782 research outputs found
Improving Broad-Coverage Medical Entity Linking with Semantic Type Prediction and Large-Scale Datasets
Medical entity linking is the task of identifying and standardizing medical
concepts referred to in an unstructured text. Most of the existing methods
adopt a three-step approach of (1) detecting mentions, (2) generating a list of
candidate concepts, and finally (3) picking the best concept among them. In
this paper, we probe into alleviating the problem of overgeneration of
candidate concepts in the candidate generation module, the most under-studied
component of medical entity linking. For this, we present MedType, a fully
modular system that prunes out irrelevant candidate concepts based on the
predicted semantic type of an entity mention. We incorporate MedType into five
off-the-shelf toolkits for medical entity linking and demonstrate that it
consistently improves entity linking performance across several benchmark
datasets. To address the dearth of annotated training data for medical entity
linking, we present WikiMed and PubMedDS, two large-scale medical entity
linking datasets, and demonstrate that pre-training MedType on these datasets
further improves entity linking performance. We make our source code and
datasets publicly available for medical entity linking research.Comment: 35 page
RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information
Distantly-supervised Relation Extraction (RE) methods train an extractor by
automatically aligning relation instances in a Knowledge Base (KB) with
unstructured text. In addition to relation instances, KBs often contain other
relevant side information, such as aliases of relations (e.g., founded and
co-founded are aliases for the relation founderOfCompany). RE models usually
ignore such readily available side information. In this paper, we propose
RESIDE, a distantly-supervised neural relation extraction method which utilizes
additional side information from KBs for improved relation extraction. It uses
entity type and relation alias information for imposing soft constraints while
predicting relations. RESIDE employs Graph Convolution Networks (GCN) to encode
syntactic information from text and improves performance even when limited side
information is available. Through extensive experiments on benchmark datasets,
we demonstrate RESIDE's effectiveness. We have made RESIDE's source code
available to encourage reproducible research.Comment: 10 pages, 6 figures, EMNLP 201
Analysing the Extent of Misinformation in Cancer Related Tweets
Twitter has become one of the most sought after places to discuss a wide
variety of topics, including medically relevant issues such as cancer. This
helps spread awareness regarding the various causes, cures and prevention
methods of cancer. However, no proper analysis has been performed, which
discusses the validity of such claims. In this work, we aim to tackle the
misinformation spread in such platforms. We collect and present a dataset
regarding tweets which talk specifically about cancer and propose an
attention-based deep learning model for automated detection of misinformation
along with its spread. We then do a comparative analysis of the linguistic
variation in the text corresponding to misinformation and truth. This analysis
helps us gather relevant insights on various social aspects related to
misinformed tweets.Comment: Proceedings of the 14th International Conference on Web and Social
Media (ICWSM-20
Calibrating Likelihoods towards Consistency in Summarization Models
Despite the recent advances in abstractive text summarization, current
summarization models still suffer from generating factually inconsistent
summaries, reducing their utility for real-world application. We argue that the
main reason for such behavior is that the summarization models trained with
maximum likelihood objective assign high probability to plausible sequences
given the context, but they often do not accurately rank sequences by their
consistency. In this work, we solve this problem by calibrating the likelihood
of model generated sequences to better align with a consistency metric measured
by natural language inference (NLI) models. The human evaluation study and
automatic metrics show that the calibrated models generate more consistent and
higher-quality summaries. We also show that the models trained using our method
return probabilities that are better aligned with the NLI scores, which
significantly increase reliability of summarization models
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