1. A Seedlink Tech White Paper
in Collaboration with International Top Talent
February 2014
www.seedlinktech.com
www.RCXUE.com
Using Natural Language Processing to Improve Speed
and Quality of Employee Recruiting
QuestMatch:

2. Table of Contents
Introduction ..................................................................................................
Key China Data .............................................................................................
Key Research Findings....................................................................................
Discussion ....................................................................................................
Questions Are Key to Job Analysis .........................................................
Less Screening, More Interacting ...........................................................
................................................
Use Technology to do More with Less .....................................................
QuestMatch .................................................................................................
QuestMatch Data ..........................................................................................
...................
Conclusions ..................................................................................................
References ...................................................................................................
3
4
5
6
6
7
7
7
8
9
10
11
12

3. Introduction
but it is especially labor-intensive in emerging markets. With a
workforce of nearly 800 million, screening résumés in China is
extremely tedious and low-value. McKinsey reports that only 10%
(MNC). Seedlink case studies indicate
that sourcing just one suitable Chinese
candidate requires reviewing between
50 – 100 résumés or CVs. The
challenging recruitment environment
in China can offset the vast market
opportunities.
This paper covers three main topics. First, we will outline key
recruiting literature. Then, we suggest a four-step process for
Finally, we introduce the concept of Natural Language Processing
(NLP) and QuestMatch to improve speed, while managing the
entire pre-interview process.
With a combination of continued
process improvement and cutting-
edge technology, companies can
vastly improve the quality of recruits
while reducing overall cost. The
improvements can impact company
Second, better matches reduce overall turnover rate and in turn,
off-boarding and replacement costs. RCXUE with QuestMatch
provides an intuitive interface to bring NLP to the most demanding
companies’ recruitment practices.
McKinsey reports that only 10%
at a multinational corporation
Better matches reduce overall
turnover rate and thus off-
boarding and replacement costs
3
QuestMatch: Using Natural Language Processing to Improve Speed and Quality of Employee Recruiting

4. Key China Data
Figure 1:
790 million people in active labor force 1
7 million college graduates in 2013 2
Nearly 200 million total college graduates by 20203
4
20% of CVs and résumés 5
6
80% of turnover is due to bad hiring decisions 7
1
2
“College Graduation Data”, China Ministry of Education, 2013.
3
ibid
4
Diana Farrell and Andrew Grant, “Addressing China’s Looming Talent Shortage,” McKinsey Global Insights Research,
5
-
6
“Retention: Is It Getting Enough Attention,” Hays Research, 2012.
7
4
QuestMatch: Using Natural Language Processing to Improve Speed and Quality of Employee Recruiting

5. 8
Fritzsche and Brannick, “The Importance of Representative Design in Judgment Tasks: The Case of Resume Screening,”
Journal of Occupational and Organizational Psychology 75, no.2 (June 2002): 163.
9
Cole et al., “Recruiters’ Perceptions and Use of Applicant Resume Information: Screening the Recent Graduate,” Applied
Psychology: An International Review 56, no.2 (April 2007): 319 – 343.
10
James M. Tyler and Jennifer Dane McCullough, “Violating Prescriptive Stereotypes on Job Resumes: A Self-
Presentational Perspective,” Management Communication Quarterly 23, no. 2 (November 2009): 272-287.
11
Journal of Social and
Personal Relationships 14, no.3 (June 1997): 417-431.
12
Journal of
Social Psychology 139, no. 6 (December 1999): 700-712.
13
Richard D. Arvey et al., “Interview Validity for Selecting Sales Clerks,” Personnel Psychology 40 (March 1987): 1-12.
14
Validity Study,” Journal of Applied Psychology 70, no.4 (November 1985): 774-776.
15
Marketing Management Journal 18, no. 2 (Fall 2008): 93-105.
Key Research Findings
The Bad News
The Good News
résumé screening criteria among
professional recruiters 8
Experienced recruiters’ inference of hard skills and personality
résumés is not statistically valid 9
Recruiters are subject to identity-image biases and recommend
than company criteria 10, 11
Candidates subjected to more thorough and longer screening
hold more favorable attitudes toward job openings 12
Interviews based on job analysis are statistically valid predictors
of performance 13, 14
Non-traditional interview techniques offer similar results to
traditional (face-to-face) interviews 15
QuestMatch: Using Natural Language Processing to Improve Speed and Quality of Employee Recruiting
5

