Connotation Frames: A Data-Driven Investigation

40568

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Abstract

Through a particular choice of a predicate (e.g., “x violated y”), a writer can subtly connote a range of implied sentiment and presupposed facts about the entities x and y: (1) writer’s perspective: projecting x as an “antagonist” and y as a “victim”, (2) entities’ perspective: y probably dislikes x, (3) effect: something bad happened to y, (4) value: y is something valuable, and (5) mental state: y is distressed by the event. We introduce connotation frames as a rep- resentation formalism to organize these rich dimensions of connotation using typed relations. First, we investigate the fea- sibility of obtaining connotative labels through crowdsourcing experiments. We then present models for predicting the con- notation frames of verb predicates based on their distributional word representations and the interplay between different types of connotative relations. Empirical results confirm that connotation frames can be in- duced from various data sources that reflect how language is used in context. We con- clude with analytical results that show the potential use of connotation frames for an- alyzing subtle biases in online news media. 1

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Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pages 311–321, Berlin, Germany, August 7-12, 2016. c⃝2016 Association for Computational Linguistics Connotation Frames: A Data-Driven Investigation Hannah Rashkin Sameer Singh Computer Science & EngineeringYejin Choi University of Washington {hrashkin, sameer, yejin}@cs.washington.edu Abstract Through a particular choice of a predicate (e.g., “x violated y”), a writer can subtly connote a range of implied sentiment and presupposed facts about the entities x and y: (1) writer’s perspective: projecting x as an “antagonist” and y as a “victim”, (2) entities’ perspective: y probably dislikes x, (3) effect: something bad happened to y, (4) value: y is something valuable, and (5) mental state: y is distressed by the event. We introduce connotation frames as a rep- resentation formalism to organize these rich dimensions of connotation using typed relations. First, we investigate the fea- sibility of obtaining connotative labels through crowdsourcing experiments. We then present models for predicting the con- notation frames of verb predicates based on their distributional word representations and the interplay between different types of connotative relations. Empirical results confirm that connotation frames can be in- duced from various data sources that reflect how language is used in context. We con- clude with analytical results that show the potential use of connotation frames for an- alyzing subtle biases in online news media. 1 Introduction People commonly express their opinions through subtle and nuanced language (Thomas et al., 2006; Somasundaran and Wiebe, 2010). Often, through seemingly objective statements, the writer can in- fluence the readers’ judgments toward an event and their participants. Even by choosing a particular predicate, the writer can indicate rich connotative information about the entities that interact through the predicate. More specifically, through a simple Writer: “Agent violates theme.” Writer + - - - - = + - Reader - = = Agent Theme P(w ! agent) P(w ! theme) P(agent ! theme) E(agent) E(theme) V(theme) V(agent) E(theme) S(agent) S(agent) E(agent) Perspective: the writer is sympathetic towards the theme Perspective: the writer portrays the agent as being antagonistic Value: the theme must be valuable + Effect: the agent is not really affected by the violation State: the theme will be unhappy State: the agent feels indifferent Effect: the theme has been hurt Value: not clear if agent is valuable Figure 1: An example connotation frame of “violate” as a set of typed relations: perspective P(x → y), effect E(x), value V(x), and mental state S(x). statement such as “x violated y”, the writer can convey: (1) writer’s perspective: the writer is projecting x as an “antagonist” and y as a “victim”, elic- iting negative perspective from readers toward x (i.e., blaming x) and positive perspective to- ward y (i.e., sympathetic or supportive toward y). (2) entities’ perspective: y most likely feels neg- atively toward x as a result of being violated. (3) effect: something bad happened to y. (4) value: y is something valuable, since it does not make sense to violate something worthless. In other words, …

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Labeled Text

Through a particular choice of a predicate (e.g., “x violated y”), a writer can subtly connote a range of implied sentiment and presupposed facts about the entities x and y: (1) writer’s perspective: projecting x as an “antagonist” and y as a “victim”, (2) entities’ perspective: y probably dislikes x, (3) effect: something bad happened to y, (4) value: y is something valuable, and (5) mental state: y is distressed by the event. We introduce connotation frames as a representation formalism to organize these rich dimensions of connotation using typed relations. First, we investigate the feasibility of obtaining connotative labels through crowdsourcing experiments. We then present models for predicting the connotation frames of verb predicates based on their distributional word representations and the interplay between different types of connotative relations. Empirical results confirm that connotation frames can be induced from various data sources that reflect how language is used in context. We conclude with analytical results that show the potential use of connotation frames for analyzing subtle biases in online news media. 1

Publication Details

Paper ID:
40568
Year:
2016
Publication:
TIPSTER
Publication Type:
conf
DOI:
nan