1,591 research outputs found
Recursive Neural Conditional Random Fields for Aspect-based Sentiment Analysis
In aspect-based sentiment analysis, extracting aspect terms along with the
opinions being expressed from user-generated content is one of the most
important subtasks. Previous studies have shown that exploiting connections
between aspect and opinion terms is promising for this task. In this paper, we
propose a novel joint model that integrates recursive neural networks and
conditional random fields into a unified framework for explicit aspect and
opinion terms co-extraction. The proposed model learns high-level
discriminative features and double propagate information between aspect and
opinion terms, simultaneously. Moreover, it is flexible to incorporate
hand-crafted features into the proposed model to further boost its information
extraction performance. Experimental results on the SemEval Challenge 2014
dataset show the superiority of our proposed model over several baseline
methods as well as the winning systems of the challenge
A Variational Approach to Weakly Supervised Document-Level Multi-Aspect Sentiment Classification
In this paper, we propose a variational approach to weakly supervised
document-level multi-aspect sentiment classification. Instead of using
user-generated ratings or annotations provided by domain experts, we use
target-opinion word pairs as "supervision." These word pairs can be extracted
by using dependency parsers and simple rules. Our objective is to predict an
opinion word given a target word while our ultimate goal is to learn a
sentiment polarity classifier to predict the sentiment polarity of each aspect
given a document. By introducing a latent variable, i.e., the sentiment
polarity, to the objective function, we can inject the sentiment polarity
classifier to the objective via the variational lower bound. We can learn a
sentiment polarity classifier by optimizing the lower bound. We show that our
method can outperform weakly supervised baselines on TripAdvisor and
BeerAdvocate datasets and can be comparable to the state-of-the-art supervised
method with hundreds of labels per aspect.Comment: Accepted by NAACL-HLT 201
Dataset Construction via Attention for Aspect Term Extraction with Distant Supervision
Aspect Term Extraction (ATE) detects opinionated aspect terms in sentences or
text spans, with the end goal of performing aspect-based sentiment analysis.
The small amount of available datasets for supervised ATE and the fact that
they cover only a few domains raise the need for exploiting other data sources
in new and creative ways. Publicly available review corpora contain a plethora
of opinionated aspect terms and cover a larger domain spectrum. In this paper,
we first propose a method for using such review corpora for creating a new
dataset for ATE. Our method relies on an attention mechanism to select
sentences that have a high likelihood of containing actual opinionated aspects.
We thus improve the quality of the extracted aspects. We then use the
constructed dataset to train a model and perform ATE with distant supervision.
By evaluating on human annotated datasets, we prove that our method achieves a
significantly improved performance over various unsupervised and supervised
baselines. Finally, we prove that sentence selection matters when it comes to
creating new datasets for ATE. Specifically, we show that, using a set of
selected sentences leads to higher ATE performance compared to using the whole
sentence set
Unsupervised Aspect Term Extraction with B-LSTM & CRF using Automatically Labelled Datasets
Aspect Term Extraction (ATE) identifies opinionated aspect terms in texts and
is one of the tasks in the SemEval Aspect Based Sentiment Analysis (ABSA)
contest. The small amount of available datasets for supervised ATE and the
costly human annotation for aspect term labelling give rise to the need for
unsupervised ATE. In this paper, we introduce an architecture that achieves
top-ranking performance for supervised ATE. Moreover, it can be used
efficiently as feature extractor and classifier for unsupervised ATE. Our
second contribution is a method to automatically construct datasets for ATE. We
train a classifier on our automatically labelled datasets and evaluate it on
the human annotated SemEval ABSA test sets. Compared to a strong rule-based
baseline, we obtain a dramatically higher F-score and attain precision values
above 80%. Our unsupervised method beats the supervised ABSA baseline from
SemEval, while preserving high precision scores.Comment: 9 pages, 3 figures, 2 tables 8th Workshop on Computational Approaches
to Subjectivity, Sentiment & Social Media Analysis (WASSA), EMNLP 201
Deep Learning for Sentiment Analysis : A Survey
Deep learning has emerged as a powerful machine learning technique that
learns multiple layers of representations or features of the data and produces
state-of-the-art prediction results. Along with the success of deep learning in
many other application domains, deep learning is also popularly used in
sentiment analysis in recent years. This paper first gives an overview of deep
learning and then provides a comprehensive survey of its current applications
in sentiment analysis.Comment: 34 pages, 9 figures, 2 table
Targeted Sentiment Analysis: A Data-Driven Categorization
Targeted sentiment analysis (TSA), also known as aspect based sentiment
analysis (ABSA), aims at detecting fine-grained sentiment polarity towards
targets in a given opinion document. Due to the lack of labeled datasets and
effective technology, TSA had been intractable for many years. The newly
released datasets and the rapid development of deep learning technologies are
key enablers for the recent significant progress made in this area. However,
the TSA tasks have been defined in various ways with different understandings
towards basic concepts like `target' and `aspect'. In this paper, we categorize
the different tasks and highlight the differences in the available datasets and
their specific tasks. We then further discuss the challenges related to data
collection and data annotation which are overlooked in many previous studies.Comment: Draf
Information Extraction from Scientific Literature for Method Recommendation
As a research community grows, more and more papers are published each year.
