NLP@Pitt

Benjamin Han: A Constraint-based Framework for Resolution of Time in Natural Language

Abstract: Temporal information plays a crucial role in many natural language (NL) applications, yet automatic interpretation and reasoning of the information has not seen wide use in practical systems. Solving this problem requires: (1) a suitably designed semantic representation of time that is rich enough to capture the meaning conveyed in NL yet compositional enough to facilitate the construction of a robust syntax-semantic interface; and (2) an appropriate inference mechanism that can work with the representation to reason with a captured temporal scenario. In this talk I will describe a constraint-based framework for processing temporal information in NL. The constraint nature of the approach makes it possible to deal with under-specification and mixed granularities one often encounters in NL. At the lower level our two-tiered framework models a human calendar (such as Gregorian) as a constraint system. Information coming from temporal expressions, verbal tense/aspect and other prepositional phrases is captured in a novel representation called Time Calculus for Natural Language (TCNL), and each TCNL formula is used to instantiate constraint satisfaction problems (CSP) over the calendar model. At the higher level the framework captures a temporal scenario described in NL as a temporal constraint satisfaction problem (TCSP). The solutions to these constraint satisfaction problems can then be solved using conventional (but modified) methods such as AC3 and the all-pair-shortest-path algorithm. Finally queries can be formulated by relating TCSPs via hypothetical constraints, and they can be answered by solving for the consistency of the merged TCSPs.

Humor: Prosody Analysis and Automatic Recognition for FRIENDS

Speaker: Amruta Purandare

Purpose: Prelim Exam

Abstract: We analyze humorous spoken conversations from a classic comedy television show, FRIENDS, by examining acoustic-prosodic and linguistic features and their utility in automatic humor recognition. Using a simple annotation scheme, we automatically label speaker turns in our corpus that are followed by "laughs" as Humorous, and the rest as Non-Humorous. Our humor-prosody analysis reveals significant differences in prosodic characteristics (such as pitch, tempo, energy etc.) of humorous and non-humorous speech. Humor recognition was carried out using standard supervised learning classifiers, and shows promising results significantly above the baseline.