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POSIT aims to segment and tag mixed-text that contains English and C-like code, such that the user both knows what a token is, and within the language it's used in, what role, such as an AST tag or PoS tag, it serves.
NLP Named Entity Recognition dalam bidang Biomedis, mendeteksi teks dan membuat klasifikasi apakah teks tersebut mempunyai entitas plant atau disease, memberi label pada teks, menguji hubungan entitas plant dan disease, menilai kecocokan antara kedua entitas, membandingkan hasil uji dengan menggunakan models BILSTM-CRF
Named Entity Recognition system, entirely in PyTorch based on a BiLSTM architecture. Includes an analysis and comparison of different architectures and embedding schemes. Includes support for Character Embeddings, CRF layer (developed from scratch), Layer Normalization, Glove embeddings