CONTEXT-AWARE QUERY CLASSIFICATION
Query classification techniques attempt to classify user search queries in order to better understand user search intent. Understanding a user's search intent allows search engines to provide relevant content tailored to the user's interest. Unfortunately, current classification techniques do not take into account contextual information. Accordingly, as provided herein, a target query may be classified based upon contextual information. In particular, features may be extracted from contextual information and/or other sources. For example, features may be extracted from the target query, related queries, and/or invoked search results of the related queries. In this way, the target query may be classified based upon other queries performed by the user and/or search results of the queries the user found interesting. In addition, a CRF model may be utilized in classifying the target query by providing generalized parameters learned from labeled query sessions.
1 . A method for classifying a target query based upon contextual information, comprising:
retrieving contextual information comprising previous queries and corresponding invoked search results for respective previous queries;
extracting features from the contextual information; and
classifying a target query based upon the extracted features.
2 . The method of claim 1 , the retrieving comprising:
dividing the contextual information into one or more sessions, a session comprising a sequence of queries and corresponding invoked search results for one or more respective queries, such that time intervals between sequential queries is less than a predetermined time interval; and
retrieving a session as the contextual information, the session comprising the target query.
3 . The method of claim 2 , the classifying comprising:
building a CRF model comprising:
extracting features from the one or more sessions; and
classifying queries within the one or more sessions based upon sequential query classifications; and
classifying the target query based upon generalized parameters derived from classified queries within the CRF model.
4 . The method of claim 1 , the classifying comprising:
classifying the target query based upon a taxonomy comprising a hierarchy of categories.
5 . The method of claim 1 , the extracting comprising:
extracting query terms as features.
6 . The method of claim 1 , the extracting comprising:
extracting pseudo feedback as features.
7 . The method of claim 6 , the extracting pseudo feedback comprising:
submitting the target query to a search engine; and
receiving a set of search results as features from the search engine.
8 . The method of claim 1 , the extracting comprising:
extracting implicit feedback comprising contextual information corresponding to invoked search results.
9 . The method of claim 1 , the extracting comprising:
extracting a feature corresponding to a direct association of a category between a first query and a second query within a session.
10 . The method of claim 4 , the extracting comprising:
extracting a feature corresponding to an association of a once removed category within the taxonomy between a first query and a second query within a session.
11 . A system for classifying a target query based upon contextual information, comprising:
a feature extraction component configured to:
retrieve contextual information comprising pervious queries and corresponding invoked search results for respective previous queries; and
extract features from the contextual information; and
a classification component configured to:
classify a target query with a classification based upon the extracted features.
12 . The system of claim 11 , comprising:
a taxonomy comprising a hierarchy of classifications.
13 . The system of claim 12 , the classification component configured:
classify the target query with a classification within the taxonomy.
14 . The system of claim 11 , the contextual information comprising a session, the session comprising the target query.
15 . The system of claim 14 , comprising:
a CRF model comprising classifications for one or more queries within respective sessions.
16 . The system of claim 15 , comprising:
a CRF trainer configured to interpolate values for queries without classifications within a session of the CRF model based upon classifications for one or more queries within the session.
17 . The system of claim 15 , the classification component configured to:
classify the target query based upon generalized parameters derived from classified queries within the CRF model.
18 . The system of claim 11 , the feature extraction component configured to:
extract query terms as features;
extract pseudo feedback as features; and
extract implicit feedback comprising contextual information corresponding to invoked search results.
19 . The system of claim 11 , the feature extraction component configured to:
extract a feature corresponding to a direct association of a category between a first query and a second query within a session; and
extract a feature corresponding to an association of a once removed category within the taxonomy between a first query and a second query within a session.
20 . A system for classifying a query based upon contextual information, comprising:
a CRF trainer configured to:
extract contextual information as a sequence of queries and corresponding invoked search results for respective queries within the sequence;
extract features from the contextual information;
classify the sequence of queries based upon the extracted features; and
train a CRF model based upon the classification;
a feature extraction component configured to:
retrieve contextual information of a target query, the contextual information comprising pervious queries and corresponding invoked search results for respective queries; and
extract features from the contextual information of the target query; and
a classification component configured to:
classify the target query based upon the extracted features of the target query and generalized parameters derived from classified queries within the CRF model.