On Assertive Predicates

Hooper, Joan Β.

doi:10.1163/9789004368828_005

Cited by 140 publications

(92 citation statements)

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“…2.3.4 Linguistic features (13 features). We use as features the presence and the frequency of occurrence of linguistic markers such as factives and assertives from [Hooper 1974], implicatives from [Karttunen 1971], hedges from [Hyland 2005], Wiki-bias terms from [Recasens et al 2013], subjectivity cues from [Riloff and Wiebe 2003], and sentiment cues from [Liu et al 2005]. 6 We compute a feature vector according to Equation (1) where for each bias type B i and answer A j , the frequency of the cues for B i in A j is computed and then normalized by the total number of words in A j :…”

Section: Named Entities (Ne)mentioning

confidence: 99%

Automatic Fact-Checking Using Context and Discourse Information

Atanasova

Nakov

Màrquez³

et al. 2019

J. Data and Information Quality

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We study the problem of automatic fact-checking, paying special attention to the impact of contextual and discourse information. We address two related tasks: (i) detecting check-worthy claims, and (ii) fact-checking claims. We develop supervised systems based on neural networks, kernel-based support vector machines, and combinations thereof, which make use of rich input representations in terms of discourse cues and contextual features. For the check-worthiness estimation task, we focus on political debates, and we model the target claim in the context of the full intervention of a participant and the previous and the following turns in the debate, taking into account contextual meta information. For the fact-checking task, we focus on answer verification in a community forum, and we model the veracity of the answer with respect to the entire question-answer thread in which it occurs as well as with respect to other related posts from the entire forum. We develop annotated datasets for both tasks and we run extensive experimental evaluation, confirming that both types of information -but especially contextual features-play an important role.

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Section: Named Entities (Ne)mentioning

confidence: 99%

Automatic Fact-Checking Using Context and Discourse Information

Atanasova

Nakov

Màrquez³

et al. 2019

J. Data and Information Quality

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“…English does not use mood productively. Instead, it tracks representationality in two ways: via (i) finiteness: representationals tend to take finite complements and preferentials take nonfinite complements; (ii) "sentence-lifting" ("slifting"; Ross, 1973), where the main clause appears inside the complement (20), and "complement preposing," where the main clause follows the complement (21), which are only possible with representationals (Bolinger, 1968;Hooper, 1975) In German, the use of mood is more productive than in English, but it does not track representationality as in Romance. Rather, subjunctive mood is used to indicate reported speech.…”

Section: (# Indicates Infelicity)mentioning

confidence: 99%

“…Three-year-olds do not reject want sentences with false complements because they do not assume indirect assertions. What leads children to assume different meanings and pragmatic functions for think and want is their syntactic distribution: like other representationals, think takes complements that allow declarative Hooper (1975), Thompson and Mulac (1991), Dayal and Grimshaw (2009). 6 It cannot be that children only pay attention to the complement and assume that it is indirectly asserted.…”

Section: Pragmatic Syntactic Bootstrapping For Attitude Verbsmentioning

confidence: 99%

Children's attitude problems: Bootstrapping verb meaning from syntax and pragmatics

Hacquard

Lidz

2018

Mind & Language

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How do children learn the meanings of propositional attitude verbs? We argue that children use information contained in both syntactic distribution and pragmatic function to zero in on the appropriate meanings. Specifically, we identify a potentially universal link between semantic subclasses of attitude verbs, their syntactic distribution and the kinds of indirect speech acts they can be used to perform. As a result, children can use the syntax as evidence about the meaning, which in turn constrains the kinds of pragmatic enrichments they do and do not make in understanding these verbs in conversation.

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“…To detect this strategy, we match the turn against a set of phrases, e.g., "throw in", "throwing in", "deliver", "delivery", "pick up", "pick it up", "in cash". • Use factive verbs defined in (Hooper, 1975) (e.g. know); • Use hedge words defined in (Hyland, 2005) Table 1: Actionable tactics designed based on negotiation principles.…”

Section: • Negotiate Side Offersmentioning

confidence: 99%