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RDATE:20371101T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:4f0f17b87aeb657433e3150877b77cfa CATEGORIES:RuCCS Colloquia:Spring 2014 CREATED:20151203T141544 SUMMARY:You Shall Know a Logical Form by the Company it Keeps (talk recording available) LOCATION:University of Texas at Austin\, Department of Linguistics DESCRIPTION:Logic-based semantics provides precise, structured characterizations of the meaning of natural language sentences, including the handling of quantifie rs, negation, embedded propositions, and so on. It has been out of favor in computational linguistics for a while for being too deep and "brittle", bu t with more complex tasks like Textual Entailment, it is attracting interes t again. Distributional models represent the meaning of words and phrases t hrough the contexts in which they have been observed. They offer wide-cover age lexical representations, and they can talk about different degrees of s imilarity -- both capabilities that have traditionally been lacking in logi c-based approaches, and that are sorely needed. But distributional models s till have trouble capturing the structure of sentences. We combine the two frameworks by turning distributional similarity sim(A, B) into weighted inf erence rules A -> B, and doing probabilistic inferences with Markov Logi c Networks on the resulting weighted formulas.\nThe combination of logical form and distributional semantics leads to interesting practical problems o n both the logical and the distributional side. It also leads to theoretic al questions. In the distributional tradition, the meaning of a word is giv en by its use. In logic-based semantics, the notions of truth, entailment, and reference are central. What does it mean to combine the two? I will arg ue that distributional models are connected to reference after all if we vi ew distributional similarity ratings as soft meaning postulates. \nTo view a recording of this talk click here (You will need a Rutgers NetID and pass word) (https://sakai.rutgers.edu/portal/site/876a56f9-2ad8-41da-b3e7-5bbf71 bc3450/page/0b5b24bb-566f-46db-8259-20ac975ea2d1)\n X-ALT-DESC;FMTTYPE=text/html:
Logic-based semantics provides precise, structured characterizations of the meaning of natural language sentences, including the handling of quanti fiers, negation, embedded propositions, and so on. It has been out of favor in computational linguistics for a while for being too deep and "brittle", but with more complex tasks like Textual Entailment, it is attracting inte rest again. Distributional models represent the meaning of words and phrase s through the contexts in which they have been observed. They offer wide-co verage lexical representations, and they can talk about different degrees o f similarity -- both capabilities that have traditionally been lacking in l ogic-based approaches, and that are sorely needed. But distributional model s still have trouble capturing the structure of sentences. We combine the t wo frameworks by turning distributional similarity sim(A, B) into weighted inference rules A -> B, and doing probabilistic inferences with Markov Logi c Networks on the resulting weighted formulas.
The combination of log ical form and distributional semantics leads to interesting practical probl ems on both the logical and the distributional side. It also leads to theo retical questions. In the distributional tradition, the meaning of a word i s given by its use. In logic-based semantics, the notions of truth, entailm ent, and reference are central. What does it mean to combine the two? I wil l argue that distributional models are connected to reference after all if we view distributional similarity ratings as soft meaning postulates.
< p>To vi ew a recording of this talk click here (You will need a Rutgers NetID and p assword) CONTACT:Dr. Katrin Erk X-EXTRAINFO:Dr. Katrin Erk (http://www.katrinerk.com/) DTSTAMP:20240328T193157 DTSTART;TZID=America/New_York:20140422T130000 DTEND;TZID=America/New_York:20140422T140000 SEQUENCE:0 TRANSP:OPAQUE END:VEVENT END:VCALENDAR