Overview of the EVALITA 2016 Named Entity rEcognition and Linking in Italian Tweets (NEEL-IT) Task

Basile, Pierpaolo; Caputo, Annalina; Gentile, Anna Lisa; Rizzo, Giuseppe

doi:10.4000/books.aaccademia.1935

Cited by 21 publications

(29 citation statements)

References 3 publications

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“…In order to have a comparison with the state-of-the-art on sentiment analysis, the proposed approach was tested on the most famous Italian dataset that exists in the literature, the SENTIPOLC 2016, also having the possibility of comparison with the state-of-the-art and in particular with AlBERTo [ 13 ], which is essentially an Italian BERT model trained from scratch directly on a corpora of tweets. In detail, this dataset was built from the following corpora: TW-SENTIPOLC14 [ 89 ]; TWitterBuonaScuola [ 90 ]; Tweets selected from the TWITA 2015 collection [ 91 ]; Tweets collected in the context of the EVALITA 2016 NEEL-IT Task [ 92 ]. …”

Section: Experimental Designmentioning

confidence: 99%

An Effective BERT-Based Pipeline for Twitter Sentiment Analysis: A Case Study in Italian

Pota

Ventura

Catelli

et al. 2020

Sensors

102

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Over the last decade industrial and academic communities have increased their focus on sentiment analysis techniques, especially applied to tweets. State-of-the-art results have been recently achieved using language models trained from scratch on corpora made up exclusively of tweets, in order to better handle the Twitter jargon. This work aims to introduce a different approach for Twitter sentiment analysis based on two steps. Firstly, the tweet jargon, including emojis and emoticons, is transformed into plain text, exploiting procedures that are language-independent or easily applicable to different languages. Secondly, the resulting tweets are classified using the language model BERT, but pre-trained on plain text, instead of tweets, for two reasons: (1) pre-trained models on plain text are easily available in many languages, avoiding resource- and time-consuming model training directly on tweets from scratch; (2) available plain text corpora are larger than tweet-only ones, therefore allowing better performance. A case study describing the application of the approach to Italian is presented, with a comparison with other Italian existing solutions. The results obtained show the effectiveness of the approach and indicate that, thanks to its general basis from a methodological perspective, it can also be promising for other languages.

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Section: Experimental Designmentioning

confidence: 99%

An Effective BERT-Based Pipeline for Twitter Sentiment Analysis: A Case Study in Italian

Pota

Ventura

Catelli

et al. 2020

Sensors

102

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“…The use of an encyclopedic knowledge base was because Person, Organisation, and Geo-political entities are not domain-specific and due to the availability of Wikipedia as a free available knowledge base on the Web. 8 http://www.nist.gov/tac…”

Section: Tac-kbpmentioning

confidence: 99%

“…8 This conference was aimed at a community focused on the analysis of textual documents and the challenge itself was part of the Knowledge Base Population track (also called TAC-KBP) [55]. The goal of this track was to explore algorithms for automatic knowledge base population from textual sources.…”

Section: Tac-kbpmentioning

confidence: 99%

“…The prizes were sponsored by industry (ebay 2 in 2013 and SpazioDati 3 in 2015) and research projects (LinkedTV 4 in 2014, and FREME 5 in 2016). The NEEL challenge also triggered the interest of localised challenges such as NEEL-IT, the NEEL challenge for tweets written in Italian [8] that brings the multilinguality aspect in the NEEL contest.…”

Section: Introductionmentioning

confidence: 99%

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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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“…Given the widespread interest in social media, a particular effort has been put in providing tasks dealing with texts in that domain. Three tasks focus on the processing of tweets (i.e., NEEL-it, PoSTWITA, and SENTIPOLC) and part of the test set is shared among 4 different tasks (i.e., FacTA, NEEL-it, PoSTWITA, and SENTIPOLC) (Minard et al, 2016;Basile et al, 2016a;Barbieri et al, 2016). Part of the SENTIPOLC data was annotated via Crowdflower 14 thanks to funds allocated by AILC.…”

Section: Lessons Learnt and Impact On Evalita 2016mentioning

confidence: 99%

Raising Interest and Collecting Suggestions on the EVALITA Evaluation Campaign

Sprugnoli¹,

Patti²,

Cutugno³

2016

EVALITA. Evaluation of NLP and Speech Tools for Italian

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This paper describes the design and reports the results of two questionnaires. The first of these questionnaires was created to collect information about the interest of industrial companies in the field of Italian text/speech analytics towards the evaluation campaign EVALITA; the second to gather comments and suggestions for the future of the evaluation and of its final workshop from the participants and the organizers of the campaign on the last two editions (2011 and 2014). Novelties introduced in the organization of EVALITA 2016 on the basis of the questionnaires results are also reported.

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Overview of the EVALITA 2016 Named Entity rEcognition and Linking in Italian Tweets (NEEL-IT) Task

Cited by 21 publications

References 3 publications

An Effective BERT-Based Pipeline for Twitter Sentiment Analysis: A Case Study in Italian

An Effective BERT-Based Pipeline for Twitter Sentiment Analysis: A Case Study in Italian

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

Raising Interest and Collecting Suggestions on the EVALITA Evaluation Campaign

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