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Following the Common Thread Through Word Hierarchies

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Machine Learning and Data Mining in Pattern Recognition (MLDM 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10934))

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Abstract

In this paper we develop a new algorithm for automatic taxonomy construction from a text corpus. In contrast to existing work, our objective is not to develop a general purpose lexicon or ontology but to identify the structure in a time–ordered sequence of documents. The idea is to identify “lead” words by which we are able to follow the common thread in the public discourse on a specific topic. Our taxonomy represents the backbone of the discourse (including names of protagonists and places) and may change over time. It is thus less rigid and universal than a lexicon and instead targets relationships that are valid in a given context. We present an example to illustrate the idea.

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Notes

  1. 1.

    Adjacency pairs constitute the central organizing format in natural conversations. They consist of two turns by two different speakers which are relatively ordered. The so–called “first pair part” initiates the exchange whereas the “second pair part” responds by providing a relevant follow–up statement. In this paper, we assume that the responses are always “pair–type related”; by starting with a filtered sub–corpus we exclude improper pairings whose dialogue–equivalent would roughly read: “Would you like some tea?”–“Hi!” [21].

  2. 2.

    This level of \(\theta _0\) is thus 1.5 times the row sum in the normalized matrix C.

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Authors and Affiliations

  1. University of Zurich, Zürich, Switzerland

    Matthias J. Feiler

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  1. Matthias J. Feiler
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Correspondence to Matthias J. Feiler .

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  1. Institute of Computer Vision and Applied Computer Sciences, Leipzig, Germany

    Petra Perner

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Feiler, M.J. (2018). Following the Common Thread Through Word Hierarchies. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2018. Lecture Notes in Computer Science(), vol 10934. Springer, Cham. https://doi.org/10.1007/978-3-319-96136-1_13

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  • DOI: https://doi.org/10.1007/978-3-319-96136-1_13

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  • Online ISBN: 978-3-319-96136-1

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