Multilingual email zoning

Jardim, B., Rei, R., & Almeida, M. S. C. (2021). Multilingual email zoning. In EACL 2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Student Research Workshop (pp. 88-95). (EACL 2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Student Research Workshop). Association for Computational Linguistics (ACL). --------------------------- Funding Information: This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 873904. Funding Information: This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 873904. Publisher Copyright: © 2021 Association for Computational LinguisticsThe segmentation of emails into functional zones (also dubbed email zoning) is a relevant preprocessing step for most NLP tasks that deal with emails. However, despite the multilingual character of emails and their applications, previous literature regarding email zoning corpora and systems was developed essentially for English. In this paper, we analyse the existing email zoning corpora and propose a new multilingual benchmark composed of 625 emails in Portuguese, Spanish and French. Moreover, we introduce OKAPI, the first multilingual email segmentation model based on a language agnostic sentence encoder. Besides generalizing well for unseen languages, our model is competitive with current English benchmarks, and reached new state-of-the-art performances for domain adaptation tasks in English.publishersversionpublishe

Segmenting email message text into zones

In the early days of email, widely-used conventions for indicating quoted reply content and email signatures made it easy to segment email messages into their functional parts. Today, the explosion of different email formats and styles, coupled with the ad hoc ways in which people vary the structure and layout of their messages, means that simple techniques for identifying quoted replies that used to yield 95% accuracy now find less than 10% of such content. In this paper, we describe Zebra, an SVM-based system for segmenting the body text of email messages into nine zone types based on graphic, orthographic and lexical cues. Zebra performs this task with an accuracy of 87.01%; when the number of zones is abstracted to two or three zone classes, this increases to 93.60% and 91.53% respectively.10 page(s

Identifying Stylometric Correlates of Social Power

This thesis takes a stylometric approach to the measurement of social power, particularly hierarchical power in an organisational setting. Following the social constructionist view of identity, we infer that construction of identity is an ongoing process incorporating the full scope of human behaviour, including linguistic behaviour. We test the primary hypothesis that stylistic choice in language is indicative of power relations, and that a stylometric signal can be extracted from natural language to enable prediction of relationship status. Additionally, we consider the effect of individual variation versus interpersonal variation, and the effects of aggregating predictions to boost the predictive strength of the model. Three different datasets are used to validate the proposed approach across three different genres: email, spoken conversation, and online chat. We also present a vector space approach to modelling linguistic style accommodation, and undertake a preliminary examination of the correlation between linguistic accommodation and social power