The Above Picture is Related Image of Another Journal
The Role of Background Knowledge in Sentence in addition to Discourse Proce
Denison University, US has reference to this Academic Journal, The Role of Background Knowledge in Sentence in addition to Discourse Processing Thesis Proposal Raluca Budiu February 9, 2000 Metaphors Time is money. People from all cultures use metaphors on an every-day basis, irrespective of their level of education. Language is full of frozen metaphors (Adam?s apple, leg of a table, etc.) People understand (most) metaphors easily. ?Mistakes? People make mistakes when they speak. Often people do not notice mistakes in addition to can understand the message communicated: How many animals of each kind did Moses take on the ark? It?s hard in consideration of people not so that ignore mistakes.
Related University That Contributed for this Journal are Acknowledged in the above Image
Memory in consideration of Text People interpret new stories in terms of past experiences. Doing that helps them remember the new stories better. Doing than makes them deform the actual facts. Motivation Metaphors ?Mistakes? Memory in consideration of text Claim: all are facets of the same cognitive mechanism, which: accounts in consideration of both fallibility in addition to robustness uses background knowledge as a heuristic in service of the current goal. Thesis Topic: Comprehension At the semantic level, comprehension works bottom-up: all the information available is used so that find an interpretation; top-down: the interpretation is further used so that help comprehension or recall. Proof: a unique computational model in ACT-R (Anderson & Lebiere, 1998) explaining in addition to unifying phenomena from various domains; satisfying a number of computational in addition to empirical (i.e. fitting actual behavioral data) constraints.
Behavioral Data Metaphor understanding; Semantic illusions; Text memory: Lexical Ambiguities. Overview Thesis topic; A model in consideration of sentence comprehension; Empirical constraints; Computational constraints; Summary in addition to work plan. Semantic Interpretation Understanding a sentence = finding a matching interpretation/context in the background knowledge.
Today?s Topics: 1. Algorithms Algorithms ! What is an algorithm? An algorithm? Complexity of algorithms Pseudocode 2. Division algorithms Notation in addition to terms Notation in addition to terms Solve the equation Solve the equation A DIVMOD algorithm A DIVMOD algorithm A DIVMOD algorithm
How Does the Model Work? Ark context How many did Farm context Ark context Ark context Farm context raise farm animals father Farm prop agent verb place-oblique patient on the take ark animals Noah Ark prop agent verb place-oblique patient Incremental From left so that right omitting Model in the Absence of Context Priming Context found? Read word Extract Word Meaning Context? Word matches context? Find context Old words match? yes no yes no no no yes yes Context Priming How many animals did Noah take on the ark? 1. Boat or ship held so that resemble that in which Noah in addition to his family were preserved from the Deluge 2. A repository traditionally in or against the wall of a synagogue in consideration of the scrolls of the Torah Ark story Noah animals took ark(1) agent verb place-oblique patient Different processing at the beginning in addition to at the end of the sentence.
Model With Context Priming Read word Extract Context Role Context role matches word? Find context Old words match? yes no no no yes yes Context found? Sentence not comprehended no Distributed Meaning Assumption Bible char Navigator Married Patriarch Noah ?Noah? meaning word meaning meaning meaning Meaning retrieval = extracting word features; Replace word meaning alongside feature as unit of processing; Model remains the same. Speak very briefly Context Finding With Distributed Meanings Bible char Married Patriarch Noah Noah word meaning meaning meaning Jesus context Moses context Jesus context Noah context Moses context took the animals on the ark. Show It only if you get questions
Summary of the Model Incremental; Trial-and-error strategy; Mixture of bottom-up in addition to top-down strategies; Incomplete processing (aka symbolic partial matching) at the word meaning level (not all features extracted); at the sentence level; No syntactic processing: thematic roles are inputs. Overview Thesis Topic; Model; Empirical constraints; Computational constraints; Summary in addition to work plan. Metaphor-related Phenomena Effects of position on metaphor understanding (Gerrig & Healy, 1983); Effects of metaphoric truth on the judgement in addition to recall of sentences of the type Some As are Bs (Glucksberg, Glidea & Bookin, 1982); Interferences of literal in addition to metaphoric truth on sentence judgements (Keysar, 1989); Effects of context length on metaphor understanding (Ortony, Schallert, Reynolds & Antos, 1978); Comprehension differences between different types of metaphors (Gibbs, 1990; Ortony et al. 1978; our data).
Metaphor Position Effects Metaphor-first sentences take longer so that comprehend than metaphor-second sentences(Gerrig & Healy, 1983). Container context Container context Stars context Stars context Stars context Drops of molten silver filled the sky The sky was filled alongside drops of molten silver 4.21s (4.23s) 3.53s (2.84s) * * Predictions * Effects of Metaphoric Truth Some roads are snakes > Some flutes are jails (Glucksberg et al., 1982): snakes needs so that be processed more deeply in order in consideration of Some roads are snakes so that be judged as false. Congruent sentences < incongruent sentences (Keysar, 1989): All features are equally informative in the congruent conditions. RT RT hide Types of Metaphors Literal sentences are as fast so that understand as metaphorical sentences (Ortony et al., 1978): The hens clucked noisily. Metaphoric anaphoras are sometimes harder so that understand than equivalent literals (Gibbs, 1990): The creampuff did not show up in consideration of the box match. Does the literality of a metaphoric sentence make a difference? The hens/women clucked/talked noisily. hide What Are Semantic Illusions? How many animals of each kind did Moses take on the ark? Semantic illusions are very robust (Reder & Kusbit, 1991); however, not anything can make an illusion. Good vs. bad illusions: How many animals did Adam take on the ark? Semantic Illusion Datasets Illusion rates in consideration of good in addition to bad distortions (Ayers, Reder & Anderson, 1996); Percent correct in consideration of good in addition to bad distortions in the gist task (Ayers et al., 1996); Latencies in the literal in addition to gist task (Reder & Kusbit, 1991); Processing of semantic anomalies in addition to contradictions (Barton & Sanford, 1993); When an aircraft crashes, where should the survivors be buried? vs. When a bicycle accident occurs where should the survivors be buried? Good vs. Bad Illusions All levels of distortion are significantly different from one another. Gist Task People are faster in addition to very good at performing the gist task (Reder & Kusbit, 1991); Undistorted > Bad Hide this; Meaning Overlap Moses Egyptian Patriarch First man Eve Adam Navigator Noah Married Bible char Eden born ?Noah? ?Moses? ?Adam? hide Modeling Semantic Illusions take ark animals Noah Ark prop agent verb place-oblique patient Moses Adam Model says ?Distorted? if it finds no interpretation; Key idea: meaning overlap (supported by van Oostendorp & Mul, 1990; van Oostendorp & Kok, 1990); Model predicts an effect of position of distortion in the sentence: late distortions are harder so that detect.
Memory in consideration of Text Prior schemas can influence text memory (Bartlett, 1932; Bransford & Johnson, 1972; etc.); If a text is consistent alongside a pre-existent script (paradigmatic situation/previous experience) subjects recall more propositions from the text, but also make more script-consistent intrusions (Owens, Bower & Black, 1979). Text Memory Datasets Recall in addition to recognition of sentences from multiple episodes related or not by a common setting (Owens et al., 1979); Interferences from related stories on recall in addition to recognition of text (Bower, Black & Turner, 1979); Text recall in the presence or absence of a topic (Bransford & Johnson, 1972); Recall of single, related in addition to unrelated facts (Bradshaw in addition to Anderson, 1982). Interferences Among Related Stories The number of intrusions can increase if subjects study more variants of the same script (Bower, Black & Turner, 1979): At the Dentist?s – about Bill At the Doctor?s – about Tom
Work Plan Modeling in addition to parameter fitting; Data collection: metaphors in addition to semantic illusions; The model still has so that solve the more difficult problems of discourse representation. Garden path Lexical ambiguity Text memory Semantic illusions Metaphor 20% 10% 15% 30% 25%
Gelt, Jessica Meteorologist
Gelt, Jessica is from United States and they belong to Meteorologist and work for CBS 5 News at Noon – KPHO-TV in the AZ state United States got related to this Particular Article.
Journal Ratings by Denison University
This Particular Journal got reviewed and rated by Gist Task People are faster in addition to very good at performing the gist task (Reder & Kusbit, 1991); Undistorted > Bad Hide this; Meaning Overlap Moses Egyptian Patriarch First man Eve Adam Navigator Noah Married Bible char Eden born ?Noah? ?Moses? ?Adam? hide Modeling Semantic Illusions take ark animals Noah Ark prop agent verb place-oblique patient Moses Adam Model says ?Distorted? if it finds no interpretation; Key idea: meaning overlap (supported by van Oostendorp & Mul, 1990; van Oostendorp & Kok, 1990); Model predicts an effect of position of distortion in the sentence: late distortions are harder so that detect. and short form of this particular Institution is US and gave this Journal an Excellent Rating.