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This paper presents empirical results that contradict the prevailing opinion that entity extraction is a boring solved problem. In particular, we consider data sets that resemble familiar MUC/ACE data, and report surprisingly poor performance for both commercial and research systems. We then give an error analysis that suggests research challenges for(More)
We present a novel application of NLP and text mining to the analysis of financial documents. In particular, we describe an implemented prototype, May-tag, which combines information extraction and subject classification tools in an interactive exploratory framework. We present experimental results on their performance , as tailored to the financial domain(More)
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