The Research Of Chinese Named Entity Recognition And Its Relation Extraction

The paper mainly researches the Chinese named entity recognition and its relation extraction, designs and realizes a Chinese named entity extraction (CNEE) system, which can recognize and extract person, location and organization name, and extracts person name and E-mail from personal homepage by the SRV algorithm. CNEE first automatically segments words and tags parts of speech, then recognizes and extracts person, location and organization name according to their different traits. The paper improves the POS tagging by introducing the negative feedback rules into HMM tagging. The final precision of POS tagging is about 96 percents, while the final experiment shows that the F-measures of the person, location and organization name extraction of CNEE are all over 75 percents. SRV is an algorithm based on rule learning, which needs only a few train samples and has high precision. The paper applies it into extracting person name and Email from personal homepage and obtains good result. The experiment demonstrates that SRV is successful and effective for the named entity relation extraction.