Contextual Embedding Evidence for Main--Light Verb Distinctions in Urdu
Urdu light verbs and their corresponding main verbs show significant representational separation in contextual embeddings, confirming they occupy distinct but related semantic spaces Same-lemma main and light verb centroids are consistently closer than mismatched pairs, supporting the theory that light verbs retain lexical relatedness to their main verb counterparts UrduBERT achieved 0.866 accuracy and 0.852 macro-F1 in a seven-way prediction task on masked light verb uses, demonstrating strong
Analysis
TL;DR
- Urdu light verbs and their corresponding main verbs show significant representational separation in contextual embeddings, confirming they occupy distinct but related semantic spaces
- Same-lemma main and light verb centroids are consistently closer than mismatched pairs, supporting the theory that light verbs retain lexical relatedness to their main verb counterparts
- UrduBERT achieved 0.866 accuracy and 0.852 macro-F1 in a seven-way prediction task on masked light verb uses, demonstrating strong verb identity recoverability
- UrduBERT maintained 0.782 accuracy under preceding-form-disjoint evaluation, indicating generalization beyond memorized local verb combinations
- The study analyzed 1,126 naturally occurring sentences across seven Urdu verbs using UrduBERT, DunbaaBERT, and multilingual BERT embeddings
Why It Matters
This research bridges computational linguistics and theoretical morphology by providing empirical, embedding-based evidence for a well-established linguistic theory about light verb constructions. For AI practitioners working with low-resource or morphologically rich languages like Urdu, it demonstrates that contextual embeddings can capture nuanced grammatical distinctions that are critical for downstream NLP tasks such as machine translation, parsing, and semantic role labeling.
Technical Details
- Models evaluated: UrduBERT, DunbaaBERT, and multilingual BERT, with contextual embeddings extracted across 1,126 naturally occurring Urdu sentences containing seven target verbs
- Representational analysis: Main and light verb uses showed significant separation in all 21 verb-model comparisons, while same-lemma pairs remained closer than mismatched pairs, quantifying the tension between distinction and relatedness
- Prediction task: A seven-way classification task on masked light verb contexts achieved 0.866 accuracy and 0.852 macro-F1 with UrduBERT, confirming that verb identity is recoverable from contextual embeddings
- Generalization test: UrduBERT retained 0.782 accuracy under a preceding-form-disjoint evaluation, showing the model generalizes beyond surface-level co-occurrence patterns rather than relying on memorized verb-collocation pairs
Industry Insight
- For multilingual NLP practitioners, this work validates the use of language-specific BERT variants (like UrduBERT) over multilingual baselines for capturing fine-grained morphological distinctions in low-resource languages
- The methodology of using embedding-space analysis to test linguistic theories can be generalized to other morphologically complex languages, offering a scalable framework for computational typology research
- Developers building Urdu-language NLP pipelines should account for light verb constructions as a distinct grammatical phenomenon rather than treating them as simple verb compounds, as they carry systematic representational differences that affect model behavior
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