Learning diachronic analogies to analyze concept change

Orlikowski M, Hartung M, Cimiano P (In Press)
In: Proceedings of the 2nd Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2018). Association for Computational Linguistics.

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Conference Paper | In Press | English
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Abstract / Notes
We propose to study the evolution of concepts by learning to complete diachronic analogies between lists of terms which relate to the same concept at different points in time. We present a number of models based on operations on word embedddings that correspond to different assumptions about the characteristics of diachronic analogies and change in concept vocabularies. These are tested in a quantitative evaluation for nine different concepts on a corpus of Dutch newspapers from the 1950s and 1980s. We show that a model which treats the concept terms as analogous and learns weights to compensate for diachronic changes (weighted linear combination) is able to more accurately predict the missing term than a learned transformation and two baselines for most of the evaluated concepts. We also find that all models tend to be coherent in relation to the represented concept, but less discriminative in regard to other concepts. Additionally, we evaluate the effect of aligning the time-specific embedding spaces using orthogonal Procrustes, finding varying effects on performance, depending on the model, concept and evaluation metric. For the weighted linear combination, however, results improve with alignment in a majority of cases. All related code is released publicly.
Publishing Year
Conference
The 2nd Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2018)
Location
Santa Fe, New Mexico
Conference Date
2018-08-25
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Orlikowski M, Hartung M, Cimiano P. Learning diachronic analogies to analyze concept change. In: Proceedings of the 2nd Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2018). Association for Computational Linguistics; In Press.
Orlikowski, M., Hartung, M., & Cimiano, P. (In Press). Learning diachronic analogies to analyze concept change. Proceedings of the 2nd Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2018) Association for Computational Linguistics.
Orlikowski, M., Hartung, M., and Cimiano, P. (In Press). “Learning diachronic analogies to analyze concept change” in Proceedings of the 2nd Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2018) (Association for Computational Linguistics).
Orlikowski, M., Hartung, M., & Cimiano, P., In Press. Learning diachronic analogies to analyze concept change. In Proceedings of the 2nd Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2018). Association for Computational Linguistics.
M. Orlikowski, M. Hartung, and P. Cimiano, “Learning diachronic analogies to analyze concept change”, Proceedings of the 2nd Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2018), Association for Computational Linguistics, In Press.
Orlikowski, M., Hartung, M., Cimiano, P.: Learning diachronic analogies to analyze concept change. Proceedings of the 2nd Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2018). Association for Computational Linguistics (In Press).
Orlikowski, Matthias, Hartung, Matthias, and Cimiano, Philipp. “Learning diachronic analogies to analyze concept change”. Proceedings of the 2nd Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2018). Association for Computational Linguistics, In Press.
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