6. Discussion
Research suggests that CV and résumé screening for prospective
employees is inaccurate and subject to bias. Applicants commonly
augment the truth; while up to 20% of résumés have gross
discrepancies.16
Stretched truths can become outright lies. In
due to CV inaccuracies. The problem is pervasive; particularly in
background checks.
Furthermore, people conducting
screenings are subject to biases based
on personal opinions. Studies have
shown that mood biases, gender biases,
and self-identity biases all contribute
17, 18
In fact,
experienced recruiters even disagree
about what criteria are essential to job
performance.19
how does a large company know that it is getting the best workers
or just more of the same unreliable workers?
This white paper examines a four-part model for how candidate
screening can be vastly improved.
1. Questions are key to job analysis
Focusing job analysis on key issues employees face,
rather than a checklist of requirements, provides
through creating simple open-ended questions to
describe job functions. Instead of creating survey-
based assessments and wordy job descriptions, hiring
managers and recruiters focus on understanding
that can be analyzed and assessed.
How does a large company
know that it is getting the best
workers or just more of the same
unreliable workers?
16
Kroll Asia Study, 2008.
17
Tyler and McCullough, 2009.
18
Byrne, 1997.
19
Fritzsche and Brannick, 2002.
QuestMatch: Using Natural Language Processing to Improve Speed and Quality of Employee Recruiting
6

7. 2. Spend less time screening and more time
interacting with candidates
Searching and screening candidates is slow and
passive. Instead, assess candidates from the outset
with questions created from job analysis. Sort
candidates into groups and actively engage groups
into a dialogue. With QuestMatch, recruiters can pose
questions to candidates digitally—before committing
time and effort to phone or face-to-face interviews.
CVs and résumés are static. People with ideas, skills
and thoughts are reduced to a paper representation.
out amongst large numbers of applicants.20
Dynamic
of recruits, while uncovering high-potential workers
whose credentials underrepresent their abilities.
RCXUE with QuestMatch automates this entire
process.
4. Use technology to do more with less
Interviews conducted via non-traditional methods
deliver results similar to face-to-face interviews. Web
applications, phone, email, and text chat are all viable
alternatives to assess potential employees. The next
era of recruitment technology will increase pre-
assessment interaction with candidates and automate
ranking.
20
International Top Talent (ITT) Research, Fall 2012.
7
QuestMatch: Using Natural Language Processing to Improve Speed and Quality of Employee Recruiting

8. QuestMatch
QuestMatch is a dynamic assessment software that allows
recruiters to interview groups of candidates digitally. Assessment
is based on open-ended questions rather than survey-based
assessment. This format allows greater variance of answers and
deeper granularity of results. Using cutting-edge Natural Language
Processing (NLP), the answers are aggregated for comparative
analysis, and candidates are automatically ranked.
Figure 2:
8
QuestMatch: Using Natural Language Processing to Improve Speed and Quality of Employee Recruiting

9. Figure 3:
QuestMatch Data
Variance in ranking by QuestMatch is statistically similar to the
variance recorded between individual recruiters.
Case studies reported greater than 70% correlation between
recruiters’ rankings and QuestMatch rankings.
Results were even more accurate for best and worst answers
(up to 90% accuracy).
compared to traditional HR practices.
9
QuestMatch: Using Natural Language Processing to Improve Speed and Quality of Employee Recruiting

