Opinion Mining with Deep Recurrent Neural Networks

İrsoy, Ozan; Cardie, Claire

doi:10.3115/v1/d14-1080

Cited by 338 publications

(251 citation statements)

References 15 publications

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“…On the contrary,İrsoy and Cardie implement an end-to-end sequence labeler for opinion extraction with SRNN [2]. It takes unsupervised trained word vectors as input and outperforms previous approaches.…”

Section: Opinion Expression Extractionmentioning

confidence: 99%

“…Recent natural language processing (NLP) works leverage word embeddings to encode syntactic and semantic information. Composing such embeddings through a deep recurrent neural network has been proven to be an efficient way to model the interactions between words.İrsoy and Cardie conducted a comprehensive research on utilizing the Elman network, also known as a Simple Recurrent Neural Network (SRNN), in opinion expression extraction and achieved state-of-the-art performance [2].…”

Section: Introductionmentioning

confidence: 99%

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Understanding Gating Operations in Recurrent Neural Networks through Opinion Expression Extraction

Wang

Liu

et al. 2016

Entropy

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Extracting opinion expressions from text is an essential task of sentiment analysis, which is usually treated as one of the word-level sequence labeling problems. In such problems, compositional models with multiplicative gating operations provide efficient ways to encode the contexts, as well as to choose critical information. Thus, in this paper, we adopt Long Short-Term Memory (LSTM) recurrent neural networks to address the task of opinion expression extraction and explore the internal mechanisms of the model. The proposed approach is evaluated on the Multi-Perspective Question Answering (MPQA) opinion corpus. The experimental results demonstrate improvement over previous approaches, including the state-of-the-art method based on simple recurrent neural networks. We also provide a novel micro perspective to analyze the run-time processes and gain new insights into the advantages of LSTM selecting the source of information with its flexible connections and multiplicative gating operations.

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Section: Opinion Expression Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Understanding Gating Operations in Recurrent Neural Networks through Opinion Expression Extraction

Wang

Liu

et al. 2016

Entropy

View full text Add to dashboard Cite

show abstract

“…Since 2014 the SemEval workshop included a shared task on the topic (Pontiki et al, 2014), which has also encouraged the development of new supervised methods. We find approaches based on CRFs such as Mitchell et al (2013) and deep learning (Irsoy and Cardie, 2014) (Liu et al, 2015a), (Zhang et al, 2015).…”

Section: Related Workmentioning

confidence: 99%

Replication issues in syntax-based aspect extraction for opinion mining

Marrese-Taylor¹,

Matsuo²

2017

Proceedings of the Student Research Workshop at the 15th Conference Of the European Chapter of the Association for Co

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Reproducing experiments is an important instrument to validate previous work and build upon existing approaches. It has been tackled numerous times in different areas of science. In this paper, we introduce an empirical replicability study of three well-known algorithms for syntactic centric aspect-based opinion mining. We show that reproducing results continues to be a difficult endeavor, mainly due to the lack of details regarding preprocessing and parameter setting, as well as due to the absence of available implementations that clarify these details. We consider these are important threats to validity of the research on the field, specifically when compared to other problems in NLP where public datasets and code availability are critical validity components. We conclude by encouraging code-based research, which we think has a key role in helping researchers to understand the meaning of the state-of-the-art better and to generate continuous advances.

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“…CNN and RNN display high quality in sentiment analysis in short texts [44] and opinion mining tasks [45].…”

Section: ав мельников дс ботов юд кленинmentioning

confidence: 99%

On Usage of Machine Learning for Natural Language Processing Tasks as Illustrated by Educational Content Mining

Melnikov¹,

Botov²,

Klenin³

2017

Ontology of Designing

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In this paper, we review most popular approaches to a variety of natural language processing (NLP) tasks, primarily those, which involve machine learning: from classics to state-of-the-art technologies. Most modern approaches can be separated into three rough categories: ones based on distributional hypothesis, those extracting information from graph-like structures (such as ontologies) and the ones that look for lexico-syntactic patterns in text documents. We focus mainly on the former of the three. Before the analysis can even begin, one of the important steps in preparation stage of NLP is the task of representing words and documents as numeric vectors. There exists a variety of approaches from the most simplistic Bag-of-Words to sophisticated machine learning methods, such as word embedding. Today, in the task of information retrieval the best quality for both English and Russian languages is achieved by approaches based on word embedding algorithms, trained on carefully picked text corpora in conjunction with deep syntactic and semantic analysis using various deep neural networks. A big variety of different machine learning algorithms is being applied for NLP tasks such as Part-of-Speech-tagging, text summarization, named entity recognition, document classification, topic and relation extraction and natural language question answering. We also review possibilities of applying these approaches and methods to educational content analysis, and propose the novel approach to utilizing NLP and machine learning capabilities in analyzing and synthesizing educational content in a form of a decision support systems.

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Opinion Mining with Deep Recurrent Neural Networks

Cited by 338 publications

References 15 publications

Understanding Gating Operations in Recurrent Neural Networks through Opinion Expression Extraction

Understanding Gating Operations in Recurrent Neural Networks through Opinion Expression Extraction

Replication issues in syntax-based aspect extraction for opinion mining

On Usage of Machine Learning for Natural Language Processing Tasks as Illustrated by Educational Content Mining

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