Using Speech Acts to Categorize Email and Identify Email Genres

Goldstein, Jade; Sabin, Roberta Evans

doi:10.1109/hicss.2006.528

Cited by 21 publications

(15 citation statements)

References 18 publications

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“…Cohen, Carvalho, & Mitchell, 2004), on the other hand, do use e-mail data (researcher generated), but they annotate speech acts at the message rather than at the utterance level. Message-rather than utterance-level annotation is also preferred by Leuski (2005) and Goldstein and Sabin (2006). We chose to annotate at the utterance level for two reasons.…”

Section: Speech Act Identification and Nlpmentioning

confidence: 99%

IDENTIFYING SPEECH ACTS IN E‐MAILS: TOWARD AUTOMATED SCORING OF THE TOEIC^® E‐MAIL TASK

Felice

Deane

2012

ETS Research Report Series

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Since its 1947 founding, ETS has conducted and disseminated scientific research to support its products and services, and to advance the measurement and education fields. In keeping with these goals, ETS is committed to making its research freely available to the professional community and to the general public. Published accounts of ETS research, including papers in the ETS Research Report series, undergo a formal peer-review process by ETS staff to ensure that they meet established scientific and professional standards. All such ETS-conducted peer reviews are in addition to any reviews that outside organizations may provide as part of their own publication processes. As part of its nonprofit mission, ETS conducts and disseminates the results of research to advance quality and equity in education and assessment for the benefit of ETS's constituents and the field.To obtain a PDF or a print copy of a report, please visit: AbstractThis study proposes an approach to automatically score the TOEIC ® Writing e-mail task. We focus on one component of the scoring rubric, which notes whether the test-takers have used particular speech acts such as requests, orders, or commitments. We developed a computational model for automated speech act identification and tested it on a corpus of TOEIC responses, achieving up to 79.28% accuracy. This model represents a positive first step toward the development of a more comprehensive scoring model. We also created a corpus of speech actannotated native English workplace e-mails. Comparisons between these and the TOEIC data allow us to assess whether English learners are approximating native models and whether differences between native and non-native data can have negative consequences in the global workplace.

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Section: Speech Act Identification and Nlpmentioning

confidence: 99%

IDENTIFYING SPEECH ACTS IN E‐MAILS: TOWARD AUTOMATED SCORING OF THE TOEIC^® E‐MAIL TASK

Felice

Deane

2012

ETS Research Report Series

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“…The papers on digital document genres demonstrate the variety of ways research issues in electronic document management, organizational communication, document production and use, Web structuring, information retrieval, human-computer interaction, and e-democracy can benefit from and be informed by a genre approach (e.g., Bergquist & Ljungberg, 1999;Goldstein & Sabin, 2006;Karjalainen et al, 2000;Rehm, 2002;Roussinov, Crowston, Nilan, Kwasnik, Liu, & Cai, 2001;Saebø & Päivärinta, 2005;Shepherd & Watters, 1998;Toms & Campbell, 1999;Tyrväinen & Päivärinta, 1999;Watters & Shepherd, 1997;Yates, Orlikowski, & Rennecker, 1997;Yates & Sumner, 1997). Many of these studies are helpful because they illustrate the depth and social and humanistic rationale with which classic LIS research topics can be approached, a depth and rationale some topics may not yet have had but nevertheless have strongly needed.…”

Section: Genre In Library and Information Studiesmentioning

confidence: 99%

The concept of genre in information studies

Andersen

2008

Annual Review Info Sci & Tec

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“…The intent was identified with help of a small ontology of email acts, where the main actions were "request," "propose," "amend," "commit," "deliver," and the subjects of these actions were "information," "meeting," "data," and so forth. Goldstein and Sabin (2006) took a broader look at the email tasks and defined 12 email genres according to their task, including not only the familiar directives, commitments, and requests for information but also expression of feelings, document forwarding, and advertising and spam. Lampert, Dale, and Paris (2008a) added details to the analysis of requests and commitments in workplace email.…”

Section: When Will the Rest Of It Come?mentioning

confidence: 99%

Automated email answering by text‐pattern matching: Performance and error analysis

2017

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Automated answering of frequent email inquiries can be implemented as a text categorization task with narrow text categories, where all messages in 1 text category have the same answer. Such email categorization should be optimized for high precision and at least acceptable recall. One such high‐precision email categorization method is matching of surface text patterns to incoming email messages. In order to assess the upper performance limits of text‐pattern matching, we conducted extensive tests with almost 10,000 messages. Our results show that automated email answering with precision around 90% and recall 50–75% is feasible. In order to achieve this, however, the system must work with multiword expressions rather than stand‐alone words. Furthermore, we argue that the system has to distinguish the context of an email inquiry from the actual need that created the inquiry—a question, request, or complaint. We have discovered and analysed 12 reasons why text‐pattern matching may fail.

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Using Speech Acts to Categorize Email and Identify Email Genres

Cited by 21 publications

References 18 publications

IDENTIFYING SPEECH ACTS IN E‐MAILS: TOWARD AUTOMATED SCORING OF THE TOEIC^® E‐MAIL TASK

IDENTIFYING SPEECH ACTS IN E‐MAILS: TOWARD AUTOMATED SCORING OF THE TOEIC^® E‐MAIL TASK

The concept of genre in information studies

Automated email answering by text‐pattern matching: Performance and error analysis

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Using Speech Acts to Categorize Email and Identify Email Genres

Cited by 21 publications

References 18 publications

IDENTIFYING SPEECH ACTS IN E‐MAILS: TOWARD AUTOMATED SCORING OF THE TOEIC® E‐MAIL TASK

IDENTIFYING SPEECH ACTS IN E‐MAILS: TOWARD AUTOMATED SCORING OF THE TOEIC® E‐MAIL TASK

The concept of genre in information studies

Automated email answering by text‐pattern matching: Performance and error analysis

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Product

Resources

About

IDENTIFYING SPEECH ACTS IN E‐MAILS: TOWARD AUTOMATED SCORING OF THE TOEIC^® E‐MAIL TASK

IDENTIFYING SPEECH ACTS IN E‐MAILS: TOWARD AUTOMATED SCORING OF THE TOEIC^® E‐MAIL TASK