Open AccessProceedings Article
Coupled Multi-Layer Attentions for Co-Extraction of Aspect and Opinion Terms.
Wenya Wang,Sinno Jialin Pan,Daniel Dahlmeier,Xiaokui Xiao +3 more
- pp 3316-3322
TLDR
A novel deep learning model, named coupled multi-layer attentions, where each layer consists of a couple of attentions with tensor operators that are learned interactively to dually propagate information between aspect terms and opinion terms.Abstract:
The task of aspect and opinion terms co-extraction aims to explicitly extract aspect terms describing features of an entity and opinion terms expressing emotions from user-generated texts. To achieve this task, one effective approach is to exploit relations between aspect terms and opinion terms by parsing syntactic structure for each sentence. However, this approach requires expensive effort for parsing and highly depends on the quality of the parsing results. In this paper, we offer a novel deep learning model, named coupled multi-layer attentions. The proposed model provides an end-to-end solution and does not require any parsers or other linguistic resources for preprocessing. Specifically, the proposed model is a multilayer attention network, where each layer consists of a couple of attentions with tensor operators. One attention is for extracting aspect terms, while the other is for extracting opinion terms. They are learned interactively to dually propagate information between aspect terms and opinion terms. Through multiple layers, the model can further exploit indirect relations between terms for more precise information extraction. Experimental results on three benchmark datasets in SemEval Challenge 2014 and 2015 show that our model achieves stateof-the-art performances compared with several baselines.read more
Citations
More filters
Journal ArticleDOI
Complete quadruple extraction using a two-stage neural model for aspect-based sentiment analysis
TL;DR: Wang et al. as mentioned in this paper proposed a two-stage neural network model composed of several modules, including BiLSTM, simple gated self-attention and position encoding for this joint task.
Proceedings ArticleDOI
Seeded-BTM: Enabling Biterm Topic Model with Seeds for Product Aspect Mining
TL;DR: A Seeded Biterm Topic Model (Seeded-BTM) is proposed that models the co-occurred word pairs (i.e., biterms) rather than sentences or whole reviews and enables unsupervised BTM to discover aspect topics that under the user guidance.
Journal ArticleDOI
A deceptive reviews detection model: Separated training of multi-feature learning and classification
TL;DR: Zhang et al. as mentioned in this paper proposed a new feature fusion strategy and verifies its performance by comparing it with other feature fusion strategies, which used three independent models for feature extraction: the TextCNN, the Bidirectional Gated Recurrent Unit (GRU), and the Self-Attention are used to learn local semantic features, temporal semantic features and weighted semantic features of reviews, respectively.
Book ChapterDOI
Aspect Term Extraction Using Deep Learning Model with Minimal Feature Engineering
Felipe Zschornack Rodrigues Saraiva,Ticiana L. Coelho da Silva,José Antônio Fernandes de Macêdo +2 more
TL;DR: This work proposes POS-AttWD-BLSTM-CRF, a neural network architecture using a deep learning model, and minimal feature engineering, to solve the problem of ATE in opinionated documents.
Posted Content
Simple Unsupervised Similarity-Based Aspect Extraction.
TL;DR: SUAEx is unsupervised and relies solely on the similarity of word embeddings for aspect extraction and achieves results that can outperform the state-of-the-art attention-based approach at a fraction of the time.
References
More filters
Proceedings Article
Distributed Representations of Words and Phrases and their Compositionality
TL;DR: This paper presents a simple method for finding phrases in text, and shows that learning good vector representations for millions of phrases is possible and describes a simple alternative to the hierarchical softmax called negative sampling.
Proceedings Article
Neural Machine Translation by Jointly Learning to Align and Translate
TL;DR: It is conjecture that the use of a fixed-length vector is a bottleneck in improving the performance of this basic encoder-decoder architecture, and it is proposed to extend this by allowing a model to automatically (soft-)search for parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly.
Proceedings ArticleDOI
Learning Phrase Representations using RNN Encoder--Decoder for Statistical Machine Translation
Kyunghyun Cho,Bart van Merriënboer,Caglar Gulcehre,Dzmitry Bahdanau,Fethi Bougares,Holger Schwenk,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio +8 more
TL;DR: In this paper, the encoder and decoder of the RNN Encoder-Decoder model are jointly trained to maximize the conditional probability of a target sequence given a source sequence.
Posted Content
Neural Machine Translation by Jointly Learning to Align and Translate
TL;DR: In this paper, the authors propose to use a soft-searching model to find the parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly.
Book
Opinion Mining and Sentiment Analysis
Bo Pang,Lillian Lee +1 more
TL;DR: This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems and focuses on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis.