Understanding how social power structures affect the way we interact with one another is of great interest to social scientists who want to answer fundamental questions about human behavior, as well as to computer scientists who want to build automatic methods to infer the social contexts of interactions. In this paper, we employ advancements in extra-propositional semantics extraction within NLP to study how author commitment reflects the social context of an interaction. Specifically, we investigate whether the level of commitment expressed by individuals in an organizational interaction reflects the hierarchical power structures they are part of. We find that subordinates use significantly more instances of non-commitment than superiors. More importantly, we also find that subordinates attribute propositions to other agents more often than superiors do --- an aspect that has not been studied before. Finally, we show that enriching lexical features with commitment labels captures important distinctions in social meanings.
The notion of belief that we use in this paper @cite_35 @cite_42 is closely related to the notion of factuality that is captured in FactBank @cite_40 . They capture three levels of factuality, certain (CT), probable (PB), and possible (PS), as well as the underspecified factuality (Uu). They also record the corresponding polarity values, and the source of the factuality assertions to distinguish between factuality assertions by the author and those by the agents sources introduced by the author. While FactBank offers a finer granularity, they are annotated on newswire text. Hence, we use the corpus of belief annotations @cite_42 that is obtained on online discussion forums, which is closer to our genre.
@cite_35 @cite_40 @cite_42
2006941876 2113710975
Recent work in computational linguistics points out the need for systems to be sensitive to the veracity or factuality of events as mentioned in text; that is, to recognize whether events are presented as corresponding to actual situations in the world, situations that have not happened, or situations of uncertain interpretation. Event factuality is an important aspect of the representation of events in discourse, but the annotation of such information poses a representational challenge, largely because factuality is expressed through the interaction of numerous linguistic markers and constructions. Many of these markers are already encoded in existing corpora, albeit in a somewhat fragmented way. In this article, we present FactBank, a corpus annotated with information concerning the factuality of events. Its annotation has been carried out from a descriptive framework of factuality grounded on both theoretical findings and data analysis. FactBank is built on top of TimeBank, adding to it an additional level of semantic information. The terms “belief” and “factuality” both refer to the intention of the writer to present the propositional content of an utterance as firmly believed by the writer, not firmly believed, or having some other status. This paper presents an ongoing annotation effort and an associated evaluation.