You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Pytesseract OCR model to identify texts. Incorporated a pre-trained Named Entity Recognition (NER) model to extract entities from the identified texts, interpreted the information by text mining and web searches to collect auxiliary information.
Constructing a corpus of ancient Chinese pediatric medicine literature, using algorithms such as BERT, lattice LSTM, and Siamese for tasks such as named entity recognition, intent recognition, entity similarity calculation, and entity linking, to develop a TCM-QA.
NLP to understand the sentiment in the latest news articles featuring Bitcoin and Ethereum, and to better understand the other factors involved with the coin prices change such as common words and phrases and organizations and entities mentioned in the articles.
Named Entity Recognition project with 70.9% F1-score on the SemEval 2022 MultiCoNER English dataset. Developed in Pytorch using BiLSTM, CRF, word embeddings and PoS embeddings.
A multi-lingual named entity classifier to perform named entity recognition (NER) on two datasets, International: CoNLL 2003, Chinese: Weibo. We used the current state-of-the-art model to test on CoNLL++ dataset, achieved a F1-score of 94.3% with pooled-embeddings.