PPT-Explicitness and Inference

EXPLICIT If something is EXPLICIT its very clear and we dont need to work it out If its raining its EXPLICIT that people outside are getting wet INFERRED If something is INFERRED we work it out from what we know for sure . This information represents a place holder for the intentions of humankind a massive database of desires needs wants and likes that can be discovered subpoenaed archived tracked and exploited to all sorts of ends Such a beast has never befo re ex Daniel R. Schlegel. Department of Computer Science and Engineering. Problem Summary. Inference graphs. 2. in their current form only support propositional logic. We expand it to support . L. A. – A Logic of Arbitrary and Indefinite Objects.. Presented By: Ms. . Seawright. What does it mean to make an inference?. Make an inference.. Use what you already know.. The inference equation. WHAT I READ. Use quotes from the text and not page number for future references. Elisabet Titik Murtisari. Monash University, Australia. etmurtisari@yahoo.com.au . Introduction. The concept of explicitation itself, which is generally understood as ‘the spelling out of information which is otherwise implicit in the source text’, has been of special interest in translation studies because of its elusiveness. Different methods have been applied in the study, e.g. by the use of the discourse based concept of explicitness and the traditional encoded/inferred distinction. The studies, however, are somewhat difficult to compare since every study seems to have its own concept of explicitation. Besides, there is also a issue with the combination of the two notions of explicitness, which is often done in explicitation research. . Meeting 5: Chunk 2. “I can infer…because…and…I know”. Today’s Cluster:. Objective: . By the end of the meeting, teachers will be prepared to introduce “I can infer…because…and I know…” using the critical attributes which. . CRF Inference Problem. CRF over variables: . CRF distribution:. MAP inference:. MPM (maximum posterior . marginals. ) inference:. Other notation. Unnormalized. distribution. Variational. distribution. Protocols for Coreference Resolution. . . Kai-Wei Chang, Rajhans Samdani. , . Alla Rozovskaya, Nick Rizzolo, Mark Sammons. , and Dan Roth. . Kari Lock Morgan. Department of Statistical Science, Duke University. kari@stat.duke.edu. . with Robin Lock, Patti Frazer Lock, Eric Lock, Dennis Lock. ECOTS. 5/16/12. Hypothesis Testing:. Use a formula to calculate a test statistic. What has received only little attention so far is that the intentions represented in such Daniel R. Schlegel and Stuart C. Shapiro. <. drschleg,shapiro. >@buffalo.edu. Department of Computer Science and Engineering. L. A. – Logic of Arbitrary and Indefinite Objects. 2. Logic in Cognitive Systems. Kari Lock Morgan. Department of Statistical Science, Duke University. kari@stat.duke.edu. . with Robin Lock, Patti Frazer Lock, Eric Lock, Dennis Lock. ECOTS. 5/16/12. Hypothesis Testing:. Use a formula to calculate a test statistic. Slide #. 1. 1-sample Z-test. H. o. :. . m. = . m. o. (where . m. o. = specific value). Statistic:. Test Statistic:. . Assume. :. . s. is known. n is “large” (. so . sampling distribution is Normal. An.  inference is an idea or conclusion that's drawn from evidence and reasoning. . An . inference.  is an educated . guess.. When reading a passage: 1) Note the facts presented to the reader and 2) use these facts to draw conclusions about . LOGIC. Immediate Inference: ( Three kinds of immediate inferences). Conversion. Obversion. Contraposition. Conversion is an inference that proceeds by interchanging the subject and predicate terms of the proposition. “No men are angels” converts to “No angels are men,” and these propositions may be validly inferred from one another..