Lexical and Discourse Analysis of Online Chat Dialog

Forsythand, Eric N.; Martell, Craig

doi:10.1109/icsc.2007.55

Cited by 67 publications

(56 citation statements)

References 3 publications

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“…Much of the previous work on analyzing chat has been restricted to a small number of users and is topic-specific. One notable exception is the work by the Naval Postgraduate School on collecting and analyzing the NPS corpus [6], which is based on chat dialogs from online chat rooms. Using a combination of manual annotations and filtering techniques, Wu et al [38] divide the utterances into semantic classes; in con-trast we use semantic cues to identify social connections.…”

Section: Prior Workmentioning

confidence: 99%

Mining Social Interaction Data in Virtual Worlds

Shah

Sukthankar

2015

Communications in Computer and Information Science

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Abstract. Virtual worlds and massively multi-player online games are rich sources of information about large-scale teams and groups, offering the tantalizing possibility of harvesting data about group formation, social networks, and network evolution. However these environments lack many of the cues that facilitate natural language processing in other conversational settings and different types of social media. Public chat data often features players who speak simultaneously, use jargon and emoticons, and only erratically adhere to conversational norms. This chapter presents techniques for inferring the existence of social links from unstructured conversational data collected from groups of participants in the Second Life virtual world.

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Section: Prior Workmentioning

confidence: 99%

Mining Social Interaction Data in Virtual Worlds

Shah

Sukthankar

2015

Communications in Computer and Information Science

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“…Some researchers use the frequency of part-of-speech (POS) tags [14] to classify user profiles from a transcribed dialogical relationship. Features like: average number of words per sentence, average number of letters in a word and use of punctuation marks are often encountered in the studies leaned towards the profile identification of authors [4]. In this context, it is important to remember the lexical aspects and types of discourse (including the choice of words, the type of participation in communicative act) for the identification of the discursive features [6].…”

Section: State Of the Artmentioning

confidence: 99%

“…For the elaboration of preliminary conclusions on the configuration process of the journalist identity, we collected, stored and processed 1,463 relevant articles (summing up 28,879 words), published by three journalists: Cristian Tudor Popescu (CTP), Marius Tucă (MT), Ion Cristoiu (IC), during January -March 2013 by three important Romanian newspapers having similar profiles 4 , but usually displaying totally disjoint opinions and journalistic styles on any topic.…”

Section: The Corpusmentioning

confidence: 99%

Monitoring and Predicting Journalistic Profiles

G®fu

Cristea

2013

Computational Collective Intelligence. Technologies and Applications

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Abstract. The paper develops a pilot study aiming at finding a methodology for identifying features of journalistic writing profiles. The study is based on capturing dominant discursive tonalities, knowing that journalistic discourse entails public legitimacy. We made use of a series of natural language processing tools as preliminary steps in revealing three egocentric journalistic identities. Based on quantitative analysis, such as syntactic and lexicalsemantic, we put in evidence qualitative pragmatic aspects. The final goal of this study is to configure a tool for automatic analysis of journalistic profiles. We have concentrated on three top Romanian journalists, whose newspaper publications have been monitored over a period of three months and semiautomatically analyzed. As a result, a database with their profiles is collected and interpreted. Such a tool could be of interest to mass-media, but also to specialists in communication and public relations, to political parties and to the public opinion, in general.

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“…IM chat is subject to intensive research in conversation analysis (Orthmann, 2004;Nardi et al, 2000), computer-mediated collaborative work (Jiang and Singley, 2009) and natural language processing (Forsythand and Martell, 2007). The data sets used come from natural workspace interaction (Avrahami and Hudson, 2006) or interaction experiments (Solomon et al, 2010).…”

Section: Datamentioning

confidence: 99%

Towards Computational Models for a Long-term Interaction with an Artificial Conversational Companion

Danilava

Busemann²,

Schommer

et al. 2013

Proceedings of the 5th International Conference on Agents and Artificial Intelligence

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Abstract:In this paper we describe a design approach for an Artificial Conversational Companion according to earlier identified requirements of utility, adaptivity, conversational capabilities and long-term interaction. The Companion is aimed to help advanced learners of a foreign language to practice conversation via instant messenger dialogues. In order to model a meaningful long-term interaction with an Artificial Conversational Companion for this application case, it is necessary to understand how natural long-term interaction via chat between human language experts and language learners works. For this purpose, we created a corpus from instant messenger-based interactions between native speakers of German and advanced learners of German as a foreign language. We used methods from conversation analysis to identify rules of interaction. Examples from our data set are used to illustrate how particular requirements for the agent can be fulfilled. Finally, we outline how the identified patterns of interaction can be used for the design of an Artificial Conversational Companion.

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Lexical and Discourse Analysis of Online Chat Dialog

Abstract: One of the ultimate goals of natural language processing (NLP)

Cited by 67 publications

References 3 publications

Mining Social Interaction Data in Virtual Worlds

Mining Social Interaction Data in Virtual Worlds

Monitoring and Predicting Journalistic Profiles

Towards Computational Models for a Long-term Interaction with an Artificial Conversational Companion

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