UWB at SemEval-2016 Task 1: Semantic Textual Similarity using Lexical, Syntactic, and Semantic Information

Brychcín, Tomáš; Svoboda, Lukás̆

doi:10.18653/v1/s16-1089

Cited by 50 publications

(55 citation statements)

References 17 publications

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“…CompiLIG The best Spanish-English performance on SNLI sentences was achieved by CompiLIG using features including: cross-lingual conceptual similarity using DBNary (Serasset, 2015), cross-language MultiVec word embeddings (Berard et al, 2016), and Brychcin and Svoboda (2016)'s improvements to Sultan et al (2015)'s method. (Nagoudi et al, 2017) Using only weighted word embeddings, LIM-LIG took second place on Arabic.…”

Section: Methodsmentioning

confidence: 99%

SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation

Cer¹,

Diab²,

Agirre³

et al. 2017

Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

1,162

775

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Semantic Textual Similarity (STS) measures the meaning similarity of sentences. Applications include machine translation (MT), summarization, generation, question answering (QA), short answer grading, semantic search, dialog and conversational systems. The STS shared task is a venue for assessing the current state-of-the-art. The 2017 task focuses on multilingual and cross-lingual pairs with one sub-track exploring MT quality estimation (MTQE) data. The task obtained strong participation from 31 teams, with 17 participating in all language tracks. We summarize performance and review a selection of well performing methods. Analysis highlights common errors, providing insight into the limitations of existing models. To support ongoing work on semantic representations, the STS Benchmark is introduced as a new shared training and evaluation set carefully selected from the corpus of English STS shared task data (2012)(2013)(2014)(2015)(2016)(2017).

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Section: Methodsmentioning

confidence: 99%

SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation

Cer¹,

Diab²,

Agirre³

et al. 2017

Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

1,162

775

View full text Add to dashboard Cite

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“…We trained the word vectors on data from MediaGist gathered during the last 17 month (approximately 1M comments for each language). Brychcín and Svoboda (2016) showed that this approach leads to very good sentence representation.…”

Section: Flames Detectormentioning

confidence: 99%

Cross-lingual Flames Detection in News Discussions

Shinar

Brychcín

Hercig

et al. 2017

RANLP 2017 - Recent Advances in Natural Language Processing Meet Deep Learning

Self Cite

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We introduce Flames Detector, an online system for measuring flames, i.e. strong negative feelings or emotions, insults or other verbal offences, in news commentaries across five languages. It is designed to assist journalists, public institutions or discussion moderators to detect news topics which evoke flames. We propose a machine learning approach to flames detection and calculate an aggregated score for a set of comment threads. The demo application shows the most flaming topics of the current period in several language variants. The search functionality gives a possibility to measure flames in any topic specified by a query. The evaluation shows that the flame detection in discussions is a difficult task, however, the application can already reveal interesting information about the actual news discussions.

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“…Most of the submissions relied on a machine translation step followed by a monolingual semantic similarity, but 4 teams tried to use learned vector representations (on words or sentences) combined with machine translation confidence (for instance the submission of Lo et al (2016) or Ataman et al (2016)). The method that achieved the best performance (Brychcin and Svoboda, 2016) was a supervised system built on a word alignment-based method proposed by Sultan et al (2015). This very recent method is, however, not evaluated in this paper.…”

Section: Mt-based Modelsmentioning

confidence: 99%

Deep Investigation of Cross-Language Plagiarism Detection Methods

Ferrero¹,

Besacier²,

Schwab³

et al. 2017

Proceedings of the 10th Workshop on Building and Using Comparable Corpora

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This paper is a deep investigation of cross-language plagiarism detection methods on a new recently introduced open dataset, which contains parallel and comparable collections of documents with multiple characteristics (different genres, languages and sizes of texts). We investigate cross-language plagiarism detection methods for 6 language pairs on 2 granularities of text units in order to draw robust conclusions on the best methods while deeply analyzing correlations across document styles and languages.

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UWB at SemEval-2016 Task 1: Semantic Textual Similarity using Lexical, Syntactic, and Semantic Information

Cited by 50 publications

References 17 publications

SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation

SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation

Cross-lingual Flames Detection in News Discussions

Deep Investigation of Cross-Language Plagiarism Detection Methods

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