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Semantic graph for word disambiguation in machine translation

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Abstract

This work is an attempt to incorporate semantic context in machine translation using a combination of parallel corpora as well as feedback from human translators. Both parallel corpora and human translators help in determining what constitute keywords/key phrases that help in the disambiguation of words or phrases that lend themselves to multiple possible meanings. The disambiguation process uses a probabilistic language model that captures the dependencies of ambiguous words/phrases on those keywords/phrases through parametric conditional probabilities, with their parameters estimated using parallel corpora data. These are augmented via human translator feedbacks using an interface that maps the degree of confidence (a measure between 0 and 1, with 1 being 100% certainty about the word disambiguation) of the human translator in the disambiguation of a word/phrase into updated language model parameters. The disambiguation is made in accordance with the most probable meaning based on the keywords/phrases. This work also presents an iterative relaxation algorithm to disambiguate multiple words in one sentence by obtaining the translation with the highest joint probability. Experimental results using our model and method are reported on testbeds in the medical and literary fiction domains and our results fare more than favorably when compared to the state-of-the-art Neural Network (NN) based Word Sense Disambiguation approach. Our method goes beyond NN learning by extracting and modeling the essential semantic elements in the original language to faithfully capture the meaning of the source text.

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  1. Electrical and Computer Engineering, Drexel University, Philadelphia, PA, 19104, USA

    Fernand S. Cohen, Zheng Zhong & Chenxi Li

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  1. Fernand S. Cohen
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  2. Zheng Zhong
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  3. Chenxi Li
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Correspondence to Fernand S. Cohen.

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Cohen, F.S., Zhong, Z. & Li, C. Semantic graph for word disambiguation in machine translation. Multimed Tools Appl 81, 43485–43502 (2022). https://doi.org/10.1007/s11042-022-13242-y

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