43,713 research outputs found
Exploiting Sentence Embedding for Medical Question Answering
Despite the great success of word embedding, sentence embedding remains a
not-well-solved problem. In this paper, we present a supervised learning
framework to exploit sentence embedding for the medical question answering
task. The learning framework consists of two main parts: 1) a sentence
embedding producing module, and 2) a scoring module. The former is developed
with contextual self-attention and multi-scale techniques to encode a sentence
into an embedding tensor. This module is shortly called Contextual
self-Attention Multi-scale Sentence Embedding (CAMSE). The latter employs two
scoring strategies: Semantic Matching Scoring (SMS) and Semantic Association
Scoring (SAS). SMS measures similarity while SAS captures association between
sentence pairs: a medical question concatenated with a candidate choice, and a
piece of corresponding supportive evidence. The proposed framework is examined
by two Medical Question Answering(MedicalQA) datasets which are collected from
real-world applications: medical exam and clinical diagnosis based on
electronic medical records (EMR). The comparison results show that our proposed
framework achieved significant improvements compared to competitive baseline
approaches. Additionally, a series of controlled experiments are also conducted
to illustrate that the multi-scale strategy and the contextual self-attention
layer play important roles for producing effective sentence embedding, and the
two kinds of scoring strategies are highly complementary to each other for
question answering problems.Comment: 8 page
A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts
Sentiment analysis seeks to identify the viewpoint(s) underlying a text span;
an example application is classifying a movie review as "thumbs up" or "thumbs
down". To determine this sentiment polarity, we propose a novel
machine-learning method that applies text-categorization techniques to just the
subjective portions of the document. Extracting these portions can be
implemented using efficient techniques for finding minimum cuts in graphs; this
greatly facilitates incorporation of cross-sentence contextual constraints.Comment: Data available at
http://www.cs.cornell.edu/people/pabo/movie-review-data
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