Identifying Ambiguous Queries in Web Search
, Zhenxiao Luo
, Ji-Rong Wen
, Yong Yu
, and Hsiao-Wuen Hon
Shanghai Jiao Tong University, Shanghai China
Microsoft Research Asia, Beijing China
Fudan University, Shanghai China
It is widely believed that some queries submitted to search
engines are by nature ambiguous (e.g., java, apple). However, few
studies have investigated the questions of “how many queries are
ambiguous?” and “how can we automatically identify an
ambiguous query?” This paper deals with these issues. First, we
construct the taxonomy of query ambiguity, and ask human
annotators to manually classify queries based upon it. From
manually labeled results, we find that query ambiguity is to some
extent predictable. We then use a supervised learning approach to
automatically classify queries as being ambiguous or not.
Experimental results show that we can correctly identify 87% of
labeled queries. Finally, we estimate that about 16% of queries in
a real search log are ambiguous.
Categories and Subject Descriptors
H.3.3 [Information Storage and Retrieval]: Information Search
and Retrieval – query formulation; H.5.2 [Information
Interfaces and Presentation]: User Interfaces – Natural
Experimentation, Languages, Human Factors
Ambiguous query, query classification, broad topics, Web user
Some technologies like personalized Web search and search
results clustering aim to improve users’ satisfaction towards
ambiguous queries from different perspectives. However, there is
no sufficient study on ambiguous queries identification. Questions
like “what percentage of queries are ambiguous?” and “can we
automatically determine whether a query is ambiguous?” are still
open. If we can estimate the percentage of ambiguous queries, we
would know how many queries will be influenced potentially by
the query ambiguity oriented technologies. If we can further
identify ambiguous queries automatically, it is possible to apply
such technologies for a particular kind of queries, instead of for
all. We will try to answer such questions in this paper.
Identifying ambiguous queries is challenging for three reasons.
First, there is no acknowledged definition and taxonomy of query
ambiguity. Many terms related to this concept, such as
“ambiguous query,” “semi-ambiguous query,” “clear query,”
“general term,” “broad topic,” and “diffuse topic.” These terms
are confusing in our investigation. Second, it is uncertain whether
most queries can be associated with a particular type in terms of
ambiguity quality. Cronen-Townsend et al.  proposed to use
the relative entropy between a query and the collection to
quantify query clarity, but the score is not easily aligned to
concepts in human’s mind. Third, even if ambiguous queries can
be recognized manually, it is not realistic to label thousand of
queries sampled from query logs. So how can we identify them in
an automatic way?
In this paper, we first construct taxonomy for query ambiguity
from the literature. We then assess human agreement on query
classification through a user study. Based on the findings, we take
a supervised learning approach to automatically identify
ambiguous queries. Experimental results show that our approach
achieves 85% precision and 81% recall in identifying ambiguous
queries. Finally, we estimate that about 16% of queries in the
sampled search log are ambiguous.
2. TAXONOMY OF QUERIES
By surveying the literature, we summarize the following three
types of queries from being ambiguous to specific.
Type A (Ambiguous Query): a query that has more than
e.g. “giant,” which may refer to “Giant Company Software
Inc.” (an internet security software developer), “Giant” (a film
produced in 1956), “Giant Bike” (a bicycle manufacturer), or
“San Francisco Giants” (National League baseball team).
Type B (Broad Query): a query that covers a variety of
subtopics and a user might look for one of the subtopics by
issuing another query.
e.g. “songs,” which covers some subtopics such as “song
lyrics,” “love songs,” “party songs,” and “download songs.” In
practice, a user often issues such a query first, and then narrows
down to a subtopic.
Type C (Clear Query): a query that has a specific meaning
and covers a narrow topic.
e.g. “University of Chicago” and “Billie Holiday.” A clear
query usually means a successful search in which a user can find
several results with a high degree of quality in the first results
3. USER STUDY
The purpose of user study is to answer whether it is ever possible
to associate a query with a certain type by looking at Web search
results. Since it is difficult to find different meanings of a query
by going through all the results, we use clustered search results
generated by Vivisimo  to facilitate understanding the query.
Copyright is held by the author/owner(s).
