SemEval-2015 Task 13: Multilingual All-Words Sense Disambiguation and Entity Linking

Moro, Andrea; Navigli, Roberto

doi:10.18653/v1/s15-2049

Cited by 185 publications

(164 citation statements)

References 25 publications

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“…Concepts are defined as units of knowledge, each with a unique meaning. In our proposed system, each sentence in a sample message is processed by using BabelFy API [28] and concepts from BabelNet repository are added to the obtained the feature space as new features of the related sentence. However, the stop words are kept in their original form and they are not extended by the related concepts.…”

Section: B Concepts Generation With Babelnetmentioning

confidence: 99%

An Ensemble Model using a BabelNet Enriched Document Space for Twitter Sentiment Classification

Sevim¹,

Omurca²,

Ekinci³

2018

IJITCS

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Abstract-With the widespread usage of social media in our daily lives, user reviews emerged as an impactful factor for numerous fields including understanding consumer attitudes, determining political tendency, revealing strengths or weaknesses of many different organizations. Today, people are chatting with their friends, carrying out social relations, shopping and following many current events through the social media. However social media limits the size of user messages. The users generally express their opinions by using emoticons, abbreviations, slangs, and symbols instead of words. This situation makes the sentiment classification of social media texts more complex. In this paper a sentiment classification model for Twitter messages is proposed to overcome this difficulty. In the proposed model first the short messages are expanded with BabelNet which is a concept network. Then the expanded and the original form of the messages are included in an ensemble learning model. Consequently we compared our ensemble model with traditional classification algorithms and observed that the F-measure value is increased.

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Section: B Concepts Generation With Babelnetmentioning

confidence: 99%

An Ensemble Model using a BabelNet Enriched Document Space for Twitter Sentiment Classification

Sevim¹,

Omurca²,

Ekinci³

2018

IJITCS

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“…In 2015, a shared task for Multilingual All-Words Sense Disambiguation and Entity Linking [58] using Babelnet was proposed as part of the International Workshop on Semantic Evaluation (SemEval). 12 In this task, the goal was to create a system that could perform both Word Sense Disambiguation and Entity Linking.…”

Section: Semevalmentioning

confidence: 99%

“…Numerous initiatives have contributed to the progress in the field broadly covering different types of textual content (and thus going beyond the boundaries of tweets). For example TAC-KBP [55] has established a yearly challenge in the field covering newswire, websites, discussion forum posts, ERD [17] with search queries content, and SemEval [58] with technical manuals and reports.…”

Section: Introductionmentioning

confidence: 99%

Lessons learnt from the Named Entity rEcognition and Linking (NEEL) challenge series

Rizzo

Pereira

Varga³

et al. 2017

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Abstract. The large number of tweets generated daily is providing decision makers with means to obtain insights into recent events around the globe in near real-time. The main barrier for extracting such insights is the impossibility of manual inspection of a diverse and dynamic amount of information. This problem has attracted the attention of industry and research communities, resulting in algorithms for the automatic extraction of semantics in tweets and linking them to machine readable resources. While a tweet is shallowly comparable to any other textual content, it hides a complex and challenging structure that requires domainspecific computational approaches for mining semantics from it. The NEEL challenge series, established in 2013, has contributed to the collection of emerging trends in the field and definition of standardised benchmark corpora for entity recognition and linking in tweets, ensuring high quality labelled data that facilitates comparisons between different approaches. This article reports the findings and lessons learnt through an analysis of specific characteristics of the created corpora, limitations, lessons learnt from the different participants and pointers for furthering the field of entity recognition and linking in tweets.

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“…WSD can in principle be used as a preprocessing step to address hypernymy detection in context, but it is not required. Also, WSD remains a challenging task (Moro and Navigli, 2015) and it might introduce errors early in the preprocessing pipeline.…”

Section: Motivationmentioning

confidence: 99%

Detecting Asymmetric Semantic Relations in Context: A Case-Study on Hypernymy Detection

Vyas

Carpuat

2017

Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)

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We introduce WHIC 1 , a challenging testbed for detecting hypernymy, an asymmetric relation between words. While previous work has focused on detecting hypernymy between word types, we ground the meaning of words in specific contexts drawn from WordNet examples, and require predictions to be sensitive to changes in contexts. WHIC lets us analyze complementary properties of two approaches of inducing vector representations of word meaning in context. We show that such contextualized word representations also improve detection of a wider range of semantic relations in context.

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SemEval-2015 Task 13: Multilingual All-Words Sense Disambiguation and Entity Linking

Cited by 185 publications

References 25 publications

An Ensemble Model using a BabelNet Enriched Document Space for Twitter Sentiment Classification

An Ensemble Model using a BabelNet Enriched Document Space for Twitter Sentiment Classification

Lessons learnt from the Named Entity rEcognition and Linking (NEEL) challenge series

Detecting Asymmetric Semantic Relations in Context: A Case-Study on Hypernymy Detection

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