2007 LIPTUS

• (Bhide et al., 2007) ⇒ Manish A. Bhide, Ajay Gupta, Rahul Gupta, Prasan Roy, Mukesh K. Mohania, Zenita Ichhaporia. (2007). “LIPTUS: associating structured and unstructured information in a banking environment.” In: Proceedings of the 2007 ACM SIGMOD Conference (SIGMOD 2007).

Subject Headings: Document Entity Linking

Notes

• Describes heuristics used to join emails to the the corresponding customer.

Cited By

~7 http://scholar.google.com/scholar?cites=18425155918898670485

Quotes

Abstract

• Growing competition has made today's banks understand the value of knowing their customers better. In this paper, we describe a tool, LIPTUS, that associates the customer interactions (emails and transcribed phone calls) with customer and account profiles stored in an existing data warehouse. The associations discovered by LIPTUS enable analytics spanning the customer and account profiles on one hand and the meta-data associated or derived from the interaction (using text mining techniques) on the other. We illustrate the value derived from this consolidated analysis through specific customer intelligence applications. LIPTUS is today being extensively used in a large bank in India. A highlight of this paper is a discussion of the technical challenges encountered while building LIPTUS and deploying it on real-life customer data.

1. Introduction

• In this paper, we describe a tool, LIPTUS (LInking and Processing Tool for Unstructured and Structured information) that addresses these issues. LIPTUS automatically associates the customer interactions (emails and transcribed phone calls) with customer and account profiles stored in an existing database.

3.1 Cleaning the Customer Interaction Text

• The customer interactions contain a significant amount of irrelevant and redundant text (including irrelevant advertisements, disclaimers, canned greetings, text of earlier messages repeated as history, etc.). This useless additional text makes analysis of the interaction content not only slower, but also less effective since it tends to obscure the actual information contained in the interaction. In this section, we describe the cleaning steps that try to identify and remove the irrelevant and redundant text present in the transactions.

3.2 The Linking Procedure

• This task is far more difficult than merely looking for numeric sequences in the text and then disambiguating these sequences based on the number of digits, prefix sequences and other patterns. This is because of a variety of reasons, some of which are listed below:
• LIPTUS uses annotators based on the Unstructured Information Management Architecture (UIMA) [6] to identify the customer and ccount ids. At its simplest, an annotator tokenizes the text and applies pattern-based rules on the token sequence obtained to dentify the interesting tokens (customer and account ids in our case). These rules combine the hints mentioned above (size of the numeric sequence, identifying prefixes) and take the presence of hyphens and whitespaces into account as well. Moreover, they also take hints from the surrounding text to identify the type of the id identified (for instance, a credit card number could be urrounded by the words such as “visa”, “mastercard”, and “expiry”). The annotator also takes hints from the category the interaction is ssociated with (“credit card inquiry”, “cheque status inquiry”, “premium payment”) to identify a small set of alternatives; a cheque status inquiry, for instance, can only relate to a savings or current account.
• We again emphasize that this extraction process is essentially a best-effort solution, and there is a possibility of an incorrect sequence being extracted as a customer or account id, as well as of a valid customer or account id not being extracted. On the interactions we considered, however, we found that these simple heuristics performed well enough.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2007 LIPTUS	Prasan Roy Manish A. Bhide Ajay Gupta Rahul Gupta Mukesh K. Mohania Zenita Ichhaporia			LIPTUS: associating structured and unstructured information in a banking environment			http://dx.doi.org/10.1145/1247480.1247587	10.1145/1247480.1247587