5,606 research outputs found
Improving Aspect Term Extraction with Bidirectional Dependency Tree Representation
Aspect term extraction is one of the important subtasks in aspect-based
sentiment analysis. Previous studies have shown that using dependency tree
structure representation is promising for this task. However, most dependency
tree structures involve only one directional propagation on the dependency
tree. In this paper, we first propose a novel bidirectional dependency tree
network to extract dependency structure features from the given sentences. The
key idea is to explicitly incorporate both representations gained separately
from the bottom-up and top-down propagation on the given dependency syntactic
tree. An end-to-end framework is then developed to integrate the embedded
representations and BiLSTM plus CRF to learn both tree-structured and
sequential features to solve the aspect term extraction problem. Experimental
results demonstrate that the proposed model outperforms state-of-the-art
baseline models on four benchmark SemEval datasets.Comment: Accepted by TASL
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
Deep Learning applied to NLP
Convolutional Neural Network (CNNs) are typically associated with Computer
Vision. CNNs are responsible for major breakthroughs in Image Classification
and are the core of most Computer Vision systems today. More recently CNNs have
been applied to problems in Natural Language Processing and gotten some
interesting results. In this paper, we will try to explain the basics of CNNs,
its different variations and how they have been applied to NLP
Structure Regularized Bidirectional Recurrent Convolutional Neural Network for Relation Classification
Relation classification is an important semantic processing task in the field
of natural language processing (NLP). In this paper, we present a novel model,
Structure Regularized Bidirectional Recurrent Convolutional Neural
Network(SR-BRCNN), to classify the relation of two entities in a sentence, and
the new dataset of Chinese Sanwen for named entity recognition and relation
classification. Some state-of-the-art systems concentrate on modeling the
shortest dependency path (SDP) between two entities leveraging convolutional or
recurrent neural networks. We further explore how to make full use of the
dependency relations information in the SDP and how to improve the model by the
method of structure regularization. We propose a structure regularized model to
learn relation representations along the SDP extracted from the forest formed
by the structure regularized dependency tree, which benefits reducing the
complexity of the whole model and helps improve the score by 10.3.
Experimental results show that our method outperforms the state-of-the-art
approaches on the Chinese Sanwen task and performs as well on the SemEval-2010
Task 8 dataset\footnote{The Chinese Sanwen corpus this paper developed and used
will be released in the further.Comment: arXiv admin note: text overlap with arXiv:1411.6243 by other author
Reasoning with Latent Structure Refinement for Document-Level Relation Extraction
Document-level relation extraction requires integrating information within
and across multiple sentences of a document and capturing complex interactions
between inter-sentence entities. However, effective aggregation of relevant
information in the document remains a challenging research question. Existing
approaches construct static document-level graphs based on syntactic trees,
co-references or heuristics from the unstructured text to model the
dependencies. Unlike previous methods that may not be able to capture rich
non-local interactions for inference, we propose a novel model that empowers
the relational reasoning across sentences by automatically inducing the latent
document-level graph. We further develop a refinement strategy, which enables
the model to incrementally aggregate relevant information for multi-hop
reasoning. Specifically, our model achieves an F1 score of 59.05 on a
large-scale document-level dataset (DocRED), significantly improving over the
previous results, and also yields new state-of-the-art results on the CDR and
GDA dataset. Furthermore, extensive analyses show that the model is able to
discover more accurate inter-sentence relations.Comment: Appeared in the proceedings of ACL 2020 (Long paper
A Sequential Model for Classifying Temporal Relations between Intra-Sentence Events
We present a sequential model for temporal relation classification between
intra-sentence events. The key observation is that the overall syntactic
structure and compositional meanings of the multi-word context between events
are important for distinguishing among fine-grained temporal relations.
Specifically, our approach first extracts a sequence of context words that
indicates the temporal relation between two events, which well align with the
dependency path between two event mentions. The context word sequence, together
with a parts-of-speech tag sequence and a dependency relation sequence that are
generated corresponding to the word sequence, are then provided as input to
bidirectional recurrent neural network (LSTM) models. The neural nets learn
compositional syntactic and semantic representations of contexts surrounding
the two events and predict the temporal relation between them. Evaluation of
the proposed approach on TimeBank corpus shows that sequential modeling is
capable of accurately recognizing temporal relations between events, which
outperforms a neural net model using various discrete features as input that
imitates previous feature based models.Comment: EMNLP 201
More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction
Relational facts are an important component of human knowledge, which are
hidden in vast amounts of text. In order to extract these facts from text,
people have been working on relation extraction (RE) for years. From early
pattern matching to current neural networks, existing RE methods have achieved
significant progress. Yet with explosion of Web text and emergence of new
relations, human knowledge is increasing drastically, and we thus require
"more" from RE: a more powerful RE system that can robustly utilize more data,
efficiently learn more relations, easily handle more complicated context, and
flexibly generalize to more open domains. In this paper, we look back at
existing RE methods, analyze key challenges we are facing nowadays, and show
promising directions towards more powerful RE. We hope our view can advance
this field and inspire more efforts in the community
Neural Metric Learning for Fast End-to-End Relation Extraction
Relation extraction (RE) is an indispensable information extraction task in
several disciplines. RE models typically assume that named entity recognition
(NER) is already performed in a previous step by another independent model.
Several recent efforts, under the theme of end-to-end RE, seek to exploit
inter-task correlations by modeling both NER and RE tasks jointly. Earlier work
in this area commonly reduces the task to a table-filling problem wherein an
additional expensive decoding step involving beam search is applied to obtain
globally consistent cell labels. In efforts that do not employ table-filling,
global optimization in the form of CRFs with Viterbi decoding for the NER
component is still necessary for competitive performance. We introduce a novel
neural architecture utilizing the table structure, based on repeated
applications of 2D convolutions for pooling local dependency and metric-based
features, that improves on the state-of-the-art without the need for global
optimization. We validate our model on the ADE and CoNLL04 datasets for
end-to-end RE and demonstrate gain (in F-score) over prior best
results with training and testing times that are seven to ten times faster ---
the latter highly advantageous for time-sensitive end user applications
Aspect Detection using Word and Char Embeddings with (Bi)LSTM and CRF
We proposed a~new accurate aspect extraction method that makes use of both
word and character-based embeddings. We have conducted experiments of various
models of aspect extraction using LSTM and BiLSTM including CRF enhancement on
five different pre-trained word embeddings extended with character embeddings.
The results revealed that BiLSTM outperforms regular LSTM, but also word
embedding coverage in train and test sets profoundly impacted aspect detection
performance. Moreover, the additional CRF layer consistently improves the
results across different models and text embeddings. Summing up, we obtained
state-of-the-art F-score results for SemEval Restaurants (85%) and Laptops
(80%).Comment: IEEE AIK
Comprehensive Analysis of Aspect Term Extraction Methods using Various Text Embeddings
Recently, a variety of model designs and methods have blossomed in the
context of the sentiment analysis domain. However, there is still a lack of
wide and comprehensive studies of aspect-based sentiment analysis (ABSA). We
want to fill this gap and propose a comparison with ablation analysis of aspect
term extraction using various text embedding methods. We particularly focused
on architectures based on long short-term memory (LSTM) with optional
conditional random field (CRF) enhancement using different pre-trained word
embeddings. Moreover, we analyzed the influence on the performance of extending
the word vectorization step with character embedding. The experimental results
on SemEval datasets revealed that not only does bi-directional long short-term
memory (BiLSTM) outperform regular LSTM, but also word embedding coverage and
its source highly affect aspect detection performance. An additional CRF layer
consistently improves the results as well
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