Generalizable distributional regularities aid fluent language processing the case of semantic valence tendencies (2)
➤ Gửi thông báo lỗi ⚠️ Báo cáo tài liệu vi phạmNội dung chi tiết: Generalizable distributional regularities aid fluent language processing the case of semantic valence tendencies (2)
Generalizable distributional regularities aid fluent language processing the case of semantic valence tendencies (2)
Fluent sentence comprehensionGeneralizable distributional regularities aid fluent language processing: The case of semantic valence tendencies*Luca Om Generalizable distributional regularities aid fluent language processing the case of semantic valence tendencies (2)miis1. Thomas A. Fanner. Marco Baroni5. Morten H. Christiansen2, and MichaelJ. Spivey4'University of Hawaii. Honolulu. HI^Cornell University. Ithaca. NYUniversity of Trento. Italy University of California. MercedRunning Head: Fluent sentence comprehensionWord count: 8.220•Corresponding Author:Univer Generalizable distributional regularities aid fluent language processing the case of semantic valence tendencies (2)sity of Hawaii at Manoa Department of Second Language Studies Center for Second Language Research 493 Moore Hall1890 East-West RoadHonolulu. HI 96822Generalizable distributional regularities aid fluent language processing the case of semantic valence tendencies (2)
email: lucaoiịì hawaii.edu phone: (808)-956-27821Fluent sentence comprehensionAbstractSentence processing is an extraordinarily complex and speeded prFluent sentence comprehensionGeneralizable distributional regularities aid fluent language processing: The case of semantic valence tendencies*Luca Om Generalizable distributional regularities aid fluent language processing the case of semantic valence tendencies (2)at speakers continuously build expectations for upcoming linguistic material based on partial information available at each relevant tune point. In addition, statistical analyses of corpora suggest that many words entail probabilistic semantic consequences. For instance, in English, the verb provide Generalizable distributional regularities aid fluent language processing the case of semantic valence tendencies (2) typically precedes positive words (e.g., 'to provide work’) whereas cause typically precedes negative items (e g., ‘to cause trouble’: Sinclair, 1996Generalizable distributional regularities aid fluent language processing the case of semantic valence tendencies (2)
). We hypothesized that these statistical patterns form units of meaning that imbue lexical items, and their argument structures, with semantic valencFluent sentence comprehensionGeneralizable distributional regularities aid fluent language processing: The case of semantic valence tendencies*Luca Om Generalizable distributional regularities aid fluent language processing the case of semantic valence tendencies (2) First, a sentence completion task elicited such tendencies 111 adults, suggesting that speakers constrain then free productions to conform to the connotative meaning of words. Second, fluent on-line reading was slowed down significantly in sentences that contained a violation of a valence tendency Generalizable distributional regularities aid fluent language processing the case of semantic valence tendencies (2)(e.g. cause optimism). Third, an automated computer algorithm assessed the pervasiveness of valence tendencies in large computerized samples of EnglisGeneralizable distributional regularities aid fluent language processing the case of semantic valence tendencies (2)
h, supporting the hypothesis that valence tendencies are a distributional phenomenon. We conclude that not only can aspects of meaning be modeled withFluent sentence comprehensionGeneralizable distributional regularities aid fluent language processing: The case of semantic valence tendencies*Luca Om Generalizable distributional regularities aid fluent language processing the case of semantic valence tendencies (2)g of language. They thus simultaneously contribute lo our understanding of the use of language and the psychology of language.3Fluent sentence comprehension1. IntroductionIn ordinary day-to-day human conversation, language comprehension and production under real-time circumstances is extremely fluen Generalizable distributional regularities aid fluent language processing the case of semantic valence tendencies (2)t, i.e. It is very rapid and yet proceeds effortlessly. Achieving language fluency may appear a trivial feat to most language users, until we considerGeneralizable distributional regularities aid fluent language processing the case of semantic valence tendencies (2)
that it involves the rapid integration of several concurrent types of information cues (sublexical. lexical, semantic, syntactic, and pragmatic) in rFluent sentence comprehensionGeneralizable distributional regularities aid fluent language processing: The case of semantic valence tendencies*Luca Om Generalizable distributional regularities aid fluent language processing the case of semantic valence tendencies (2) to pick up linguistic information on the fly must somehow be flexible enough to encompass fluent generativity in both comprehension and production. Given this state of affairs, it becomes relevant to understand the cognitive mechanisms underlying fluent language processing.In this article, we consi Generalizable distributional regularities aid fluent language processing the case of semantic valence tendencies (2)der the hypothesis that adult speakers possess implicit knowledge of distributional patterns of words accumulated during years of language usage. We aGeneralizable distributional regularities aid fluent language processing the case of semantic valence tendencies (2)
