Understanding the intent behind a user's query can help search engine to automatically route the query to some corresponding vertical search engines to obtain particularly relevant contents, thus, greatly improving user satisfaction. There are three major challenges to the query intent classification problem: (1) Intent representation; (2) Domain coverage and (3) Semantic interpretation. Current approaches to predict the user's intent mainly utilize machine learning techniques. However, it is difficult and often requires many human efforts to meet all these challenges by the statistical machine learning approaches. In this paper, we propose a general methodology to the problem of query intent classification. With very little human effort, our method can discover large quantities of intent concepts by leveraging Wikipedia, one of the best human knowledge base. The Wikipedia concepts are used as the intent representation space, thus, each intent domain is represented as a set of Wikipedia articles and categories. The intent of any input query is identified through mapping the query into the Wikipedia representation space. Compared with previous approaches, our proposed method can achieve much better coverage to classify queries in an intent domain even through the number of seed intent examples is very small. Moreover, the method is very general and can be easily applied to various intent domains. We demonstrate the effectiveness of this method in three different applications, i.e., travel, job, and person name. In each of the three cases, only a couple of seed intent queries are provided. We perform the quantitative evaluations in comparison with two baseline methods, and the experimental results shows that our method significantly outperforms other methods in each intent domain.

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Understanding user's query intent with wikipedia

Web query classification (QC) aims to classify Web users' queries, which are often short and ambiguous, into a set of target categories QC has many applications including page ranking in Web search, targeted advertisement in response to queries, and personalization In this paper, we present a novel approach for QC that outperforms the winning solution of the ACM KDDCUP 2005 competition, whose objective is to classify 800,000 real user queries In our approach, we first build a bridging classifier on an intermediate taxonomy in an offline mode This classifier is then used in an online mode to map user queries to the target categories via the above intermediate taxonomy A major innovation is that by leveraging the similarity distribution over the intermediate taxonomy, we do not need to retrain a new classifier for each new set of target categories, and therefore the bridging classifier needs to be trained only once In addition, we introduce category selection as a new method for narrowing down the scope of the intermediate taxonomy based on which we classify the queries Category selection can improve both efficiency and effectiveness of the online classification By combining our algorithm with the winning solution of KDDCUP 2005, we made an improvement by 97% and 38% in terms of precision and F1 respectively compared with the best results of KDDCUP 2005

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Building bridges for web query classification

We present a simple and scalable algorithm for clustering tens of millions of phrases and use the resulting clusters as features in discriminative classifiers. To demonstrate the power and generality of this approach, we apply the method in two very different applications: named entity recognition and query classification. Our results show that phrase clusters offer significant improvements over word clusters. Our NER system achieves the best current result on the widely used CoNLL benchmark. Our query classifier is on par with the best system in KDDCUP 2005 without resorting to labor intensive knowledge engineering efforts.

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Phrase Clustering for Discriminative Learning

We propose a methodology for building a practical robust query classification system that can identify thousands of query classes with reasonable accuracy, while dealing in real-time with the query volume of a commercial web search engine. We use a blind feedback technique: given a query, we determine its topic by classifying the web search results retrieved by the query. Motivated by the needs of search advertising, we primarily focus on rare queries, which are the hardest from the point of view of machine learning, yet in aggregation account for a considerable fraction of search engine traffic. Empirical evaluation confirms that our methodology yields a considerably higher classification accuracy than previously reported. We believe that the proposed methodology will lead to better matching of online ads to rare queries and overall to a better user experience.

Robust classification of rare queries using web knowledge

Web search engines have stored in their logs information about users since they started to operate. This information often serves many purposes. The primary focus of this survey is on introducing to the discipline of query mining by showing its foundations and by analyzing the basic algorithms and techniques that are used to extract useful knowledge from this (potentially) infinite source of information. We show how search applications may benefit from this kind of analysis by analyzing popular applications of query log mining and their influence on user experience. We conclude the paper by, briefly, presenting some of the most challenging current open problems in this field.

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Mining Query Logs: Turning Search Usage Data into Knowledge

The performance of search engines crucially depends on their ability to capture the meaning of a query most likely intended by the user. We study the problem of mapping a search engine query to those nodes of a given subject taxonomy that characterize its most likely meanings. We describe the architecture of a classification system that uses a web directory to identify the subject context that the query terms are frequently used in. Based on its performance on the classification of 800,000 example queries recorded from MSN search, the system received the Runner-Up Award for Query Categorization Performance of the KDD Cup 2005.

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Classifying search engine queries using the web as background knowledge

An approach is provided for determining the similarity between a reference point-of-interest and similarity candidate points-of-interest. Data specifying a reference point-of-interest and location data of a search region are received. A reference vector specifying a plurality of features associated with the reference point-of-interest is retrieved. A plurality of candidates for similar points-of-interest are determined based, at least in part, on the search region. A similarity score is determined for each of the candidates. A list of one or more similar points-of-interest is generated based on the similarity scores.

Method and apparatus for the retrieval of similar places

An approach is provided for determining the similarity between two points-of-interest. Data about a plurality of features of each of a plurality of points-of-interest are received. A feature vector is created based on the respective features for each of the points-of-interest. A geospatial index comprising the feature vectors and associated location data for each of the points-of-interest is generated.

Peter Siemen

Papers

Classifying search engine queries using the web as background knowledge

Method and apparatus for the retrieval of similar places

Method and apparatus for preparation of indexing structures for determining similar points-of-interests