Assessing agreement on classification tasks: the kappa statistic
Computational Linguistics
A maximum entropy approach to natural language processing
Computational Linguistics
Nymble: a high-performance learning name-finder
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Detecting action-items in e-mail
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Enriching the knowledge sources used in a maximum entropy part-of-speech tagger
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
Toward joint segmentation and classification of dialog acts in multiparty meetings
MLMI'05 Proceedings of the Second international conference on Machine Learning for Multimodal Interaction
Detecting action items in multi-party meetings: annotation and initial experiments
MLMI'06 Proceedings of the Third international conference on Machine Learning for Multimodal Interaction
Automatic annotation of dialogue structure from simple user interaction
MLMI'07 Proceedings of the 4th international conference on Machine learning for multimodal interaction
The CALO meeting assistant system
IEEE Transactions on Audio, Speech, and Language Processing
Participants' personal note-taking in meetings and its value for automatic meeting summarisation
Information Technology and Management
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Identification of action items in meeting recordings can provide immediate access to salient information in a medium notoriously difficult to search and summarize. To this end, we use a maximum entropy model to automatically detect action item-related utterances from multi-party audio meeting recordings. We compare the effect of lexical, temporal, syntactic, semantic, and prosodic features on system performance. We show that on a corpus of action item annotations on the ICSI meeting recordings, characterized by high imbalance and low inter-annotator agreement, the system performs at an F measure of 31.92%. While this is low compared to better-studied tasks on more mature corpora, the relative usefulness of the features towards this task is indicative of their usefulness on more consistent annotations, as well as to related tasks.