Programme booklet (pdf)

Abstract
34
Nauze, Fabrice
Q-go
Clustering customer questions
CLIN 21 – CONFERENCE PROGRAMME
Q-go’s natural language search technology powers the search box of many corporate
websites. Its NLP technology allows customers to ask questions in their own words and
returns a small set of relevant answers. Hundreds of millions of questions have already
been processed and answered with Q-go’s solution providing us with a mine of data.
In order to improve our knowledge of what customers are asking and to help further
refine our core systems, Q-go needs a way to automatically cluster relevant queries
from large sets of customer questions.
To achieve this goal we tested several standard clustering methods on sets of customer
questions. The outline of the talk will be the following.
First we will explain the specific challenges one has to face when clustering customer
questions (very short queries, typos, etc…). We will then present the clustering
algorithms that have been tested (among other k-Means, GAAC hierarchical clustering,
mini-batch k-Means). Thirdly we will outline two different types of heuristics used in
the first case to improve the quality of the vector representations feeding the
clustering algorithms and in the second to overcome the curse of dimensionality.
Finally the different methods will be evaluated and compared with respect to
processing speed and intrinsic quality of clustering (as well as its practical usefulness).
Corresponding author: fabrice.nauze@q-go.com