636 research outputs found
Convolutional Neural Networks for Sentence Classification
We report on a series of experiments with convolutional neural networks (CNN)
trained on top of pre-trained word vectors for sentence-level classification
tasks. We show that a simple CNN with little hyperparameter tuning and static
vectors achieves excellent results on multiple benchmarks. Learning
task-specific vectors through fine-tuning offers further gains in performance.
We additionally propose a simple modification to the architecture to allow for
the use of both task-specific and static vectors. The CNN models discussed
herein improve upon the state of the art on 4 out of 7 tasks, which include
sentiment analysis and question classification.Comment: To appear in EMNLP 201
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
Scientific Information Extraction with Semi-supervised Neural Tagging
This paper addresses the problem of extracting keyphrases from scientific
articles and categorizing them as corresponding to a task, process, or
material. We cast the problem as sequence tagging and introduce semi-supervised
methods to a neural tagging model, which builds on recent advances in named
entity recognition. Since annotated training data is scarce in this domain, we
introduce a graph-based semi-supervised algorithm together with a data
selection scheme to leverage unannotated articles. Both inductive and
transductive semi-supervised learning strategies outperform state-of-the-art
information extraction performance on the 2017 SemEval Task 10 ScienceIE task.Comment: accepted by EMNLP 201
Fidelity-Weighted Learning
Training deep neural networks requires many training samples, but in practice
training labels are expensive to obtain and may be of varying quality, as some
may be from trusted expert labelers while others might be from heuristics or
other sources of weak supervision such as crowd-sourcing. This creates a
fundamental quality versus-quantity trade-off in the learning process. Do we
learn from the small amount of high-quality data or the potentially large
amount of weakly-labeled data? We argue that if the learner could somehow know
and take the label-quality into account when learning the data representation,
we could get the best of both worlds. To this end, we propose
"fidelity-weighted learning" (FWL), a semi-supervised student-teacher approach
for training deep neural networks using weakly-labeled data. FWL modulates the
parameter updates to a student network (trained on the task we care about) on a
per-sample basis according to the posterior confidence of its label-quality
estimated by a teacher (who has access to the high-quality labels). Both
student and teacher are learned from the data. We evaluate FWL on two tasks in
information retrieval and natural language processing where we outperform
state-of-the-art alternative semi-supervised methods, indicating that our
approach makes better use of strong and weak labels, and leads to better
task-dependent data representations.Comment: Published as a conference paper at ICLR 201
- …