lso argue that this accumulated distributional knowledge may facilitate fluency in on-line human sentence comprehension. In particular, we advance theFluent sentence comprehensionGeneralizable distributional regularities aid fluent language processing: The case of semantic valence tendencies*Luca Om Generalizable distributional regularities aid fluent language processing the case of semantic valence tendencies (2) of fast and fluent sentence comprehension. One example of the extended units of meaning which we shall consider here can be seen in the observation that the verb cause is usually associated with unpleasant words, such as ‘cause problems’, or4Fluent sentence comprehension‘cause trouble' (Sinclair. 1 Generalizable distributional regularities aid fluent language processing the case of semantic valence tendencies (2)991). Importantly, these extended units of meaning arise from word combinations that are constrained and yet productive at the same time, thus going bGeneralizable distributional regularities aid fluent language processing the case of semantic valence tendencies (2)
eyond knowledge of frozen expressions like idioms and collocations. Hence, adult speakers may (at least implicitly) be sensitive to a generalized pattFluent sentence comprehensionGeneralizable distributional regularities aid fluent language processing: The case of semantic valence tendencies*Luca Om Generalizable distributional regularities aid fluent language processing the case of semantic valence tendencies (2)‘core' denotational meaning of these words may not a priori involve a positive or negative reading. There is no reason to assume that fo cause or Í0 encounter anticipate negative words or events. Thus, another intriguing aspect IS that the connotational meaning of these words may emerge as meaning d Generalizable distributional regularities aid fluent language processing the case of semantic valence tendencies (2)istributed over the context of then occurrences 111 language.The proposal that certain woid distributional patterns may conti ibute to language fluencGeneralizable distributional regularities aid fluent language processing the case of semantic valence tendencies (2)
y IS consistent with recent suggestions that on-line sentence comprehension takes place incrementally, and can be driven by expectations made on the bFluent sentence comprehensionGeneralizable distributional regularities aid fluent language processing: The case of semantic valence tendencies*Luca Om Generalizable distributional regularities aid fluent language processing the case of semantic valence tendencies (2)005). For instance, upon hearing the sentence fragment "Yesterday's news caused ..." a native speaker of English may have an implicit expectation for a noun phrase that is likely to have a negative connotation, although the specific word to follow is unknown. Therefore, the language processing syste Generalizable distributional regularities aid fluent language processing the case of semantic valence tendencies (2)m may be facilitated 111 processing the continuation of the sentence "Yesterday's news caused pessimism among the viewers’’ even though that specificGeneralizable distributional regularities aid fluent language processing the case of semantic valence tendencies (2)
sentence or the specific word5Fluent sentence comprehensioncombination (collocation) ‘cause pessimism’ may have never been encountered before, 01 has Fluent sentence comprehensionGeneralizable distributional regularities aid fluent language processing: The case of semantic valence tendencies*Luca Om Generalizable distributional regularities aid fluent language processing the case of semantic valence tendencies (2)on for the predicate of a verb as a semantic valence tendency (SVT). Importantly, this latter aspect preserves the generativity of language, while at the same time imposing probabilistic constraints in terms of what to expect for the continuation of a sentence. In the literature there IS mounting ev Generalizable distributional regularities aid fluent language processing the case of semantic valence tendencies (2)idence, discussed below, that humans use expectations as the sentence unfolds in order to reduce the set of possible competitors to a word or sentenceGeneralizable distributional regularities aid fluent language processing the case of semantic valence tendencies (2)
continuation. In other words, at each time step the linguistic processor uses the currently available input and the lexical information associated wiFluent sentence comprehensionGeneralizable distributional regularities aid fluent language processing: The case of semantic valence tendencies*Luca Om Generalizable distributional regularities aid fluent language processing the case of semantic valence tendencies (2) in language is not entirely new. As we detail below, it has been fruitfully exploited in some linguistic circles—in particular, those adhering to usage-based accounts of language. Analyses of large databases of written and spoken language have staited to show that most language is patterned, such t Generalizable distributional regularities aid fluent language processing the case of semantic valence tendencies (2)hat word combinations are constrained not only by syntactic but also by lexical factors in very subtle ways. Corpus analyses have also provided initiaGeneralizable distributional regularities aid fluent language processing the case of semantic valence tendencies (2)
l evidence for SVTs for a relatively small number of words. However, so far these facts have often been confined to linguistic enquiry with little effFluent sentence comprehensionGeneralizable distributional regularities aid fluent language processing: The case of semantic valence tendencies*Luca OmFluent sentence comprehensionGeneralizable distributional regularities aid fluent language processing: The case of semantic valence tendencies*Luca OmGọi ngay
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