The output of an ASR system does not contain case information and punctuations. It has been shown that in the absence of punctuations extraction of different syntactic entities like parts of speech and noun phrases is not accurate (Nasukawa et. al., 2007). So IE from ASRed documents becomes harder. Miller et. al. (Miller et. al., 2000) have shown how IE performance varies with ASR noise. It has been shown that it is possible to build aggregate models from ASR data (Roy & Subramaniam, 2006). In this work topical models are constructed by utilizing inter document redundancy to overcome the noise. In this work only a few natural language processing steps have been used. Phrases have been aggregated over the noisy collection to get to the clean underlying text.
FUTURE TRENDS
More and more data from sources like chat, conver- sations, blogs, discussion groups need to be mined to capture opinions, trends, issues and opportunities. These forms of communication encourage informal language which can be considered noisy due to spell- ing errors, grammatical errors and informal writing styles. Companies are interested in mining such data to observe customer preferences and improve customer satisfaction. Online agents need to be able to understand web posts to take actions and communicate with other agents. Customers are interested in collated product reviews from web posts of other users. The nature of the noisy text warrants moving beyond traditional text analytics techniques. There is need for developing natural language processing techniques that are robust to noise. Also techniques that implicitly and explicitly tackle textual noise need to be developed.
CONCLUSION
In this chapter we have looked at information extraction from noisy text. This topic is gaining in importance as more and more noisy data gets generated and useful information needs to be obtained from this. We have presented a survey of existing techniques information extraction techniques. We have also presented some of the future trends in noisy text analytics.
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KEy TERMS
Automatic Speech Recognition: Machine recogni- tion and conversion of spoken words into text.
Data Mining: The application of analytical methods and tools to data for the purpose of identifying patterns, relationships or obtaining systems that perform useful
tasks such as classification, prediction, estimation, or affinity grouping.
Information Extraction: Automatic extraction of structured knowledge from unstructured documents.
Knowledge Extraction: Explicitation of the internal knowledge of a system or set of data in a way that is easily interpretable by the user.
Noisy Text: Text with any kind of difference in the surface form, from the intended, correct or original text.
Optical Character Recognition: Translation of images of handwritten or typewritten text (usually captured by a scanner) into machine-editable text.
Rule Induction: Process of learning, from cases or instances, if-then rule relationships that consist of
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