WWW 2007, May 8-12, 2007, Banff, Alberta, Canada.
WWW 2007 / Poster Paper Topic: Search
Work & Money
Work & Money
Work & Money
(a) Type A: giant (b) Type B: songs (c) Type C: Billie Holiday
Figure 1. Projection of documents represented in categories for three example queries
Queries used in our user study are sampled from 12-day Live
Search  query logs in August 2006. We use a total of 60
queries and involve five human subjects. Each participant is asked
to judge whether a query is ambiguous (Type A) or not. If the
query is not ambiguous, the participant would answer an
additional question: “Is it necessary to add some words to the
query in order to let it be clearer?” The question aims to clarify
whether the query is broad (Type B) or clear (Type C).
The user study results indicate that participants are in general
agreement, i.e. 90%, in judging whether a query is ambiguous or
not. However, it is difficult to distinguish Type B from Type C as
the agreement is only 50%.
4. LEARNING A QUERY AMBIGUITY
In this paper, we utilize a query q and a set of top n search
with respect to the query in modeling query ambiguity.
We formulate the problem of identifying ambiguous queries as a
(, ) ( | )
qD A Aa
Based on the findings in the user study, we aim to classify a query
(ambiguous queries) or
(broad or clear queries). Support
Vector Machines (SVM) developed by Vapnik  with RBF
kernel is used as our classifier.
A text classifier similar to that used in  is applied to classify
each Web document in
D into predefined categories in
KDDCUP 2005. We represent a document by a vector of
categories, in which each dimension corresponds to the
confidence that the document belongs to a category.
Our main idea of identifying an ambiguous query is that relevant
documents with different interpretations probably belong to
several different categories. To illustrate this assumption, we
project documents into a three-dimensional (3D) space and show
three example queries in Figure 1. The coordinates correspond to
three categories that a query most likely belongs to. “
an ambiguous query, may refer to “
giant squid” in Library
Giant Company Inc.” in Computing category, and
Gaint Food supermarket” in Work&Money category. Figure 1(a)
shows scattered distribution among these three categories. “
” is a clear query and Figure 1(c) shows almost all the
documents are gathered in the category of Entertainment. “
is a broad query. A pattern of documents between being scattered
and gathered is observed in Figure 1(b).
12 features are derived to quantify the distribution of
D , such as
the maximum Euclidean distance between a document vector and
the centroid document vector in
We conduct the experiments of learning a query ambiguity model
on 253 labeled queries. Five-fold cross validation is performed.
The best classifier in our experiments achieves precision of 85.4%,
recall of 80.9%, and accuracy of 87.4%. Such performance
verifies that ambiguous queries can be identified automatically.
We try to estimate what percentage of queries is ambiguous in a
query set sampled from Live Search logs. The set consists of 989
queries. To achieve the goal, our newly learned query ambiguity
model is used to do prediction on the query set. When we increase
the size of query set for estimation from 1/10 to 10/10, the
percentage first vibrates between 15% and 18% and finally
stabilizes at around 16%. Therefore, we estimate that about 16%
of all the queries are ambiguous.
In this paper, we find people are in general agreement on whether
a query is ambiguous or not. Thus we propose a machine learning
model based on search results to identify ambiguous queries. The
best classifier achieves high accuracy as 87%. By applying the
classifier, we estimate that about 16% queries are ambiguous in
the sampled logs.
 S. Cronen-Townsend, Y. Zhou, and W. B. Croft. Predicting
query performance. In Proceedings of the 25
Conference on Research in Information Retrieval (SIGIR),
pages 299-306, 2002
 D. Shen, R. Pan, J.-T. Sun, J. J. Pan, K. Wu, J. Yin, and Q.
Yang. Q2c@ust: our winning solution to query classification
in KDDCUP 2005. SIGKDD Explorations, 7(2):100–110,
 V. Vapnik. Principles of risk minimization for learning
theory. In D. S. Lippman, J. E. Moody, and D. S. Touretzky,
editors, Advances in neural information processing systems 3,
pages 831-838. Morgan Kaufmann, 1992
 Live Search. http://www.live.com/
 Vivisimo search engine. http://www.vivisimo.com
WWW 2007 / Poster Paper Topic: Search