2,365 research outputs found
Conditional Random Field Autoencoders for Unsupervised Structured Prediction
We introduce a framework for unsupervised learning of structured predictors
with overlapping, global features. Each input's latent representation is
predicted conditional on the observable data using a feature-rich conditional
random field. Then a reconstruction of the input is (re)generated, conditional
on the latent structure, using models for which maximum likelihood estimation
has a closed-form. Our autoencoder formulation enables efficient learning
without making unrealistic independence assumptions or restricting the kinds of
features that can be used. We illustrate insightful connections to traditional
autoencoders, posterior regularization and multi-view learning. We show
competitive results with instantiations of the model for two canonical NLP
tasks: part-of-speech induction and bitext word alignment, and show that
training our model can be substantially more efficient than comparable
feature-rich baselines
Deep learning for extracting protein-protein interactions from biomedical literature
State-of-the-art methods for protein-protein interaction (PPI) extraction are
primarily feature-based or kernel-based by leveraging lexical and syntactic
information. But how to incorporate such knowledge in the recent deep learning
methods remains an open question. In this paper, we propose a multichannel
dependency-based convolutional neural network model (McDepCNN). It applies one
channel to the embedding vector of each word in the sentence, and another
channel to the embedding vector of the head of the corresponding word.
Therefore, the model can use richer information obtained from different
channels. Experiments on two public benchmarking datasets, AIMed and BioInfer,
demonstrate that McDepCNN compares favorably to the state-of-the-art
rich-feature and single-kernel based methods. In addition, McDepCNN achieves
24.4% relative improvement in F1-score over the state-of-the-art methods on
cross-corpus evaluation and 12% improvement in F1-score over kernel-based
methods on "difficult" instances. These results suggest that McDepCNN
generalizes more easily over different corpora, and is capable of capturing
long distance features in the sentences.Comment: Accepted for publication in Proceedings of the 2017 Workshop on
Biomedical Natural Language Processing, 10 pages, 2 figures, 6 table
Mining Large-scale Event Knowledge from Web Text
AbstractThis paper addresses the problem of automatic acquisition of semantic relations between events. While previous works on semantic relation automatic acquisition relied on annotated text corpus, it is still unclear how to develop more generic methods to meet the needs of identifying related event pairs and extracting event-arguments (especially the predicate, subject and object). Motivated by this limitation, we develop a three-phased approach that acquires causality from the Web text. First, we use explicit connective markers (such as “because”) as linguistic cues to discover causal related events. Next, we extract the event-arguments based on local dependency parse trees of event expressions. At the last step, we propose a statistical model to measure the potential causal relations. The results of our empirical evaluations on a large-scale Web text corpus show that (a) the use of local dependency tree extensively improves both the accuracy and recall of event-arguments extraction task, and (b) our measure improves the traditional PMI method
Inferring Interpersonal Relations in Narrative Summaries
Characterizing relationships between people is fundamental for the
understanding of narratives. In this work, we address the problem of inferring
the polarity of relationships between people in narrative summaries. We
formulate the problem as a joint structured prediction for each narrative, and
present a model that combines evidence from linguistic and semantic features,
as well as features based on the structure of the social community in the text.
We also provide a clustering-based approach that can exploit regularities in
narrative types. e.g., learn an affinity for love-triangles in romantic
stories. On a dataset of movie summaries from Wikipedia, our structured models
provide more than a 30% error-reduction over a competitive baseline that
considers pairs of characters in isolation
Content Differences in Syntactic and Semantic Representations
Syntactic analysis plays an important role in semantic parsing, but the
nature of this role remains a topic of ongoing debate. The debate has been
constrained by the scarcity of empirical comparative studies between syntactic
and semantic schemes, which hinders the development of parsing methods informed
by the details of target schemes and constructions. We target this gap, and
take Universal Dependencies (UD) and UCCA as a test case. After abstracting
away from differences of convention or formalism, we find that most content
divergences can be ascribed to: (1) UCCA's distinction between a Scene and a
non-Scene; (2) UCCA's distinction between primary relations, secondary ones and
participants; (3) different treatment of multi-word expressions, and (4)
different treatment of inter-clause linkage. We further discuss the long tail
of cases where the two schemes take markedly different approaches. Finally, we
show that the proposed comparison methodology can be used for fine-grained
evaluation of UCCA parsing, highlighting both challenges and potential sources
for improvement. The substantial differences between the schemes suggest that
semantic parsers are likely to benefit downstream text understanding
applications beyond their syntactic counterparts.Comment: NAACL-HLT 2019 camera read
Using a shallow linguistic kernel for drug-drug interaction extraction
A drug–drug interaction (DDI) occurs when one drug influences the level or activity of another drug. Information Extraction (IE) techniques can provide health care professionals with an interesting way to reduce time spent reviewing the literature for potential drug–drug interactions. Nevertheless, no approach has been proposed to the problem of extracting DDIs in biomedical texts. In this article, we study whether a machine learning-based method is appropriate for DDI extraction in biomedical texts and whether the results provided are superior to those obtained from our previously proposed pattern-based approach [1]. The method proposed here for DDI extraction is based on a supervised machine learning technique, more specifically, the shallow linguistic kernel proposed in Giuliano et al. (2006) [2]. Since no benchmark corpus was available to evaluate our approach to DDI extraction, we created the first such corpus, DrugDDI, annotated with 3169 DDIs. We performed several experiments varying the configuration parameters of the shallow linguistic kernel. The model that maximizes the F-measure was evaluated on the test data of the DrugDDI corpus, achieving a precision of 51.03%, a recall of 72.82% and an F-measure of 60.01%.
To the best of our knowledge, this work has proposed the first full solution for the automatic extraction of DDIs from biomedical texts. Our study confirms that the shallow linguistic kernel outperforms our previous pattern-based approach. Additionally, it is our hope that the DrugDDI corpus will allow researchers to explore new solutions to the DDI extraction problem.This study was funded by the Projects MA2VICMR (S2009/TIC-1542) and MULTIMEDICA (TIN2010-20644-C03-01).Publicad
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