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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.
Experiment with three different models: conditional random field (CRF), bidirectional long short-term memory (BiLSTM), and a combination of the two, and their performances on two named entity recognition (NER) datasets.
A generic and semantic profiling of 1159 New York City open datasets using Apache Spark. The generic profiling has been performed using the Spark RDDs and Dataframes. Semantic profiling has been performed using Named Entity Recognition, Soundex, Regex, Ontologies and Clustering.
Save .pb model for C++. It's changed Determined22/zh-NER-TF : A very simple BiLSTM-CRF model for Chinese Named Entity Recognition 中文命名实体识别 (TensorFlow)