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Found 8 papers, 0 papers with code
Learning what to read: Focused machine reading
In this work, we introduce a focused reading approach to guide the machine reading of biomedical literature towards what literature should be read to answer a biomedical query as efficiently as possible.
Inter-sentence Relation Extraction for Associating Biological Context with Events in Biomedical Texts
We present an analysis of the problem of identifying biological context and associating it with biochemical events in biomedical texts.
Understanding the Polarity of Events in the Biomedical Literature: Deep Learning vs. Linguistically-informed Methods
An important task in the machine reading of biochemical events expressed in biomedical texts is correctly reading the polarity, i. e., attributing whether the biochemical event is a promotion or an inhibition.
Neural Architectures for Biological Inter-Sentence Relation Extraction
We introduce a family of deep-learning architectures for inter-sentence relation extraction, i. e., relations where the participants are not necessarily in the same sentence.
Learning Open Domain Multi-hop Search Using Reinforcement Learning
We propose a method to teach an automated agent to learn how to search for multi-hop paths of relations between entities in an open domain.
Low Resource Causal Event Detection from Biomedical Literature
Recognizing causal precedence relations among the chemical interactions in biomedical literature is crucial to understanding the underlying biological mechanisms.
Neural-Guided Program Synthesis of Information Extraction Rules Using Self-Supervision
We propose a neural-based approach for rule synthesis designed to help bridge the gap between the interpretability, precision and maintainability exhibited by rule-based information extraction systems with the scalability and convenience of statistical information extraction systems.
A Human-machine Interface for Few-shot Rule Synthesis for Information Extraction
We propose a system that assists a user in constructing transparent information extraction models, consisting of patterns (or rules) written in a declarative language, through program synthesis. Users of our system can specify their requirements through the use of examples, which are collected with a search interface. The rule-synthesis system proposes rule candidates and the results of applying them on a textual corpus; the user has the option to accept the candidate, request another option, or adjust the examples provided to the system. Through an interactive evaluation, we show that our approach generates high-precision rules even in a 1-shot setting.
