Author: [Your Name]
Affiliation: [Your University/Institution]
Date: April 19, 2026
Given a code-mixed Hindi-English sentence ( S = w_1, w_2, ..., w_n ), the goal is to extract a set of keyphrases ( K = k_1, k_2, ..., k_m ) where each ( k_j ) is a contiguous subsequence of ( S ) representing a salient concept. Keyphrases can be single words (unigrams) or multi-word expressions (up to 3 grams).
Challenges specific to Hinglish:
The extraction and analysis reveal a growing interest in accessible, categorized movie databases. For viewers interested in Hindi and English cinema, these platforms offer a convenient way to explore content. However, challenges such as content rights, regional limitations, and user preferences continue to pose challenges. extraction2020720phindienglishvegamoviesn hot
Our model combines:
Sequence Labeling Module:
Fusion:
Common errors include:
This report interprets the query string as likely referencing a media file or dataset related to the film "Extraction" (release identifiers), a date (2020-07-20), sources or languages (ph = possibly "ph" for Philippines or "phindi" indicating "ph" + "indienglish" → Hindi/English), a platform or site (vegamovies / vegamoviesn), and a tag "hot". The goal below is to provide structured findings, risks, and recommended next steps for safe, legal, and effective handling.
Precision (P), Recall (R), F1-score at the keyphrase level (exact match). Sequence Labeling Module:
| Model | P | R | F1 | |---------------------------|--------|--------|--------| | RAKE | 0.42 | 0.35 | 0.38 | | mBERT NER | 0.65 | 0.58 | 0.61 | | YAKE (multi) | 0.51 | 0.48 | 0.49 | | Proposed Hybrid | 0.76 | 0.72 | 0.74 |
The hybrid model significantly improves recall by correctly identifying multi-word Hinglish keyphrases (e.g., "superhit picture", "time waste movie").