Cognition-aware Cognate Detection
- URL: http://arxiv.org/abs/2112.08087v1
- Date: Wed, 15 Dec 2021 12:48:04 GMT
- Title: Cognition-aware Cognate Detection
- Authors: Diptesh Kanojia, Prashant Sharma, Sayali Ghodekar, Pushpak
Bhattacharyya, Gholamreza Haffari, Malhar Kulkarni
- Abstract summary: We propose a novel method for enriching the feature sets, with cognitive features extracted from human readers' gaze behaviour.
We collect gaze behaviour data for a small sample of cognates and show that extracted cognitive features help the task of cognate detection.
We use the collected gaze behaviour data to predict cognitive features for a larger sample and show that predicted cognitive features, also, significantly improve the task performance.
- Score: 46.69412510723641
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Automatic detection of cognates helps downstream NLP tasks of Machine
Translation, Cross-lingual Information Retrieval, Computational Phylogenetics
and Cross-lingual Named Entity Recognition. Previous approaches for the task of
cognate detection use orthographic, phonetic and semantic similarity based
features sets. In this paper, we propose a novel method for enriching the
feature sets, with cognitive features extracted from human readers' gaze
behaviour. We collect gaze behaviour data for a small sample of cognates and
show that extracted cognitive features help the task of cognate detection.
However, gaze data collection and annotation is a costly task. We use the
collected gaze behaviour data to predict cognitive features for a larger sample
and show that predicted cognitive features, also, significantly improve the
task performance. We report improvements of 10% with the collected gaze
features, and 12% using the predicted gaze features, over the previously
proposed approaches. Furthermore, we release the collected gaze behaviour data
along with our code and cross-lingual models.
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