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Optimized Term Extraction Method Based on Computing Merged Partial C-Values

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Information and Communication Technologies in Education, Research, and Industrial Applications (ICTERI 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1175))

Abstract

Assessing the completeness of a document collection, regarding terminological coverage of a domain of interest, is a complicated task that requires substantial computational resource and human effort. Automated term extraction (ATE) is an important step within this task in our OntoElect approach. It outputs the bags of terms extracted from incrementally enlarged partial document collections for measuring terminological saturation. Saturation is measured iteratively, using our \( thd \) measure of terminological distance between the two bags of terms. The bags of retained significant terms \( T_{i} \) and \( T_{i + 1} \) extracted at i-th and i + 1-st iterations are compared \( (thd(T_{i} ,T_{i + 1} )) \) until it is detected that \( thd \) went below the individual term significance threshold. The flaw of our conventional approach is that the sequence of input datasets is built by adding an increment of several documents to the previous dataset. Hence, the major part of the documents undergoes term extraction repeatedly, which is counter-productive. In this paper, we propose and prove the validity of the optimized pipeline based on the modified C-value method. It processes the disjoint partitions of a collection but not the incrementally enlarged datasets. It computes partial C-values and then merges these in the resulting bags of terms. We prove that the results of extraction are statistically the same for the conventional and optimized pipelines. We support this formal result by evaluation experiments to prove document collection and domain independence. By comparing the run times, we prove the efficiency of the optimized pipeline. We also prove experimentally that the optimized pipeline effectively scales up to process document collections of industrial size.

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Notes

  1. 1.

    This paper is a refined and extended version of [6].

  2. 2.

    In OntoElect, we do not require the availability of this complete collection. Instead, we require that a substantial part of it is available, which presumably contains all the significant terms describing the subject domain. If so, it is further revealed that \( DSC_{sat} \subset CDC \).

  3. 3.

    UPM Term Extractor has been developed in Dr Inventor EU project. It is a Java software for extracting terms and relations from scientific papers: https://github.com/ontologylearning-oeg/epnoi-legacy. The software is available under Apache 2.0 license.

  4. 4.

    For example, the UPM Term Extractor software [43] used in our experiments, which is based on the C-value method, does not take in texts of more than 15 Mb in volume.

  5. 5.

    https://link.springer.com/journal/10618.

  6. 6.

    The increment of 20 papers has been chosen as appropriately granular for the shape of the diagrams in the figures and table length. As we have proven in Corollary 1 to Theorem 1 (Sect. 5), the size of the increment does not influence the result if \( h1 \) holds true.

  7. 7.

    DMKD-300 collection in plain texts: http://dx.doi.org/10.17632/knb8fgyr8n.1#folder-637dc34c-fa29-4587-9f63-df0e602d6e86; incrementally enlarged datasets generated of these texts: http://dx.doi.org/10.17632/knb8fgyr8n.1#folder-b307088c-9479-43fb-8197-a12a66ff685b.

  8. 8.

    The partition of the DMKD-300 collection: https://github.com/OntoElect/Data/blob/master/DMKD-300-DCF-Part.zip.

  9. 9.

    TIME collection in plain texts: http://dx.doi.org/10.17632/knb8fgyr8n.1#folder-d1e5f2b6-c51e-4572-b10d-0e2ebccead02; incrementally enlarged datasets generated of these texts: http://dx.doi.org/10.17632/knb8fgyr8n.1#folder-7f44cddd-2a9b-44ec-9e40-ecedf2a9943e.

  10. 10.

    The partition of the TIME collection: https://github.com/OntoElect/Data/blob/master/TIME-DCF-Part.zip.

  11. 11.

    The full texts were provided by Springer based on their policy on full text provision for data mining purposes: https://www.springer.com/gp/rights-permissions/springer-s-text-and-data-mining-policy/29056. The volume of the KM collection after conversion to plain texts is 413.66 Mb.

  12. 12.

    The increment of 100 papers has been chosen as appropriately granular for the shape of the diagrams in the figures.

  13. 13.

    The partition of the KM collection has not been made publicly available, as it requires additional permissions by Springer.

  14. 14.

    One may argue that the reported experiment was just an experiment with one document collection. Hence, for a different document collection the results might be different regarding the validity of \( h1 \). Our counter-argument was that the computation of С-values is collection- and domain-independent. We proved that in our domain neutrality experiment. Results are the same for a different collection representing a different subject domain.

  15. 15.

    A false positive here and in Fig. 8 is a term candidate string with high C-value, i.e. significance score, but appearing to be not a term for a human expert (domain knowledge stakeholder).

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Authors and Affiliations

  1. Department of Computer Science, Zaporizhzhia National University, Zaporizhzhia, Ukraine

    Victoria Kosa, Hennadii Dobrovolskyi & Vadim Ermolayev

  2. Ontology Engineering Group, Universidad Politécnica de Madrid, Madrid, Spain

    David Chaves-Fraga

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  1. Victoria Kosa
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  4. Vadim Ermolayev
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Correspondence to Victoria Kosa .

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Editors and Affiliations

  1. Zaporizhzhia National University, Zaporizhzhia, Ukraine

    Vadim Ermolayev

  2. Université Cote d’Azur, Nice, France

    Frédéric Mallet

  3. University of Warmia and Mazury in Olsztyn, Olsztyn, Poland

    Vitaliy Yakovyna

  4. University of Klagenfurt, Klagenfurt am Wörthersee, Austria

    Heinrich C. Mayr

  5. Kherson State University, Kherson, Ukraine

    Aleksander Spivakovsky

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Kosa, V., Chaves-Fraga, D., Dobrovolskyi, H., Ermolayev, V. (2020). Optimized Term Extraction Method Based on Computing Merged Partial C-Values. In: Ermolayev, V., Mallet, F., Yakovyna, V., Mayr, H., Spivakovsky, A. (eds) Information and Communication Technologies in Education, Research, and Industrial Applications. ICTERI 2019. Communications in Computer and Information Science, vol 1175. Springer, Cham. https://doi.org/10.1007/978-3-030-39459-2_2

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