The exponential growth of user-generated content on streaming platforms and social media has led to a surge in code-mixed text, particularly Hindi-English (Hinglish). Extracting meaningful keyphrases from such unstructured data remains challenging due to lexical variations, lack of standardized grammar, and resource scarcity. This paper proposes a hybrid keyphrase extraction model combining statistical features (TF-IDF, TextRank) with a lightweight neural sequence labeler. Evaluated on a manually annotated corpus of 5,000 movie review sentences from online forums, the proposed model achieves an F1-score of 0.74, outperforming baseline methods by 12%. The approach demonstrates robust performance on named entities, movie titles, and sentiment-bearing phrases.
[1] S. Kumar et al. "Keyphrase Extraction in Hinglish: A Benchmark Dataset." ICON , 2022. [2] Mihalcea & Tarau. "TextRank: Bringing Order into Texts." EMNLP , 2004. [3] Devlin et al. "BERT: Pre-training of Deep Bidirectional Transformers." NAACL , 2019. extraction2020720phindienglishvegamoviesn hot
: Tyler Rake (Chris Hemsworth), a black-market mercenary, is hired to rescue Ovi Mahajan, the kidnapped son of an imprisoned Indian drug lord, from Dhaka, Bangladesh. Evaluated on a manually annotated corpus of 5,000