Beliefs, points of view, and multiple environments

Wilks, Yorick; Bień, Janusz S.

doi:10.1016/s0364-0213(83)80007-x

Cited by 22 publications

(17 citation statements)

References 10 publications

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“…This model was, in turn, based on work in literary theory and linguistics, most directly Dolezˇel (1973), Uspensky (1973), Kuroda (1973Kuroda ( , 1976, Chatman (1978), Cohn (1978), Fodor (1979), and Banfield (1982). Our work capturing nested levels of attribution (''nested sources'') was inspired by work on propositional attitudes and belief spaces in artificial intelligence (Wilks and Bien, 1983;Asher, 1986;Rapaport, 1986) and linguistics (Fodor, 1979;Fauconnier, 1985).…”

Section: Related Workmentioning

confidence: 99%

Annotating Expressions of Opinions and Emotions in Language

2005

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This paper describes a corpus annotation project to study issues in the manual annotation of opinions, emotions, sentiments, speculations, evaluations and other private states in language. The resulting corpus annotation scheme is described, as well as examples of its use. In addition, the manual annotation process and the results of an inter-annotator agreement study on a 10,000-sentence corpus of articles drawn from the world press are presented.

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Section: Related Workmentioning

confidence: 99%

Annotating Expressions of Opinions and Emotions in Language

2005

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“…Its forerunners are considered to be projects investigating "belief analysis." Both Wilks [30] and Carbonell [31] generated a computer system for analyzing subjective understanding. During the late 1980s and 1990s, studies were mainly focusing on the interpretation of narrative [32,33] directionality of a sentence [34], and point of view (POV) tracking: the storyteller's POV [35] or a certain character's psychological POV [36,37].…”

Section: Sentiment Analysis and Social Media: Background And Related mentioning

confidence: 99%

Let’s play on Facebook: using sentiment analysis and social media metrics to measure the success of YouTube gamers’ post types

Poecze

Ebster

Strauß

2019

Pers Ubiquit Comput

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This paper discusses the analysis results of successful self-marketing techniques on Facebook pages in the cases of three YouTube gamers: PewDiePie, Markiplier, and Kwebbelkop. The research focus was to identify significant differences in terms of the gamers' user-generated Facebook metrics and commentary sentiments. Analysis of variance (ANOVA) and k-nearest neighbor sentiment analysis were employed as core research methods. ANOVA of the classified post categories revealed that photos tended to show significantly more user-generated interactions than other post types, while, on the other hand, re-posted YouTube videos gained significantly fewer numbers in the retrieved metrics than other content types. K-nearest neighbor sentiment analysis pointed out underlying follower negativity in cases where user-generated activity was relatively low, thereby improving the understanding of the opinion of the masses previously hidden behind metrics such as the number of likes, comments, and shares. The paper at hand highlights the methodological design of the study as well as a detailed discussion of key findings and their implications, and future work. The results per se indicate the need to utilize natural language processing techniques to optimize brand communication on social media and highlight the importance of considering machine learning sentiment analysis techniques for a better understanding of consumer feedback.

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“…If an impliciture is computed, the choice and speed of choosing a compatible response should be fast 33 See Bach and Harnish (1979, chapter 1) for the notion and role of "mutual beliefs" in communication, and see Wilks and Bien (1983) for the idea of making a piece of information "mutual" as the need arises. Taylor (2001) also sees such background information as playing a decisive role, though he does not relate it to Levinson's heuristic, whereas Brennan (2003) does.…”

Section: Cancelling Contextmentioning

confidence: 99%

3. Skating along the syntactic verge: Experimental pragmatics and understood elements of content

Garrett¹,

Harnish²

2009

Linguistik Aktuell/Linguistics Today

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IntroductionWhen we understand language, we are, by conventional construal of the process, reconstructing the speaker's thought. The physical elements of an utterance are data that listeners use to identify and represent the relevant thought of the speaker. But, that is not all the 'data'. Indispensable additional information resides in the context of utterance and in background information in the mind of the listener. The dynamic combination of these two information sources with the physical form of the utterance determines the outcome of the comprehension process.The notion that the explicit form of an utterance is the primary driver of language processing is often described as a kind of 'bottom up' priority for language comprehension, and there is much in the experimental literature on language processing to support some version of that idea. One does, after all, want some defense against wholesale hallucination. But, one does not march very far into the web of language use before encountering equally strong evidence for constraints derived from the projection of several kinds of background information onto the acoustic (or orthographic, or signed) data stream. These range from the sublimely metaphoric to the most mundanely literal. How is the balance struck between form-driven and knowledge-driven processing in a way that combines veridical perception with our fluent appreciation of the force of an unexpected turn of phrase, a bit of whimsy, or a creative metaphor? We are inclined to the view that the answer lies in the multi-systems approaches that have emerged in many cognitive domains over the past couple of decades. Wherever the research probe has been stuck --memory, human judgment and decision making, mathematical reasoning, social reasoning, and so on, we find a collection of specialized systems from whose interaction the fabric of performance in that domain emerges. We will return to this issue.The infrastructure of phonetics, phonology, morphology, and phrasal syntax are the conservative focus of bottom up analysis approaches to language. Processes based on such structures deal in a sharply limited range of information, and their real-time application to the analysis of a signal is, while complex, demonstrably manageable. Matters become more difficult as syntax becomes more complex and as information not explicitly represented in standard grammar is required for capturing interpretive options. The difficulty is, as Fodor (1983) outlined the matter in his monograph on modularity, that the range of facts to which interpretation of a sentence is potentially responsive is open. This complicates enormously the computational problem that must be solved in real time. It is the limitation on access to information that motivates the informational encapsulation move in modularity theory. And, it is the unencapsulated nature of information relevant to the interpretive nexus of utterance, speaker and context that makes the pragmatic problem so acutely compelling. We know that people can solve this problem and th...

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Beliefs, points of view, and multiple environments

Cited by 22 publications

References 10 publications

Annotating Expressions of Opinions and Emotions in Language

Annotating Expressions of Opinions and Emotions in Language

Let’s play on Facebook: using sentiment analysis and social media metrics to measure the success of YouTube gamers’ post types

3. Skating along the syntactic verge: Experimental pragmatics and understood elements of content

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