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A Structuralist Approach for Personal Knowledge Exploration Systems on Mobile Devices

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Text Mining

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Abstract

We describe the reasons and choices we made when designing an architecture for a multilingual Natural Language Processing (NLP) system for mobile devices. The most tangible limitations and problems are limited processing power of mobile devices, strong influence of idiolect (or generally personal language usage differentiation between individual users in their personal communication), effort required to port the NLP system to multiple languages, and finally the additional processing layers required when dealing with real-world data as opposed to controlled academic set-ups. Our solution is based on a strict differentiation between server-side preprocessing and client-side processing, as well as maximized usage of unsupervised techniques to avoid the problems posed by personal language usage variations. Hence it represents an adequate combination of solutions to provide robust NLP despite all these limitations.

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Notes

  1. 1.

    Assuming the dichotomy between structured and unstructured data to be loosely defined as “information or pieces of data stored in explicit relations with each other (cf. relational data bases) to be structured data and information stored without explicit relations within unanalyzed texts to be unstructured”. Another possible definition is that “any data base is structured if it is possible to use a simple and precise query system which guarantees to retrieve a particular piece of information if it exists”. No current system is able to achieve that fully with textual information.

  2. 2.

    Input by people educated in a relevant field of linguistics.

  3. 3.

    rtw.ml.cmu.edu/rtw, retrieved on 24.03.2014.

  4. 4.

    www.google.com/insidesearch/features/search/knowledge.html, retrieved on 24.03.2014.

  5. 5.

    www-05.ibm.com/de/watson, retrieved on 24.03.2014.

  6. 6.

    The product is an email client with enhanced NLP capabilities; see http://mailbe.at.

  7. 7.

    trec.nist.gov.

  8. 8.

    This equals an average of more than 18 person name tokens per mail. Besides many false positives (classified incorrectly due to broken contexts in log file texts, quoted mails and signature parts), most of them can be verified as correct.

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Authors and Affiliations

  1. ExB Research & Development GmbH, Seeburgstr. 100, 04103, Leipzig, Germany

    Stefan Bordag, Christian Hänig & Christian Beutenmüller

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  1. Stefan Bordag
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  2. Christian Hänig
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  3. Christian Beutenmüller
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Correspondence to Stefan Bordag .

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Editors and Affiliations

  1. Computer Science Department, Technische Universität Darmstadt FG Language Technology, Darmstadt, Germany

    Chris Biemann

  2. Computer Science Department, Goethe University WG Text Technology, Frankfurt am Main, Hessen, Germany

    Alexander Mehler

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Bordag, S., Hänig, C., Beutenmüller, C. (2014). A Structuralist Approach for Personal Knowledge Exploration Systems on Mobile Devices. In: Biemann, C., Mehler, A. (eds) Text Mining. Theory and Applications of Natural Language Processing. Springer, Cham. https://doi.org/10.1007/978-3-319-12655-5_6

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  • DOI: https://doi.org/10.1007/978-3-319-12655-5_6

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