As a result there is increasing demand for improved methods for finding
relevant papers, automatically understanding the key ideas and recommending
potential methods for a target problem. Despite advances in search engines, it
is still hard to identify new technologies according to a researcher's need.
Due to the large variety of domains and extremely limited annotated resources,
there has been relatively little work on leveraging natural language processing
in scientific recommendation. In this proposal, we aim at making scientific
recommendations by extracting scientific terms from a large collection of
scientific papers and organizing the terms into a knowledge graph. In
preliminary work, we trained a scientific term extractor using a small amount
of annotated data and obtained state-of-the-art performance by leveraging large
amount of unannotated papers through applying multiple semi-supervised
approaches. We propose to construct a knowledge graph in a way that can make
minimal use of hand annotated data, using only the extracted terms,
unsupervised relational signals such as co-occurrence, and structural external
resources such as Wikipedia. Latent relations between scientific terms can be
learned from the graph. Recommendations will be made through graph inference
for both observed and unobserved relational pairs.Comment: Thesis Proposal. arXiv admin note: text overlap with arXiv:1708.0607
PoD: Positional Dependency-Based Word Embedding for Aspect Term Extraction
Dependency context-based word embedding jointly learns the representations of
word and dependency context, and has been proved effective in aspect term
extraction. In this paper, we design the positional dependency-based word
embedding (PoD) which considers both dependency context and positional context
for aspect term extraction. Specifically, the positional context is modeled via
relative position encoding. Besides, we enhance the dependency context by
integrating more lexical information (e.g., POS tags) along dependency paths.
Experiments on SemEval 2014/2015/2016 datasets show that our approach
outperforms other embedding methods in aspect term extraction.Comment: 7 pages, 1 figure, 3 table
Controlled CNN-based Sequence Labeling for Aspect Extraction
One key task of fine-grained sentiment analysis on reviews is to extract
aspects or features that users have expressed opinions on. This paper focuses
on supervised aspect extraction using a modified CNN called controlled CNN
(Ctrl). The modified CNN has two types of control modules. Through asynchronous
parameter updating, it prevents over-fitting and boosts CNN's performance
significantly. This model achieves state-of-the-art results on standard aspect
extraction datasets. To the best of our knowledge, this is the first paper to
apply control modules to aspect extraction
A Variational Approach to Unsupervised Sentiment Analysis
In this paper, we propose a variational approach to unsupervised sentiment
analysis. Instead of using ground truth provided by domain experts, we use
target-opinion word pairs as a supervision signal. For example, in a document
snippet "the room is big," (room, big) is a target-opinion word pair. These
word pairs can be extracted by using dependency parsers and simple rules. Our
objective function is to predict an opinion word given a target word while our
ultimate goal is to learn a sentiment classifier. By introducing a latent
variable, i.e., the sentiment polarity, to the objective function, we can
inject the sentiment classifier to the objective function via the evidence
lower bound. We can learn a sentiment classifier by optimizing the lower bound.
We also impose sophisticated constraints on opinion words as regularization
which encourages that if two documents have similar (dissimilar) opinion words,
the sentiment classifiers should produce similar (different) probability
distribution. We apply our method to sentiment analysis on customer reviews and
clinical narratives. The experiment results show our method can outperform
unsupervised baselines in sentiment analysis task on both domains, and our
method obtains comparable results to the supervised method with hundreds of
labels per aspect in customer reviews domain, and obtains comparable results to
supervised methods in clinical narratives domain.Comment: arXiv admin note: substantial text overlap with arXiv:1904.0505
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