30 research outputs found
Joint Entity Extraction and Assertion Detection for Clinical Text
Negative medical findings are prevalent in clinical reports, yet
discriminating them from positive findings remains a challenging task for
information extraction. Most of the existing systems treat this task as a
pipeline of two separate tasks, i.e., named entity recognition (NER) and
rule-based negation detection. We consider this as a multi-task problem and
present a novel end-to-end neural model to jointly extract entities and
negations. We extend a standard hierarchical encoder-decoder NER model and
first adopt a shared encoder followed by separate decoders for the two tasks.
This architecture performs considerably better than the previous rule-based and
machine learning-based systems. To overcome the problem of increased parameter
size especially for low-resource settings, we propose the Conditional Softmax
Shared Decoder architecture which achieves state-of-art results for NER and
negation detection on the 2010 i2b2/VA challenge dataset and a proprietary
de-identified clinical dataset.Comment: Accepted at the 57th Annual Meeting of the Association for
Computational Linguistics (ACL 2019
Better Document-level Sentiment Analysis from RST Discourse Parsing
Discourse structure is the hidden link between surface features and
document-level properties, such as sentiment polarity. We show that the
discourse analyses produced by Rhetorical Structure Theory (RST) parsers can
improve document-level sentiment analysis, via composition of local information
up the discourse tree. First, we show that reweighting discourse units
according to their position in a dependency representation of the rhetorical
structure can yield substantial improvements on lexicon-based sentiment
analysis. Next, we present a recursive neural network over the RST structure,
which offers significant improvements over classification-based methods.Comment: Published at Empirical Methods in Natural Language Processing (EMNLP
2015
Morphological Priors for Probabilistic Neural Word Embeddings
Word embeddings allow natural language processing systems to share
statistical information across related words. These embeddings are typically
based on distributional statistics, making it difficult for them to generalize
to rare or unseen words. We propose to improve word embeddings by incorporating
morphological information, capturing shared sub-word features. Unlike previous
work that constructs word embeddings directly from morphemes, we combine
morphological and distributional information in a unified probabilistic
framework, in which the word embedding is a latent variable. The morphological
information provides a prior distribution on the latent word embeddings, which
in turn condition a likelihood function over an observed corpus. This approach
yields improvements on intrinsic word similarity evaluations, and also in the
downstream task of part-of-speech tagging.Comment: Appeared at the Conference on Empirical Methods in Natural Language
Processing (EMNLP 2016, Austin
Relation Extraction using Explicit Context Conditioning
Relation Extraction (RE) aims to label relations between groups of marked
entities in raw text. Most current RE models learn context-aware
representations of the target entities that are then used to establish relation
between them. This works well for intra-sentence RE and we call them
first-order relations. However, this methodology can sometimes fail to capture
complex and long dependencies. To address this, we hypothesize that at times
two target entities can be explicitly connected via a context token. We refer
to such indirect relations as second-order relations and describe an efficient
implementation for computing them. These second-order relation scores are then
combined with first-order relation scores. Our empirical results show that the
proposed method leads to state-of-the-art performance over two biomedical
datasets.Comment: Accepted for Publication at NAACL 201
LATTE: Latent Type Modeling for Biomedical Entity Linking
Entity linking is the task of linking mentions of named entities in natural
language text, to entities in a curated knowledge-base. This is of significant
importance in the biomedical domain, where it could be used to semantically
annotate a large volume of clinical records and biomedical literature, to
standardized concepts described in an ontology such as Unified Medical Language
System (UMLS). We observe that with precise type information, entity
disambiguation becomes a straightforward task. However, fine-grained type
information is usually not available in biomedical domain. Thus, we propose
LATTE, a LATent Type Entity Linking model, that improves entity linking by
modeling the latent fine-grained type information about mentions and entities.
Unlike previous methods that perform entity linking directly between the
mentions and the entities, LATTE jointly does entity disambiguation, and latent
fine-grained type learning, without direct supervision. We evaluate our model
on two biomedical datasets: MedMentions, a large scale public dataset annotated
with UMLS concepts, and a de-identified corpus of dictated doctor's notes that
has been annotated with ICD concepts. Extensive experimental evaluation shows
our model achieves significant performance improvements over several
state-of-the-art techniques.Comment: AAAI 2020 Conferenc