Proceedings of the Nineteenth Annual Meeting of the Berkeley Linguistics Society: Special Session on Syntactic Issues in Native American Languages (1993)
Choctaw, a Muskogean language, shows a complex set of restrictions on verbal prefixes which requires reference both to exponence and position class. An approach like that of Information-Based Morphology Crysmann and Bonami (2016) allows us to model the facts correctly.
In this paper, we describe a novel approach to computational modeling and understanding of social and cultural phenomena in multi-party dialogues. We developed a two-tier approach in which we first detect and classify certain sociolinguistic behaviors, including topic control, disagreement, and involvement, that serve as first-order models from which presence the higher level social roles, such as leadership, may be inferred. G. A. Broadwell et al.structure influence in the group (based on proxemics, height, vocal tone, and the like). As such, studying online chat relies on the more explicit linguistic devices necessary to convey social and cultural nuances than is typical in face-to-face or even telephonic conversations.Our objective is to develop computational models of how certain social phenomena, such as leadership, conflict, and group cohesion, are signaled and reflected in language through the choice of lexical, syntactic, semantic, and conversational forms by discourse participants. In this paper we report the results of an initial phase of our work during which we constructed a prototype system called Detecting Social Actions and Roles in Multi-party Dialogue-1 (DSARMD-1). Given a representative segment of multi-party task-oriented dialogue, DSARMD-1 automatically classifies all discourse participants by the degree to which they engage in selected sociolinguistic behaviors (SLB), such as topic control, task control, involvement, and disagreement. These are the mid-level social phenomena that are deployed by discourse participants in order to achieve or assert higher level social roles, including leadership. In this work we adopted a two-tier empirical approach where sociolinguistic behaviors are modeled through observable linguistic features that can be automatically extracted from dialogue. The high-level social roles are then inferred from a combination of sociolinguistic behaviors attributed to each discourse participant; for example, a high degree of influence and a high degree of involvement by the same person may indicate a leadership role. In this paper we limit our discussion to the first tier only: How to effectively model and classify selected sociolinguistic behaviors in multi-party dialogue.
This article describes a novel approach to automated determination of affect associated with metaphorical language. Affect in language is understood to mean the attitude toward a topic that a writer attempts to convey to the reader by using a particular metaphor. This affect, which we will classify as positive, negative or neutral with various degrees of intensity, may arise from the target of the metaphor, from the choice of words used to describe it, or from other elements in its immediate linguistic context. We attempt to capture all these contributing elements in an Affect Calculus and demonstrate experimentally that the resulting method can accurately approximate human judgment. The work reported here is part of a larger effort to develop a highly accurate system for identifying, classifying, and comparing metaphors occurring in large volumes of text across four different languages: English, Spanish, Russian, and Farsi.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.