Exploiting Multi-Document Term Extraction to Improve Named Entity Recognition for Major Concept Detection

With worldwide events happening everyday, it's useful to discover the most discussed topics for intelligence and trend analysis. However, existing news media only provide manually annotated tags related to each article. Given huge amount of Web data with new or unknown terms, it's critical if these topic-related keywords can be automatically extracted in an efficient way. In this paper, we propose a multi-document term extraction approach to major concept detection. First, major keywords occurring in multiple documents are extracted by statistical term extraction methods. These are then utilized as the dictionary for word segmentation to improve named entity recognition, by which the most relevant concepts in each single document can be extracted. Finally, the extracted concepts are ranked by their importance score based on their popularity and average position in documents. In the experimental results for major online news sources in Chinese, exploiting multi-document term extraction helps to improve the effectiveness of named entity recognition, with the best F1-measure of 0.61, a significant 24% improvement over the baseline. By combining the popularity with average position of terms as the importance score, we able to obtain the best precision of 68% when keeping the top-25% content in documents. Keyword extraction results for selected major events further validated the effectiveness of our proposed approach using topic-related news articles. Further investigation is needed to evaluate the performance of the proposed approach with various data sources.