The power of context: Random Forest classification of near synonyms. A case study in Modern Hindi
| Source: ArXiv | Original article
A new arXiv pre‑print, arXiv:2604.01425v1, demonstrates that a classic machine‑learning technique can tease apart “near synonyms” in Modern Hindi by exploiting contextual cues. The authors train a Random Forest classifier on a curated corpus of Hindi sentences, feeding it features such as part‑of‑speech tags, dependency relations, collocation frequencies and contextual word‑embedding vectors. The model achieves over 85 % accuracy in distinguishing pairs that traditional lexical resources treat as interchangeable, confirming that even subtle shifts in usage create measurable patterns.
The study matters for several reasons. First, it challenges the long‑standing linguistic claim that absolute synonyms do not exist, showing that computational methods can quantify the degree of overlap between words. Second, it provides a low‑resource, interpretable alternative to deep‑neural approaches that often require massive datasets and opaque decision‑making. Random Forests also yield feature‑importance scores, giving lexicographers insight into which contextual signals matter most. Third, the findings have immediate downstream impact: more precise synonym handling can improve Hindi machine‑translation quality, enhance search relevance, and support language‑learning apps that need to teach nuanced vocabulary differences.
Looking ahead, the research opens a clear path for broader multilingual validation. If similar context‑driven classifiers succeed in other Indo‑Aryan languages, they could become a staple of regional NLP toolkits. The authors plan to release their annotated dataset and code, inviting the community to benchmark against transformer‑based models. Watch for follow‑up work that integrates these classifiers into large‑scale language models, potentially refining token‑level predictions in multilingual LLMs and sharpening the next generation of AI‑assisted writing assistants for Hindi speakers.
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