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There is a wealth of information about financial systems that is embedded indocument collections. In this paper, we focus on a specialized text extractiontask for this domain. The objective is to extract mentions of names offinancial institutions, or FI names, from financial prospectus documents, andto identify the corresponding real world entities, e.g., by matching against acorpus of such entities. The tasks are Named Entity Recognition (NER) andEntity Resolution (ER); both are well studied in the literature. Ourcontribution is to develop a rule-based approach that will exploit lists of FInames for both tasks; our solution is labeled Dict-based NER and Rank-based ER.Since the FI names are typically represented by a root, and a suffix thatmodifies the root, we use these lists of FI names to create specialized rootand suffix dictionaries. To evaluate the effectiveness of our specializedsolution for extracting FI names, we compare Dict-based NER with a generalpurpose rule-based NER solution, ORG NER. Our evaluation highlights thebenefits and limitations of specialized versus general purpose approaches, andpresents additional suggestions for tuning and customization for FI nameextraction. To our knowledge, our proposed solutions, Dict-based NER andRank-based ER, and the root and suffix dictionaries, are the first attempt toexploit specialized knowledge, i.e., lists of FI names, for rule-based NER andER.

Exploiting Lists of Names for Named Entity Identification of Financial Institutions from Unstructured Documents