Jansche 2002 Information extraction from voicemail transcripts

Citation

Jansche, M. and Abney, S. 2002. Information Extraction from Voicemail Transcripts. In Proceedings of ACL-EMNLP.

Online version

An online version of this paper is available [1].

Summary

This paper introduces a simple yet effective way to extract Caller Phrase/Name and Phone Number from the voicemail transcripts. The author presents the detailed empirical results and statistics drawn from the corpus.

Key Contributions

The paper presents the following key findings between the trade-off of heuristic based simple classifier and ngram based sophisticated classifier

• The second one might have serious over fitting problems and prone to errors in unseen values of attributes (for example in ASR outputs)

• The first one exploits both the linguistics intuitions and empirical distributions thus is able to rely on strong heuristics and simple classifier, and the paper introduces an interesting two phrases approach on such learning problem

Introduction

The paper first introduces the major problem of information extraction for voicemail is to identify the caller and a call back number if available. This, if not extracted, will take a person 36 seconds in average since she/he has to listen to the whole voicemail.

This paper focuses on only the transcribed voicemail text instead of including a speech recognition front-end. However, it still differs from traditional

Variations of MeMMs

The paper produces several variations of the basic MeMM architecture explained above:

• Factored state representation

To deal with data sparseness problem in standard MeMMs (due to $O(|S|^{2})$ transition parameters), one can avoid having S different transition functions (one for each state), and just maintain one function, which uses information about the previous state as features. This reduces the expressive power of the model but allows sharing of information across states and alleviates sparseness problems.

• Observations in states instead of transitions

Rather than combining transition and emission parameters into a single function, one could represent the transition probabilities as a standard multinomial, and P(S|O) using a Maxent model. This may also help with sparseness.

• Environmental model for reinforcement learning

The transition function can also include an action, resulting in a model suitable for representing the environment of a reinforcement agent.

Experiments

The authors trained 4 different types of models to classify lines from Usenet files into one of 4 categories: head, question, answer and tail. They used a set of 24 boolean features. The types of models they trained were: ME-Stateless (non-sequential Maxent), TokenHMM (a standard 4-state fully connected HMM), FeatureHMM (an HMM where the lines i.e. obsevations were replaced by their corresponding features), and the MeMM model described above. They found that the MeMM outperformed the other approaches.

Related papers

The Huang et al., 2001 paper discussed a very similar problem but rather with a traditional perspective, it studied three approaches: hand-crafted rules, grammatical inference of sub-sequential transducers and log-linear classifier with bi-gram and tri-gram features, which is essentially the same as in Ratnaparkhi, 1996 paper on Maxent POS tagging.