1,485 research outputs found
A Unified Model for Opinion Target Extraction and Target Sentiment Prediction
Target-based sentiment analysis involves opinion target extraction and target
sentiment classification. However, most of the existing works usually studied
one of these two sub-tasks alone, which hinders their practical use. This paper
aims to solve the complete task of target-based sentiment analysis in an
end-to-end fashion, and presents a novel unified model which applies a unified
tagging scheme. Our framework involves two stacked recurrent neural networks:
The upper one predicts the unified tags to produce the final output results of
the primary target-based sentiment analysis; The lower one performs an
auxiliary target boundary prediction aiming at guiding the upper network to
improve the performance of the primary task. To explore the inter-task
dependency, we propose to explicitly model the constrained transitions from
target boundaries to target sentiment polarities. We also propose to maintain
the sentiment consistency within an opinion target via a gate mechanism which
models the relation between the features for the current word and the previous
word. We conduct extensive experiments on three benchmark datasets and our
framework achieves consistently superior results.Comment: AAAI 201
On the Role of Text Preprocessing in Neural Network Architectures: An Evaluation Study on Text Categorization and Sentiment Analysis
Text preprocessing is often the first step in the pipeline of a Natural
Language Processing (NLP) system, with potential impact in its final
performance. Despite its importance, text preprocessing has not received much
attention in the deep learning literature. In this paper we investigate the
impact of simple text preprocessing decisions (particularly tokenizing,
lemmatizing, lowercasing and multiword grouping) on the performance of a
standard neural text classifier. We perform an extensive evaluation on standard
benchmarks from text categorization and sentiment analysis. While our
experiments show that a simple tokenization of input text is generally
adequate, they also highlight significant degrees of variability across
preprocessing techniques. This reveals the importance of paying attention to
this usually-overlooked step in the pipeline, particularly when comparing
different models. Finally, our evaluation provides insights into the best
preprocessing practices for training word embeddings.Comment: Blackbox EMNLP 2018. 7 page
Structure Learning for Neural Module Networks
Neural Module Networks, originally proposed for the task of visual question
answering, are a class of neural network architectures that involve
human-specified neural modules, each designed for a specific form of reasoning.
In current formulations of such networks only the parameters of the neural
modules and/or the order of their execution is learned. In this work, we
further expand this approach and also learn the underlying internal structure
of modules in terms of the ordering and combination of simple and elementary
arithmetic operators. Our results show that one is indeed able to
simultaneously learn both internal module structure and module sequencing
without extra supervisory signals for module execution sequencing. With this
approach, we report performance comparable to models using hand-designed
modules
- …