Discourse Markers as the Classificatory Factors of Speech Acts
5532Authors
nan
Abstract
Abstract“Since the debut of the speech act theory, the classification standards of speech acts have been in dispute. Traditional abstract taxonomies seem insufficient to meet the needs of artificial intelligence for identifying and even understanding speech acts. To facilitate the automatic identification of the communicative intentions in human dialogs, scholars have tried some data-driven methods based on speech-act annotated corpora. However, few studies have objectively evaluated those classification schemes. In this regard, the current study applied the frequencies of the eleven discourse markers (oh, well, and, but, or, so, because, now, then, I mean, and you know) proposed by Schiffrin (1987) to investigate whether they can be effective indicators of speech act variations. The results showed that the five speech acts of Agreement can be well classified in terms of their functions by the frequencies of discourse markers. Moreover, it was found that the discourse markers well and oh are rather efficacious in differentiating distinct speech acts. This paper indicates that quantitative indexes can reflect the characteristics of human speech acts, and more objective and data-based classification schemes might be achieved based on these metrics.”
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Discourse Markers as the Classificatory Factors of Speech Acts Da Qi1, Chenliang Zhou1, Haitao Liu∗1,2,� 1Department of Linguistics, Zhejiang University, China 2Center for Linguistics and Applied Linguistics, Guangdong University of Foreign Studies, China {da.qi,cl.zhou}@zju.edu.cn, lhtzju@yeah.net Abstract Since the debut of the speech act theory, the classification standards of speech acts have been in dispute. Traditional abstract taxonomies seem insufficient to meet the needs of artificial intel- ligence for identifying and even understanding speech acts. To facilitate the automatic identifi- cation of the communicative intentions in human dialogs, scholars have tried some data-driven methods based on speech-act annotated corpora. However, few studies have objectively evalu- ated those classification schemes. In this regard, the current study applied the frequencies of the eleven discourse markers (oh, well, and, but, or, so, because, now, then, I mean, and you know) proposed by Schiffrin (1987) to investigate whether they can be effective indicators of speech act variations. The results showed that the five speech acts of Agreement can be well classified in terms of their functions by the frequencies of discourse markers. Moreover, it was found that the discourse markers well and oh are rather efficacious in differentiating distinct speech acts. This paper indicates that quantitative indexes can reflect the characteristics of human speech acts, and more objective and data-based classification schemes might be achieved based on these metrics. 1 Discourse Markers and the (Dis)agreement Continuum A discourse marker (DM) is a word or phrase that people often use in the process of communication, and its main function is to coordinate and organize discourse to ensure the smooth flow of conversation. In addition, as a carrier of pragmatic information, it usually reflects speakers’ mental states and com- municative intentions, thus facilitating pragmatic inference (Furk´o, 2020). In this regard, Fraser (1996, p.68) defined DMs as “linguistically encoded clues which signal the speaker’s potential communicative intentions.” Although scholars have never reached a consensus on the definition of DMs, no one would doubt their diverse discursive functions and the capability to transmit communicative intentions. When analyzing the functions of DMs, scholars also differ considerably in terms of their frameworks and research paradigms. Ariel (1998) distinguished DMs from a semantic perspective: a DM either possesses a semantic meaning, which is interpreted in a particular context with some connection to its form (e.g., and and I mean); or it does not contain any semantic information (e.g., well and oh). However, Matei (2010) pointed out that although some DMs contain rich semantic information, there are particular contexts in which the communicative intention it conveys is not related to the semantic information it carries. For example, in some cases, the DM and can be used as a discourse continuative, filler word, and buffer term, etc. Some scholars analyzed the range of functions through the functional-cognitive approach, which shows that DMs have a specific rather than a completely arbitrary range of functions (Redeker, 2006; Fischer, 2006). Ariel (1998, pp.242-243) also expressed support for the non-arbitrary nature of DM functions. She explicated this view …
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Abstract“Since the debut of the speech act theory, the classification standards of speech acts have been in dispute. Traditional abstract taxonomies seem insufficient to meet the needs of artificial intelligence for identifying and even understanding speech acts. To facilitate the automatic identification of the communicative intentions in human dialogs, scholars have tried some data-driven methods based on speech-act annotated corpora. However, few studies have objectively evaluated those classification schemes. In this regard, the current study applied the frequencies of the eleven discourse markers (oh, well, and, but, or, so, because, now, then, I mean, and you know) proposed by Schiffrin (1987) to investigate whether they can be effective indicators of speech act variations. The results showed that the five speech acts of Agreement can be well classified in terms of their functions by the frequencies of discourse markers. Moreover, it was found that the discourse markers well and oh are rather efficacious in differentiating distinct speech acts. This paper indicates that quantitative indexes can reflect the characteristics of human speech acts, and more objective and data-based classification schemes might be achieved based on these metrics.”
Publication Details
- Paper ID:
- 5532
- Year:
- 2022
- Publication:
- TIPSTER
- Publication Type:
- conf
- DOI:
- nan