Information Extraction from Voicemail

Information Extraction From Voicemail Jing Huang and Geoffrey Zweig and Mukund Padmanabhan IBM T. J. Watson Research Center Yorktown Heights, NY 10598 USA jhuang, gzweig, mukund@watson.ibm.com Abstract In this paper we address the problem of extracting key pieces of information from voicemail messages, such as the identity and phone number of the caller. This task differs from the named entity task in that the information we are inter- ested in is a subset of the named entities in the message, and consequently, the need to pick the correct subset makes the problem more difficult. Also, the caller’s identity may include informa- tion that is not typically associated with a named entity. In this work, we present three information extraction methods, one based on hand-crafted rules, one based on maximum entropy tagging, and one based on probabilistic trans- ducer induction. We evaluate their per- formance on both manually transcribed messages and on the output of a speech recognition system. 1 Introduction In recent years, the task of automatically extract- ing information from data has grown in impor- tance, as a result of an increase in the number of publicly available archives and a realization of the commercial value of the available data. One as- pect of information extraction (IE) is the retrieval of documents. Another aspect is that of identify- ing words from a stream of text that belong in pre- defined categories, for instance, “named entities” such as proper names, organizations, or numerics. Though most of the earlier IE work was done in the context of text sources, recently a great deal of work has also focused on extracting information from speech sources. Examples of this are the Spoken Document Retrieval (SDR) task (NIST, 1999), named entity (NE) extraction (DARPA, 1999; Miller et al., 2000; Kim and Woodland, 2000). The SDR task focused on Broadcast News and the NE task focused on both Broadcast News and telephone conversations. In this paper, we focus on a source of con- versational speech data, voicemail, that is found in relatively large volumes in the real-world, and that could benefit greatly from the use of IE tech- niques. The goal here is to query one’s personal voicemail for items of information, without hav- ing to listen to the entire message. For instance, “who called today?”, or “what is X’s phone num- ber?”. Because of the importance of these key pieces of information, in this paper, we focus pre- cisely on extracting the identity and the phone number of the caller. Other attempts at sum- marizing voicemail have been made in the past (Koumpis and Renals, 2000), however the goal there was to compress a voicemail message by summarizing it, and not to extract the answers to specific questions. An interesting aspect of this research is that be- cause a transcription of the voicemail is not avail- able, speech recognition algorithms have to be used to convert the speech to text and the sub- sequent IE algorithms must operate on the tran- scription. One of …

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