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Creators/Authors contains: "Welch, Charles"

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  1. ; ; ; ( , Proceedings of the International Conference on Computational Linguistics)
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  2. ; ; ; ( , Conference on Empirical Methods in Natural Language Processing)
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  3. ; ; ; ( , IEEE intelligent systems)
    We explore the use of longitudinal dialog data for two dialog prediction tasks: next message prediction and response time prediction. We show that a neural model using personal data that leverages a combination of message content, style matching, time features, and speaker attributes leads to the best results for both tasks, with error rate reductions of up to 15\% compared to a classifier that relies exclusively on message content and to a classifier that does not use personal data. 
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  4. ; ; ; ( , Proceedings of the 20th International Conference on Computational Linguistics and Intelligent Text Processing (CICLing))
    We examine a large dialog corpus obtained from the conversation history of a single individual with 104 conversation partners. The corpus consists of half a million instant messages, across several messaging platforms. We focus our analyses on seven speaker attributes, each of which partitions the set of speakers, namely: gender; relative age; family member; romantic partner; classmate; co-worker; and native to the same country. In addition to the content of the messages, we examine conversational aspects such as the time messages are sent, messaging frequency, psycholinguistic word categories, linguistic mirroring, and graph-based features reflecting how people in the corpus mention each other. We present two sets of experiments predicting each attribute using (1) short context windows; and (2) a larger set of messages. We find that using all features leads to gains of 9-14% over using message text only. 
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