A two-step convolutional neural network approach for semantic role labeling

Fonseca, Erick Rocha; Rosa, João Luís Garcia

doi:10.1109/ijcnn.2013.6707118

Cited by 30 publications

(23 citation statements)

References 11 publications

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“…We will summarize it here, mainly following [19]. The algorithm trains the neural network by back-propagation in order to maximize the likelihood over training sentences.…”

Section: Aspect Extractionmentioning

confidence: 99%

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Sentiment Analysis, Basic Tasks of

Chaturvedi¹,

Poria²,

Wang³

2017

Encyclopedia of Social Network Analysis and Mining

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“…We will summarize it here, mainly following [19]. The algorithm trains the neural network by back-propagation in order to maximize the likelihood over training sentences.…”

Section: Aspect Extractionmentioning

confidence: 99%

“…For inference, we need to find the best tag path using the Viterbi algorithm; e.g., we need to find the best tag path that minimizes the sentence score (19).…”

Section: Aspect Extractionmentioning

confidence: 99%

Sentiment Analysis, Basic Tasks of

Chaturvedi¹,

Poria²,

Wang³

2017

Encyclopedia of Social Network Analysis and Mining

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“…They have demonstrated the impact of a joint architecture on the task with a strong impact on the extraction of aspect terms, but less so for the extraction of opinion terms. The approach to semantic role labeling proposed by Fonseca et al [6] is closely related to our approach to aspect term extraction in that the task is phrased as a sequence tagging problem to which a convolutional neural network is applied.…”

Section: Related Workmentioning

confidence: 99%

Aspect-Based Sentiment Analysis Using a Two-Step Neural Network Architecture

Jebbara

Cimiano

2016

Semantic Web Challenges

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Sentiment analysis can be regarded as a relation extraction problem in which the sentiment of some opinion holder towards a certain aspect of a product, theme or event needs to be extracted. We present a novel neural architecture for sentiment analysis as a relation extraction problem that addresses this problem by dividing it into three subtasks: i) identification of aspect and opinion terms, ii) labeling of opinion terms with a sentiment, and iii) extraction of relations between opinion terms and aspect terms. For each subtask, we propose a neural network based component and combine all of them into a complete system for relational sentiment analysis.The component for aspect and opinion term extraction is a hybrid architecture consisting of a recurrent neural network stacked on top of a convolutional neural network. This approach outperforms a standard convolutional deep neural architecture as well as a recurrent network architecture and performs competitively compared to other methods on two datasets of annotated customer reviews. To extract sentiments for individual opinion terms, we propose a recurrent architecture in combination with word distance features and achieve promising results, outperforming a majority baseline by 18% accuracy and providing the first results for the USAGE dataset. Our relation extraction component outperforms the current state-of-the-art in aspect-opinion relation extraction by 15% F-Measure.

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“…Two SRL classifiers were trained on the resulting corpus. One of them (AlvaManchego and Rosa, 2012) adopted a semisupervised approach and obtained an F-Measure of 82.3%; the other (Fonseca and Rosa, 2013) adopted a neural architecture to label semantic arguments, disregarding the syntactic layer of annotation, and obtained an F-Measure of 62.82%.…”

Section: The Corpus Propbank-brmentioning

confidence: 99%

Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics

2015

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will take place in Denver on June 4 and 5 and is colocated with SemEval and NAACL. As in 2014 at COLING, also on this occasion *SEM and SemEval chose to coordinate their programs by featuring a joint invited talk. In this way, *SEM aims to bring together the ACL SIGLEX and ACL SIGSEM communities.The acceptance rate of *SEM 2015 was quite competitive: out of 98 submissions, we accepted 36 papers for an overall acceptance of 37%. The acceptance rate of long paper that were accepted for oral presentation (18 out of 62) is 29%. The papers cover a wide range of topics including distributional semantics; lexical semantics and lexical acquisition; formal and linguistic semantics; discourse semantics; lexical resources, linked data and ontologies; semantics for applications; and extra-propositional semantics: sentiment and figurative meaning.The *SEM 2015 program consists of oral presentations for selected long papers and a poster session for long and short papers.Day One, June 4th:• Joint *SEM SemEval keynote talk by Marco Baroni;• Oral presentation sessions on distributional semantics, lexical semantics, and extra-propositional semantics;• Poster session.Day Two, June 5th:• Keynote talk by Preslav Natkov;• Oral presentation sessions on semantics for applications, lexical resources and ontologies, formal semantics, and discourse semantics;• *SEM Best Paper Award.We cannot finish without saying that *SEM 2015 would not have been possible without the considerable efforts of our area chairs, their reviewers, and the computational semantics community in general.We hope you will enjoy *SEM 2015, Distributional semantic methods have some a priori appeal as models of human meaning acquisition, because they induce word representations from contextual distributions naturally occurring in corpus data without need for supervision. However, learning the meaning of a (concrete) word also involves establishing a link between the word and its typical visual referents, which is beyond the scope of classic, text-based distributional semantics. Since recently several proposals have been put forward about how to induce multimodal word representations from linguistic and visual contexts, it is natural to ask if this line of work, besides its practical implications, can help us to develop more realistic, grounded models of human word learning within the distributional semantics framework.In my talk, I will report about two studies in which we used multimodal distributional semantics (MDS) to simulate human word learning. In one study, we first measured the ability of subjects to link a nonce word to relevant linguistic and visual associates when prompted only by exposure to minimal corpus evidence about it. We then simulated the same task with an MDS model, finding its behavior remarkably similar to that of subjects. In the second study, we constructed a corpus in which child-directed speech is aligned with real-life pictures of the objects mentioned by care-givers. We then trained our MDS model on these data, and inspected the generaliza...

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A two-step convolutional neural network approach for semantic role labeling

Cited by 30 publications

References 11 publications

Sentiment Analysis, Basic Tasks of

Sentiment Analysis, Basic Tasks of

Aspect-Based Sentiment Analysis Using a Two-Step Neural Network Architecture

Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics

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