Music Domain Named Entity Recognition: Challenges, Solutions, and Future Directions

Named Entity Recognition (NER) is a core NLP task that identifies and classifies entities in text. In the music domain, entities include song titles, artist names, shows, genres, and more, making NER essential for search, recommendation, and content structuring.

The music domain poses specific difficulties: strong domain relevance, ambiguous entity names, limited context in user queries, and highly diverse textual expressions. These challenges require specialized modeling beyond generic NER approaches.

The overall solution consists of offline and online modules. Offline processing builds foundational data (search logs, playback logs, music library), constructs intermediate data such as entity knowledge bases, rule libraries, and candidate sets, and trains models. Online prediction performs candidate generation, applies rule‑based and model‑based recognition, and fuses results to achieve high precision and recall.

Three model versions were iteratively developed. V1 uses handcrafted features and a traditional classifier (XGBoost) for candidate classification, achieving high precision but limited recall. V2 reformulates NER as sequence labeling with BiLSTM‑CRF and domain‑fusion layers, improving recall. V3 adds feature self‑attention and multi‑view attention to better handle ambiguous entities, yielding balanced precision and recall.

Knowledge‑fusion frameworks were explored, including Lattice LSTM, CGN, and FLAT. A customized FLAT variant with enhanced position encoding, pairwise relations, and heterogeneous graph aggregation was adopted to integrate multi‑granularity lexical information effectively.

Training data construction leverages active learning, weak supervision, and data augmentation (entity replacement, non‑entity replacement, entity perturbation). Post‑training strategies such as standard MLM and an entity‑focused MLM that masks entity boundary markers improve model robustness, especially for long‑tail cases.

Future work aims to incorporate entity knowledge‑graph embeddings, jointly optimize candidate generation with final prediction, and extend the model to support nested and discontinuous NER, further enhancing its applicability across music‑related AI services.