10. Natural Language Processing (NLP) to Increase Speed
meaning from human language input. Progress over the last
decade allows modern algorithms to assess the sentiment
and feelings of people. Going forward there is opportunity
to utilize NLP to automate even more
sponsored by Harvard and MIT, is using
computer programs to grade student
papers. The variance of computer-
generated results is nearly identical to
variance in human readers.21
QuestMatch allows recruiters to reduce time spent on
creating and reviewing assessments through automation
and answer abstraction. The result is more engagement
strategy, interaction and coordination rather than searching,
sorting and screening.
As non-traditional interview results
are similar to face-to-face interviews,22
in-depth questions can be posed to
prospective candidates as a screening
procedure. QuestMatch then automates
the entire assessment process, saving
organizations time and money.
The variance of computer-
generated results is nearly
identical to variance in human
readers
Non-traditional interview results
are similar to face-to-face
interviews
21
The New York Times, April 4, 2013.
22
10
QuestMatch: Using Natural Language Processing to Improve Speed and Quality of Employee Recruiting

11. 23
Cole et al., April 2007.
24
Fritzsche and Brannick, June 2002.
Conclusion
Résumé screening is an outdated recruitment paradigm. Prospective
candidates are reduced to words on paper, while recruiters’
inferences of skills from résumés are not statistically valid.23
Experienced recruiters even disagree on which criteria to judge
resumes.24
Furthermore, screening is tedious. The process is both
slow and inaccurate.
and results of candidate screening through process improvement
QuestMatch as the new paradigm in candidate screening.
Recruiters can now digitally interview groups of candidates and
automatically assess responses with NLP (Natural Language
Processing) and machine learning. The result is savings in time,
money, and effort.
11
QuestMatch: Using Natural Language Processing to Improve Speed and Quality of Employee Recruiting

12. References
Anat, Rafaeli, “Pre-Employment Screening and Applicants’ Attitudes Toward an Employment
Journal of Social Psychology 139, no. 6 (December 1999): 700-712.
Richard D. Arvey et al., “Interview Validity for Selecting Sales Clerks,” Personnel Psychology 40
(March 1987): 1-12.
Paradigm,” Journal of Social and Personal Relationships 14, no.3 (June 1997): 417-431.
Cole et al., “Recruiters’ Perceptions and Use of Applicant Resume Information: Screening the
Recent Graduate,” Applied Psychology: An International Review 56, no.2 (April 2007): 319 –
343.
“College Graduation Data”, China Ministry of Education, 2013.
Diana Farrell and Andrew Grant, “Addressing China’s Looming Talent Shortage,” McKinsey Global
Fritzsche and Brannick, “The Importance of Representative Design in Judgment Tasks: The Case
of Resume Screening,” Journal of Occupational and Organizational Psychology 75, no.2 (June
2002): 163.
International Top Talent (ITT) Research, Fall 2012.
The New York Times, April 4,
2013.
Interview Technique,” Marketing Management Journal 18, no. 2 (Fall 2008): 93-105.
A Comparative Validity Study,” Journal of Applied Psychology 70, no.4 (November 1985):
774-776.
“Retention: Is It Getting Enough Attention,” Hays Research, 2012.
James M. Tyler and Jennifer Dane McCullough, “Violating Prescriptive Stereotypes on Job
Resumes: A Self-Presentational Perspective,” Management Communication Quarterly 23,
no. 2 (November 2009): 272-287.
12
QuestMatch: Using Natural Language Processing to Improve Speed and Quality of Employee Recruiting

13. rights reserved. This document is provided for information purposes only and
the contents hereof are subject to change without notice. This document is not
warranted to be error-free, nor subject to any other warranties or conditions,
whether expressed orally or implied in law, including implied warranties and
disclaim any liability with respect to this document and no contractual obligations
are formed either directly or indirectly by this document. This document may
not be reproduced or transmitted in any form or by any means, electronic or
mechanical, for any purpose, without our prior written permission.
Seedlink, QuestMatch, and RCXUE are trademarks of Seedlink Technology
respective owners.
QuestMatch: Using Natural
Language Processing to Improve
Speed and Quality of Employee
Recruiting
Februrary 2014
Seedlink Technology Holdings, Ltd.
700 Changping Road
Shanghai, China
200060
www.seedlinktech.com
www.RCXUE.com