
Template Kernels for Dependency Parsing
TLDR
This work presents a novel kernel which facilitates efficient parsing with feature representations corresponding to a much larger set of combinations, and integrates into a parse reranking system and demonstrates its effectiveness on four languages from the CoNLL-X shared task. Expand
Bootstrapping Relation Extractors using Syntactic Search by Examples
TLDR
This work proposes a process for bootstrapping training datasets which can be performed quickly by non-NLP-experts and takes advantage of search engines over syntactic-graphs to obtain positive examples by searching for sentences that are syntactically similar to user input examples. Expand
Neural Extractive Search
TLDR
The goals of this paper are to concisely introduce the extractive-search paradigm; and to demonstrate a prototype neural retrieval system for extractive search and its benefits and potential. Expand