Browsing by Subject "Information structure"

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Open Access
An information-based approach to punctuation
(1998) Say, Bilge
Punctuation marks have special importance in bringing out the meaning of a text. Geoffrey Nunberg's 1990 monograph bridged the gap between descriptive treatments of punctuation and perspective accounts, by spelling out the features of a text-grammar for the orthographic sentence. His research inspired most of the recent work concentrating on punctuation marks in Natural Language Processing (NLP). Several grammars incorporating punctuation were then shown to reduce failures and ambiguities in parsing. Numberg's approach to punctuation (and other formatting devices) was partially incorporated into natural language generation systems. However, little has been done concerning how punctuation marks bring semantic and discourse cues to the text and whether these can be exploited computationally. The aim of this thesis is to analyse the semantic and discourse aspects of punctuation marks, within the framework of Hans Kamp and Uwe Reyle's Discourse Representation Theory (DRT) and its extension by Nicholas Asher, Segmented Discourse Representation Theory (SDRT), drawing implications for NLP systems. The method used is the extraction of patterns for four common punctuation marks (dashes, semicolons, colons, and parentheses) from corpora, followed by formal modeling and a modest computational prototype. Our observations and results have revealed interesting occurrences of linguistic phenomena, such as anaphora resolution and presupposition, in conjunction with punctuation marks. Within the framework of SDRT such occurrences are then tied with the overall discourse structure. The proposed model can be taken as a template for NLP software developers for making use of the punctuation marks more effectively. Overall, the thesis describes the contribution of punctuation at the orthographic sentence level to the information passed on to the reader of a text.
Unknown
Nash equilibria for exchangeable team-against-team games, their mean-field limit, and the role of common randomness
(Society for Industrial and Applied Mathematics, 2024-05-16) Sanjari, Sina; Saldı, Naci; Yüksel, Serdar
We study stochastic exchangeable games among a finite number of teams consisting of a large but finite number of decision makers as well as their mean-field limit with infinite number of decision makers in each team. For this class of games within static and dynamic settings, we introduce sets of randomized policies under various decentralized information structures with pri- vately independent or common randomness for decision makers within each team. (i) For a general class of exchangeable stochastic games with a finite number of decision makers, we first establish the existence of a Nash equilibrium under randomized policies (with common randomness) within each team that are exchangeable (but not necessarily symmetric, i.e., identical) among decision makers within each team. (ii) As the number of decision makers within each team goes to infinity (that is, for the mean-field limit game among teams), we show that a Nash equilibrium exists under randomized policies within each team that are independently randomized and symmetric among decision makers within each team (that is, there is no common randomness). (iii) Finally, we establish that a Nash equilibrium for a class of mean-field games among teams under independently randomized symmetric policies constitutes an approximate Nash equilibrium for the corresponding prelimit (exchangeable) game among teams with finite but large numbers of decision makers. (iv) We thus establish a rigor- ous connection between agent-based-modeling and team-against-team games, via the representative agents defining the game played in equilibrium, and we furthermore show that common randomness is not necessary for large team-against-team games, unlike the case with small-sized ones.