14,637 research outputs found
Variational Semi-supervised Aspect-term Sentiment Analysis via Transformer
Aspect-term sentiment analysis (ATSA) is a longstanding challenge in natural
language understanding. It requires fine-grained semantical reasoning about a
target entity appeared in the text. As manual annotation over the aspects is
laborious and time-consuming, the amount of labeled data is limited for
supervised learning. This paper proposes a semi-supervised method for the ATSA
problem by using the Variational Autoencoder based on Transformer (VAET), which
models the latent distribution via variational inference. By disentangling the
latent representation into the aspect-specific sentiment and the lexical
context, our method induces the underlying sentiment prediction for the
unlabeled data, which then benefits the ATSA classifier. Our method is
classifier agnostic, i.e., the classifier is an independent module and various
advanced supervised models can be integrated. Experimental results are obtained
on the SemEval 2014 task 4 and show that our method is effective with four
classical classifiers. The proposed method outperforms two general
semisupervised methods and achieves state-of-the-art performance.Comment: Accepted by CoNLL 201
GLoMo: Unsupervisedly Learned Relational Graphs as Transferable Representations
Modern deep transfer learning approaches have mainly focused on learning
generic feature vectors from one task that are transferable to other tasks,
such as word embeddings in language and pretrained convolutional features in
vision. However, these approaches usually transfer unary features and largely
ignore more structured graphical representations. This work explores the
possibility of learning generic latent relational graphs that capture
dependencies between pairs of data units (e.g., words or pixels) from
large-scale unlabeled data and transferring the graphs to downstream tasks. Our
proposed transfer learning framework improves performance on various tasks
including question answering, natural language inference, sentiment analysis,
and image classification. We also show that the learned graphs are generic
enough to be transferred to different embeddings on which the graphs have not
been trained (including GloVe embeddings, ELMo embeddings, and task-specific
RNN hidden unit), or embedding-free units such as image pixels
Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research
Sentiment analysis as a field has come a long way since it was first
introduced as a task nearly 20 years ago. It has widespread commercial
applications in various domains like marketing, risk management, market
research, and politics, to name a few. Given its saturation in specific
subtasks -- such as sentiment polarity classification -- and datasets, there is
an underlying perception that this field has reached its maturity. In this
article, we discuss this perception by pointing out the shortcomings and
under-explored, yet key aspects of this field that are necessary to attain true
sentiment understanding. We analyze the significant leaps responsible for its
current relevance. Further, we attempt to chart a possible course for this
field that covers many overlooked and unanswered questions.Comment: Published in the IEEE Transactions on Affective Computing (TAFFC
Boost Phrase-level Polarity Labelling with Review-level Sentiment Classification
Sentiment analysis on user reviews helps to keep track of user reactions
towards products, and make advices to users about what to buy. State-of-the-art
review-level sentiment classification techniques could give pretty good
precisions of above 90%. However, current phrase-level sentiment analysis
approaches might only give sentiment polarity labelling precisions of around
70%~80%, which is far from satisfaction and restricts its application in many
practical tasks. In this paper, we focus on the problem of phrase-level
sentiment polarity labelling and attempt to bridge the gap between phrase-level
and review-level sentiment analysis. We investigate the inconsistency between
the numerical star ratings and the sentiment orientation of textual user
reviews. Although they have long been treated as identical, which serves as a
basic assumption in previous work, we find that this assumption is not
necessarily true. We further propose to leverage the results of review-level
sentiment classification to boost the performance of phrase-level polarity
labelling using a novel constrained convex optimization framework. Besides, the
framework is capable of integrating various kinds of information sources and
heuristics, while giving the global optimal solution due to its convexity.
