Topic Detection and Extraction in Chat

Adams, Paige; Martell, Craig

doi:10.1109/icsc.2008.61

Cited by 59 publications

(43 citation statements)

References 3 publications

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“…A related problem of clustering utterances is defined specifically for Internet Relay Chat (IRC) and speech conversations. In this case, multiple conversations, on different topics, are mixed in and systems extract threads which separate out individual conversations (Shen et al, 2006;Adams and Martell, 2008;Elsner and Charniak, 2010).…”

Section: Related Workmentioning

confidence: 99%

Conversation Trees: A Grammar Model for Topic Structure in Forums

Louis¹,

Cohen²

2015

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Online forum discussions proceed differently from face-to-face conversations and any single thread on an online forum contains posts on different subtopics. This work aims to characterize the content of a forum thread as a conversation tree of topics. We present models that jointly perform two tasks: segment a thread into subparts, and assign a topic to each part. Our core idea is a definition of topic structure using probabilistic grammars. By leveraging the flexibility of two grammar formalisms, Context-Free Grammars and Linear Context-Free Rewriting Systems, our models create desirable structures for forum threads: our topic segmentation is hierarchical, links non-adjacent segments on the same topic, and jointly labels the topic during segmentation. We show that our models outperform a number of tree generation baselines.

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Section: Related Workmentioning

confidence: 99%

Conversation Trees: A Grammar Model for Topic Structure in Forums

Louis¹,

Cohen²

2015

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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“…Compared to our SL dataset, the NPS corpus contains more discussion about the participants' real-life interactions whereas the SL data is more heavily slanted toward discussions of the virtual world (e.g., scripting, shopping). The NPS corpus has additionally been used in a study of topic identification [1], which is something that we hope to do in future work.…”

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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“…Hughes et al [16] Detecting conversation topic: e.g. Adams and Martell [40] Detecting harassment: Yin et al [41] Authorship Attribution: e.g. Juola [42] Authorship Profiling (detecting age and gender): e.g.…”

Section: State-of-the-artmentioning

confidence: 99%

"Our Little Secret": pinpointing potential predators

Vartapetiance

Gillam

2014

Secur Inform

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The word "Paedophilia" has come a long way from its Greek origin of child-companionship to a Mental Disorder, Social Taboo and Criminal Offence. Various laws are in place to help control such behaviour, protect the vulnerable and restrain related criminal offences. However, enforcement of such laws has become a significant challenge with the advent of social media creating a new platform for this old crime. This move necessitates consideration of approaches that are suited to this new platform and the way in which it affects the Cycle of Entrapment. This paper reviews definitions of, and features of, paedophilia and other related -philias, and sexual offences against children, and seeks through the understanding of these to determine where specific detection approaches are effective. To this end, we present our own detection approach which is geared towards predatory behaviours, which can be a precursor to sexual offences against children, and which directly references this Cycle of Entrapment. Our approach has shown early promise with an F1 score of 0.66 for training data but only achieving 0.48 for testing data on a collection of chat logs of sexual predators. The results were later improved to achieve an F1 score of 0.77 for train and 0.54 for test data based on the approach.

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Topic Detection and Extraction in Chat

Cited by 59 publications

References 3 publications

Conversation Trees: A Grammar Model for Topic Structure in Forums

Conversation Trees: A Grammar Model for Topic Structure in Forums

Mining Social Interaction Data in Virtual Worlds

"Our Little Secret": pinpointing potential predators

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