Aligning Opinions: Cross-Lingual Opinion Mining with Dependencies

Almeida, Mariana S. C.; Pinto, C.; Figueira, Helena; Mendes, Pedro; Martins, André F. T.

doi:10.3115/v1/p15-1040

Cited by 15 publications

(8 citation statements)

References 30 publications

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“…In this paper, we have proposed a dependency graph parsing approach to structured sentiment analysis and shown that these models outperform state-of-the-art sequence labeling models on five benchmark datasets. Using parse trees as input has shown promise for sentiment analysis in the past, either to guide a tree-based algorithm (Socher et al, 2013;Tai et al, 2015) or to create features for sentiment models (Nakagawa et al, 2010;Almeida et al, 2015). However, to the authors' knowledge, this is the first attempt to directly predict dependencybased sentiment graphs.…”

Section: Discussionmentioning

confidence: 99%

Structured Sentiment Analysis as Dependency Graph Parsing

Barnes¹,

Kurtz²,

Oepen³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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Structured sentiment analysis attempts to extract full opinion tuples from a text, but over time this task has been subdivided into smaller and smaller sub-tasks, e.g., target extraction or targeted polarity classification. We argue that this division has become counterproductive and propose a new unified framework to remedy the situation. We cast the structured sentiment problem as dependency graph parsing, where the nodes are spans of sentiment holders, targets and expressions, and the arcs are the relations between them. We perform experiments on five datasets in four languages (English, Norwegian, Basque, and Catalan) and show that this approach leads to strong improvements over state-of-the-art baselines. Our analysis shows that refining the sentiment graphs with syntactic dependency information further improves results.

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Section: Discussionmentioning

confidence: 99%

Structured Sentiment Analysis as Dependency Graph Parsing

Barnes¹,

Kurtz²,

Oepen³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

View full text Add to dashboard Cite

show abstract

“…In this section, we also conduct experiments by using multilingual transfer, thanks to the availability of the Portuguese dataset (Almeida et al, 2015a). For a fair comparison, we still focus on Chinese as the target language.…”

Section: Multilingual Transfermentioning

confidence: 99%

“…However, the studies on other languages are relatively rare due to the scarcity of annotated datasets. To our best knowledge, there is only one exception by Almeida et al (2015a), which has annotated a small-scale dataset for the Portuguese language.…”

Section: Introductionmentioning

confidence: 99%

“…We conduct experiments on the newly constructed Chinese dataset to evaluate our methods, together with the English MPQA corpus (Wiebe et al, 2005) and the Portuguese dataset (Almeida et al, 2015a) for cross-lingual transfer. We observe that for the unsupervised cross-lingual transfer from the English corpus, the translation-based method is better than the model transfer, and their combination leads to further improvements.…”

Section: Introductionmentioning

confidence: 99%

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Chinese Opinion Role Labeling with Corpus Translation: A Pivot Study

Zhen¹,

Wang²,

Fu³

et al. 2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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Opinion Role Labeling (ORL), aiming to identify the key roles of opinion, has received increasing interest. Unlike most of the previous works focusing on the English language, in this paper, we present the first work of Chinese ORL. We construct a Chinese dataset by manually translating and projecting annotations from a standard English MPQA dataset. Then, we investigate the effectiveness of cross-lingual transfer methods, including model transfer and corpus translation. We exploit multilingual BERT with Contextual Parameter Generator and Adapter methods to examine the potentials of unsupervised crosslingual learning and our experiments and analyses for both bilingual and multilingual transfers establish a foundation for the future research of this task 1 . * Corresponding author. 1 We release the code and way of obtaining Chinese dataset at https://github.com/zenRRan/ ChineseORL-with-Corpus-Translation.

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“…They are usually aimed to bridge the gap of missing resources in the target language. So far, they have been quite successfully applied to part-of-speech tagging (Täckström et al, 2013), syntactic parsing (Hwa et al, 2005), semantic role labeling (Padó and Lapata, 2009), opinion mining (Almeida et al, 2015), etc. Coreference resolution is no exception in this respect.…”

Section: Related Workmentioning

confidence: 99%

Proceedings of the 2nd Workshop on Coreference Resolution Beyond OntoNotes (CORBON 2017)

Ogrodniczuk¹,

Ng²

2017

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ii IntroductionMany NLP researchers, especially those not working in the area of discourse processing, tend to equate coreference resolution with the sort of coreference that people did in MUC, ACE, and OntoNotes, having the impression that coreference is a well-worn task owing in part to the large number of papers reporting results on the MUC/ACE/OntoNotes corpora. This is an unfortunate misconception: the previous SemEval 2010 and CoNLL 2012 shared tasks on coreference resolution have largely focused on entity coreference, which constitutes only one of the many kinds of coreference relations that were discussed in theoretical and computational linguistics in the past few decades. In fact, by focusing on entity coreference resolution, NLP researchers have only scratched the surface of the wealth of interesting problems in coreference resolution.The decision to focus on entity coreference resolution was initially made by information extraction (IE) researchers when coreference was selected as one of the tasks in the MUC-6 coreference in 1995. Many interesting kinds of coreference relations, such as bridging and reference to abstract entities, were left out not because they were not important, but because "it was felt that the menu was simply too ambitious". It turned out that this decision had an important consequence: the progress made in coreference research in the past two decades was largely driven by the availability of coreferenceannotated corpora such as MUC, ACE, and OntoNotes, where entity coreference was the focus.Being aware of other fora gathering coreference-related papers (such as LAW, DiscoMT or EVENTS), in 2016 we started a new workshop on the single topic of knowledge-oriented coreference resolution under the name of Coreference Resolution Beyond OntoNotes (CORBON) that would bring together researchers who were interested in under-investigated coreference phenomena, willing to contribute both theoretical and applied computational work on coreference resolution, especially for languages other than English, less-researched forms of coreference and new applications of coreference resolution.The success of the first edition of the workshop (held in conjunction with NAACL HLT 2016 in San Diego, USA) and our intention to verify the role of the Europe-based researchers in the field encouraged us to organise the second edition of the workshop in conjunction with EACL 2017 in Valencia, Spain. Our call attracted 12 submissions (nine from European research institutions and three from India). We are pleased to see that the submissions covered not only a variety of less-studied languages in the coreference community (e.g., Basque, French, German, Polish, Portuguese, Russian, and Tamil) but also many under-investigated topics in coreference resolution (e.g., feature representation, the use of semantics and deep syntax for coreference resolution, difficult cases of anaphora, and the use of coreference chains in high-level natural language applications). Each submission was rigorously reviewed by three to five ...

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Aligning Opinions: Cross-Lingual Opinion Mining with Dependencies

Cited by 15 publications

References 30 publications

Structured Sentiment Analysis as Dependency Graph Parsing

Structured Sentiment Analysis as Dependency Graph Parsing

Chinese Opinion Role Labeling with Corpus Translation: A Pivot Study

Proceedings of the 2nd Workshop on Coreference Resolution Beyond OntoNotes (CORBON 2017)

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