Linking EEOC Case Investigation Records 

by Employer Name using Text Analytics

Ada Harris

Government Advances in Statistical Programming (GASP) Presentation

September 23, 2019
Overview

• Background

• Introduction

• Integrated Mission System (IMS)

• Problem

• Approach 

• Data and Methods

• Results

• Summary

• Next Steps
Background

EEOC Investigates complaints of discrimination in the workplace

• Race

• Color

• Religion

• Sex

• National Origin

• Age

• Disability

• Retaliation
Introduction

Why is this important to the EEOC?

•Expand the use of administrative data to 

understand trend across the country

•Efficiently leverage data into insight to 

support the mission of the agency
Integrated Mission System 

(IMS)

• EEOC’s iミterミal iミforマatioミ マaミageマeミt systeマ aミd data repository 

for case investigations – さIharge dataざ

• Database repository for intake, investigation, settlement and closure of 

charges of employment discrimination

• Five main tables

• Charge

• Allegation

• Respondent Name (Employer)

• Action 

• Charging Party Name (Employee)
Problem

There is no key or ID number to link different 

charges against the same employer or corporation. 

Respondent name must be used to link charges.

A Respondent Name must be recorded for a 

forマal Iharge. Variatioミs of the eマployer’s ミaマe, 

business name changes over time, and errors in 

manual entry make it difficult to link cases. 

The purpose is to use text analysis to identify 

unique respondent names and determine whether 

they should link to other respondents.
Challenge Example

さPri┗ate Coマpaミyざ FauIet

さPri┗ate Coマpaミyざ Deミtal 

Insurance

• EEOCRespondent#1 vs. EEOC-Respondent #1

• EEOC Respondent Store #1784

• Business Name d/b/a EEOC Respondent

• Corporate Name vs. Franchise Name

さPri┗ate Coマpaミyざ Tools
Approach

• Develop search query for Respondent Name to model the degree of 

similarity between the Respondent Name and all Respondents within 

IMS.

• How close are two pieces of text in lexical similarity and semantic 

similarity? 

Represent Respondents as vectors of features and compare all 

Respondents to a single respondent of interest by measuring the 

distance between the features.
Data and Methods

• Data from IMS from 2008-2018 from the Respondent Table were used 

to for text analytics (1.4 million observations)

•Data cleansing

• Remove punctuation

• All lowercase

• Trim White Space

• Reマo┗e stop ┘ords ふsuIh as さtheざ, さaざ, さaミざ aミd さiミざぶ

•Deterministic deduplication by unique string of a Respondent 

Name(681,625 observations)
Data and Methods

• N- Grams – sequence of N contiguous items (in this case character)

• Example : EEOC Respondent 3-Gram

• 'EEO', 'EOC', 'OC ', 'C R', ' RE', 'RES', 'ESP', 'SPO', 'PON', 'OND', 'NDE', 'DEN', 'ENT'

Source: http://recognize-speech.com/language-model/n-gram-model/comparison
Data and Methods

Term Frequency – Inverse Document Frequency (TF-IDF) – measures how 

important a word is to document in a corpus

Term Frequency – how frequently a word occurs in a document

TF(t) = (Number of times term t appears in a document) / (Total number of 

terms in the document) 

IDF – measures the importance of the term

IDF(t) = log_e(Total number of documents / Number of documents with term 

t in it) 

Example

Consider a document containing 100 words where the Respondent Name 

appears 5 times. The TF for the Respondent Name is (5/100) = 0.05. Assume 

we have 10 million documents and the Respondent Name appears in 1,000 of 

these. Then, the IDF is log(10,000,000/1000) = 4. The TF_IDF is the product of 

these quantitates 0.05* 4 = 0.20
Data and Methods

• Cosine Similarity – to calculate the similarity between TF-IDF values 

• Measure of orientation and not magnitude

• Normalized dot product
RESULTS EXAMPLE

Query Term Document Term Variations Similarity

さPri┗ate Coマpaミyざ 

Tool

さPri┗ate Coマpaミy # 580ざ Tool Name entry 

variation

0.9948

さPrivte Comanyざ Tool Spelling 

Variation

0.9948

さCoマpaミy Pri┗ateざ Tool Name entry 

variation

0.9937

さPrivate&Companyざ Tool Name entry 

variation

0.9937

さPri┗ate Coマpaミyざ 

Faucet

さThe Pri┗ate Coマpaミyざ FauIet Name entry 

variation

0.9854

さPri┗ate Coマpaミy INCざ FauIet Name entry 

variation

0.9742

さd/H/a Pri┗ate Coマpaミyざ FauIet Name entry 

variation

0.9548

さPri┗ate aミd Coマpaミyざ FauIet Name entry 

variation

0.9398
RESULTS

EEOC Respondent #1

Manual String Search: EEOC Respondent #1 classified 7,783 of 681,625 

observations 

Entity Linkage Approach: EEOC Respondent #1 appears 16,088 of 

681,625 observations

EEOC Respondent #2

Manual String Search: EEOC Respondent #2 classified 4,856 of 681,625 

observations 

Entity Linkage Approach: EEOC Respondent #2 classified 10,859 of 

681,625 observations
Summary

• Multiple entries corresponding to the same entity is a problem often 

occurring in databases and can lead to loss of information. 

•Growing data sources create interesting challenges and opportunities 

for linking data 

• Record linkage helps to find information about Respondents and 

extract actionable data from the IMS database to support leveraging 

internal data to understand Respondent charge frequency
Next Steps

• Decrease time to results

• Parallel processing

• Integrate business name databases as golden standard for correct 

name

•Analyze cut-off scores for classification

•Manually classify output to for future automation