Experimental results on both English and Chinese reviews show that our
framework achieves high labelling precisions of up to 89%, which is a
significant improvement from current approaches
User-Guided Aspect Classification for Domain-Specific Texts
Aspect classification, identifying aspects of text segments, facilitates
numerous applications, such as sentiment analysis and review summarization. To
alleviate the human effort on annotating massive texts, in this paper, we study
the problem of classifying aspects based on only a few user-provided seed words
for pre-defined aspects. The major challenge lies in how to handle the noisy
misc aspect, which is designed for texts without any pre-defined aspects. Even
domain experts have difficulties to nominate seed words for the misc aspect,
making existing seed-driven text classification methods not applicable. We
propose a novel framework, ARYA, which enables mutual enhancements between
pre-defined aspects and the misc aspect via iterative classifier training and
seed updating. Specifically, it trains a classifier for pre-defined aspects and
then leverages it to induce the supervision for the misc aspect. The prediction
results of the misc aspect are later utilized to filter out noisy seed words
for pre-defined aspects. Experiments in two domains demonstrate the superior
performance of our proposed framework, as well as the necessity and importance
of properly modeling the misc aspect
DRTS Parsing with Structure-Aware Encoding and Decoding
Discourse representation tree structure (DRTS) parsing is a novel semantic
parsing task which has been concerned most recently. State-of-the-art
performance can be achieved by a neural sequence-to-sequence model, treating
the tree construction as an incremental sequence generation problem. Structural
information such as input syntax and the intermediate skeleton of the partial
output has been ignored in the model, which could be potentially useful for the
DRTS parsing. In this work, we propose a structural-aware model at both the
encoder and decoder phase to integrate the structural information, where graph
attention network (GAT) is exploited for effectively modeling. Experimental
results on a benchmark dataset show that our proposed model is effective and
can obtain the best performance in the literature.Comment: ACL202
Semi-supervised Convolutional Neural Networks for Text Categorization via Region Embedding
This paper presents a new semi-supervised framework with convolutional neural
networks (CNNs) for text categorization. Unlike the previous approaches that
rely on word embeddings, our method learns embeddings of small text regions
from unlabeled data for integration into a supervised CNN. The proposed scheme
for embedding learning is based on the idea of two-view semi-supervised
learning, which is intended to be useful for the task of interest even though
the training is done on unlabeled data. Our models achieve better results than
previous approaches on sentiment classification and topic classification tasks.Comment: v1 has a different title, and the results there are obsolete. The
current version is to appear in NIPS 201
Left-Center-Right Separated Neural Network for Aspect-based Sentiment Analysis with Rotatory Attention
Deep learning techniques have achieved success in aspect-based sentiment
analysis in recent years. However, there are two important issues that still
remain to be further studied, i.e., 1) how to efficiently represent the target
especially when the target contains multiple words; 2) how to utilize the
interaction between target and left/right contexts to capture the most
important words in them. In this paper, we propose an approach, called
left-center-right separated neural network with rotatory attention (LCR-Rot),
to better address the two problems. Our approach has two characteristics: 1) it
has three separated LSTMs, i.e., left, center and right LSTMs, corresponding to
three parts of a review (left context, target phrase and right context); 2) it
has a rotatory attention mechanism which models the relation between target and
left/right contexts. The target2context attention is used to capture the most
indicative sentiment words in left/right contexts. Subsequently, the
context2target attention is used to capture the most important word in the
target. This leads to a two-side representation of the target: left-aware
target and right-aware target. We compare our approach on three benchmark
datasets with ten related methods proposed recently. The results show that our
approach significantly outperforms the state-of-the-art techniques
Weakly-Supervised Neural Text Classification
Deep neural networks are gaining increasing popularity for the classic text
classification task, due to their strong expressive power and less requirement
for feature engineering. Despite such attractiveness, neural text
classification models suffer from the lack of training data in many real-world
applications. Although many semi-supervised and weakly-supervised text
classification models exist, they cannot be easily applied to deep neural
models and meanwhile support limited supervision types. In this paper, we
propose a weakly-supervised method that addresses the lack of training data in
neural text classification. Our method consists of two modules: (1) a
pseudo-document generator that leverages seed information to generate
pseudo-labeled documents for model pre-training, and (2) a self-training module
that bootstraps on real unlabeled data for model refinement. Our method has the
flexibility to handle different types of weak supervision and can be easily
integrated into existing deep neural models for text classification. We have
performed extensive experiments on three real-world datasets from different
domains. The results demonstrate that our proposed method achieves inspiring
performance without requiring excessive training data and outperforms baseline
methods significantly.Comment: CIKM 2018 Full Pape
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
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