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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

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Dimas263/NLP_NER_BILSTM_CRF_Named_Entity_Recognition

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NLP

Named Entity Recognition (NER) - BILSTM - CRF

Slamet Riyanto S.Kom., M.M.S.I.

Dimas Dwi Putra

Architecture

Sentence # Word POS Tag
Sentence: 0 studies NNS O
Sentence: 0 on IN O
Sentence: 0 magnesium NN O
Sentence: 0 s NN O
Sentence: 0 mechanism NN O
Sentence: 0 of IN O
Sentence: 0 action NN O
Sentence: 0 in IN O
Sentence: 0 digitalis NN B-plant
Sentence: 0 induced VBD O
Sentence: 0 arrhythmias NNS B-disease
...
Entities precision recall f1-score support excecution time processor ram model batch size epochs embedding Length Uji
PAD 1,000000 1,000000 1,000000 131 4.49.57 cpu high 2 16 40 128 3000 7
Disease 0,774775 0,623188 0,690763 140
Plant 0,892857 0,892857 0,892857 135
micro avg 0,895288 0,836186 0,864728 406
macro avg 0,889211 0,838682 0,861207 406
weighted avg 0,887332 0,836186 0,858986 406
F-1 Scores 86,5%

Predict

Word           ||True ||Pred
==============================
the            : O     O
mutagen        : O     O
sensitivity    : O     O
assay          : O     O
msa            : O     O
a              : O     O
phenotypic     : O     O
marker         : O     O
of             : O     O
dna            : O     O
damage         : O     O
response       : O     O
and            : O     O
repair         : O     O
capacity       : O     O
has            : O     O
been           : O     O
consistently   : O     O
shown          : O     O
to             : O     O
associate      : O     O
with           : O     O
risk           : O     O
tobacco        : plant plant
related        : O     O
cancers        : disease disease

Save model output as .hdf5

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Websites Prediction

Named Entity Recognition (NER)

Relation Extraction (RE)

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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

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