SemEval-2016 Task 3: Community Question Answering

Nakov, Preslav; Màrquez, Lluı́s; Moschitti, Alessandro; Magdy, Walid; Mubarak, Hamdy; Freihat, Abed Alhakim; Glass, James; Randeree, Bilal

doi:10.18653/v1/s16-1083

Cited by 153 publications

(104 citation statements)

References 36 publications

(32 reference statements)

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“…However, recent results have shown that automatically extracted large-scale lexicons (e.g., up to a million words and phrases) offer important performance advantages, as confirmed at shared tasks on Sentiment Analysis on Twitter at SemEval 2013-2016 [38,40,55,56]. Using such large-scale lexicons was crucial for the performance of the top-ranked systems.…”

Section: Sentiment Polarity Lexiconsmentioning

confidence: 99%

“…The simplest and also the most popular task of sentiment analysis on Twitter is to determine the overall sentiment expressed by the author of a tweet [38,39,40,55,56]. Typically, this means choosing one of the following three classes to describe the sentiment: POSITIVE, NEGATIVE, and NEUTRAL.…”

Section: Variants Of the Task At Semevalmentioning

confidence: 99%

“…From a machine learning perspective, this means moving from classification to ordinal regression. This also requires different evaluation measures [38].…”

Section: Variants Of the Task At Semevalmentioning

confidence: 99%

“…Most (if not all) tweet sentiment classification studies conducted within political science [6,23,30], economics [5,41], social science [13], and market research [7,51] use Twitter with an interest in aggregate data and not in individual classifications. Thus, some tasks, such as SemEval-2016 Task 4 [38], replace classification with class prevalence estimation, which is also known as quantification in data mining and related fields. Note that quantification is not a mere byproduct of classification, since a good classifier is not necessarily a good quantifier, and vice versa [15].…”

Section: Variants Of the Task At Semevalmentioning

confidence: 99%

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Semantic Sentiment Analysis of Twitter Data

Nakov¹

2017

Encyclopedia of Social Network Analysis and Mining

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Microblog sentiment analysis; Twitter opinion mining 2 GlossarySentiment Analysis: This is text analysis aiming to determine the attitude of a speaker or a writer with respect to some topic or the overall contextual polarity of a piece of text. DefinitionSentiment analysis on Twitter is the use of natural language processing techniques to identify and categorize opinions expressed in a tweet, in order to determine the author's attitude toward a particular topic or in general. Typically, discrete labels such as positive, negative, neutral, and objective are used for this purpose, but it is also possible to use labels on an ordinal scale, or even continuous numerical values.

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Section: Sentiment Polarity Lexiconsmentioning

confidence: 99%

Section: Variants Of the Task At Semevalmentioning

confidence: 99%

“…From a machine learning perspective, this means moving from classification to ordinal regression. This also requires different evaluation measures [38].…”

Section: Variants Of the Task At Semevalmentioning

confidence: 99%

Section: Variants Of the Task At Semevalmentioning

confidence: 99%

See 2 more Smart Citations

Semantic Sentiment Analysis of Twitter Data

Nakov¹

2017

Encyclopedia of Social Network Analysis and Mining

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“…We show the results on two AS corpora, WikiQA and TREC13 (Wang et al, 2007). Then, we report the results obtained when using RelTextRank in a cQA system for English and Arabic comment selection tasks in the SemEval-2016 competition, Tasks 3.A and 3.D (Nakov et al, 2016).…”

Section: Previous Uses Of Reltextrankmentioning

confidence: 99%

Proceedings of ACL 2017, System Demonstrations

Zhang

2017

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We present the first open-source tool for annotating morphosyntactic tense, mood and voice for English, French and German verbal complexes. The annotation is based on a set of language-specific rules, which are applied on dependency trees and leverage information about lemmas, morphological properties and POS-tags of the verbs. Our tool has an average accuracy of about 76%. The tense, mood and voice features are useful both as features in computational modeling and for corpuslinguistic research. IntroductionNatural language employs, among other devices such as temporal adverbials, tense and aspect to locate situations in time and to describe their temporal structure (Deo, 2012). The tool presented here addresses the automatic annotation of morphosyntactic tense, i.e., the tense-aspect combinations, expressed in the morphology and syntax of verbal complexes (VC). VCs are sequences of verbal tokens within a verbal phrase. We address German, French and English, in which the morphology and syntax also includes information on mood and voice. Morphosyntactic tenses do not always correspond to semantic tense (Deo, 2012). For example, the morphosyntactic tense of the English sentence "He is leaving at noon." is present progressive, while the semantic tense is future. In the remainder of this paper, we use the term tense to refer to the morphological tense and aspect information encoded in finite verbal complexes.Corpus-linguistic research, as well as automatic modeling of mono-and cross-lingual use of tense, mood and voice will strongly profit from a reliable automatic method for identifying these clausal features. They may, for instance, be used to classify texts with respect to the epoch or region in which they have been produced, or for assigning texts to a specific author. Moreover, in crosslingual research, tense, mood, and voice have been used to model the translation of tense between different language pairs (Santos, 2004; Loáiciga et al., 2014; Ramm and Fraser, 2016)). Identifying the morphosyntactic tense is also a necessary prerequisite for identifying the semantic tense in synthetic languages such as English, French or German (Reichart and Rappoport, 2010). The extracted tense-mood-voice (TMV) features may also be useful for training models in computational linguistics, e.g., for modeling of temporal relations (Costa and Branco, 2012; UzZaman et al., 2013).As illustrated by the examples in Figure 1, relevant information for determining TMV is given by syntactic dependencies and partially by partof-speech (POS) tags output by analyzers such as Mate (Bohnet and Nivre, 2012). However, the parser's output is not sufficient for determining TMV features; morphological features and lexical information needs to be taken into account as well. Learning TMV features from an annotated corpus would be an alternative; however, to the best of our knowledge, no such large-scale corpora exist.A sentence may contain more than one VC, and the tokens belonging to a VC are not always contiguous in the sentence (see VCs A...

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A survey of consumer health question answering systems

Welivita,

2023

AI Magazine

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Consumers are increasingly using the web to find answers to their health‐related queries. Unfortunately, they often struggle with formulating the questions, further compounded by the burden of having to traverse long documents returned by the search engine to look for reliable answers. To ease these burdens for users, automated consumer health question answering systems try to simulate a human professional by refining the queries and giving the most pertinent answers. This article surveys state‐of‐the‐art approaches, resources, and evaluation methods used for automatic consumer health question answering. We summarize the main achievements in the research community and industry, discuss their strengths and limitations, and finally come up with recommendations to further improve these systems in terms of quality, engagement, and human‐likeness.

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SemEval-2016 Task 3: Community Question Answering

Cited by 153 publications

References 36 publications

Semantic Sentiment Analysis of Twitter Data

Semantic Sentiment Analysis of Twitter Data

Proceedings of ACL 2017, System Demonstrations

A survey of consumer health question answering systems

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