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Machine learning using context vectors for object coreference resolution

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Abstract

Object coreference resolution is used in sentiment analysis to identify sentiment words referring to an aspect of an object in a document. However, this poses a challenge in natural language processing and is consequently an area of ongoing research. Further, to the best of our knowledge, object coreference resolution with more than one object has not been given much attention. To effectively address object coreference resolution, this paper proposes a method in which machine learning is applied to a large volume of textual data represented by context vectors, constituting a new form of language representation. The proposed machine learning model uses these vectors to achieve state-of-the-art performance in object coreference resolution. In addition, a combination of dependency grammar, sentiment ontology, and coreference graphs is used to obtain triplets of object, aspect, and sentiment. In experiments conducted on sentiment textual data obtained from Amazon.com, the proposed method achieved an average coreference resolution of object, aspect, and sentiment precision value of approximately 90%. This result suggests that the proposed method can contribute considerably to the field of object coreference resolution, and further research is therefore warranted.

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Notes

  1. https://www.amazon.com/ and http://snap.stanford.edu/data/.

  2. http://www.younetmedia.com.

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Funding

This research is funded by Ho Chi Minh City University of Technology (HCMUT), VNU-HCM, under Grant Number BK-SDH-2021-1585007.

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Authors and Affiliations

  1. Faculty of Computer Science & Engineering, Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh City, Vietnam

    Thuy Le Thi, Tuoi Phan Thi & Tho Quan Thanh

  2. Vietnam National University, Ho Chi Minh City, Vietnam

    Thuy Le Thi, Tuoi Phan Thi & Tho Quan Thanh

  3. Faculty of Information Technology, Industrial University of Ho Chi Minh City, Ho Chi Minh City, Vietnam

    Thuy Le Thi

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  1. Thuy Le Thi
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  2. Tuoi Phan Thi
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  3. Tho Quan Thanh
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Writing-original draft: TLT; writing-review & editing: TPT, TQT.

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Correspondence to Thuy Le Thi.

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Le Thi, T., Phan Thi, T. & Quan Thanh, T. Machine learning using context vectors for object coreference resolution. Computing 105, 539–558 (2023). https://doi.org/10.1007/s00607-021-00902-4

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