Encyclopedic queries express the intent of obtaining information typically available in encyclopedias, such as biographical, geographical or historical facts. In this paper, we train a classifier for detecting the encyclopedic intent of web queries. For training such a classifier, we automatically label training data from raw query logs. We use click-through data to select positive examples of encyclopedic queries as those queries that mostly lead to Wikipedia articles. We investigated a large set of features that can be generated to describe the input query. These features include both term-specific patterns as well as query projections on knowledge bases items (e.g. Freebase). Results show that using these feature sets it is possible to achieve an F1 score above 87 , competing with a Google-based baseline, which uses a much wider set of signals to boost the ranking of Wikipedia for potential encyclopedic queries. The results also show that both query projections on Wikipedia article titles and Freebase entity match represent the most relevant groups of features. When the training set contains frequent positive examples (i.e rare queries are excluded) results tend to improve.
The user intent behind a search query has been subject of intense study during the last decade. Previous research works have represented user intent by query classification, by existing knowledge base (e.g. Wikipedia) or by queries and click-through data. The most common approach consists in representing user intent by classifying a query into a pre-defined category of goals tasks or topics. Broder @cite_4 has proposed three categories representing the users' goal: navigational, transactional and informational queries. This has been the basis of several research works attempting to automatically identify the users' goal into these three categories @cite_9 @cite_1 . Rose @cite_8 and Baeza-Yates @cite_2 studied the users' intent problem and have proposed slight changes to the Broder's taxonomy.
@cite_4 @cite_8 @cite_9 @cite_1 @cite_2
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In this paper, we define and present a comprehensive classification of user intent for Web searching. The classification consists of three hierarchical levels of informational, navigational, and transactional intent. After deriving attributes of each, we then developed a software application that automatically classified queries using a Web search engine log of over a million and a half queries submitted by several hundred thousand users. Our findings show that more than 80 of Web queries are informational in nature, with about 10 each being navigational and transactional. In order to validate the accuracy of our algorithm, we manually coded 400 queries and compared the results from this manual classification to the results determined by the automated method. This comparison showed that the automatic classification has an accuracy of 74 . Of the remaining 25 of the queries, the user intent is vague or multi-faceted, pointing to the need for probabilistic classification. We discuss how search engines can use knowledge of user intent to provide more targeted and relevant results in Web searching. This work presents the use of click graphs in improving query intent classifiers, which are critical if vertical search and general-purpose search services are to be offered in a unified user interface. Previous works on query classification have primarily focused on improving feature representation of queries, e.g., by augmenting queries with search engine results. In this work, we investigate a completely orthogonal approach --- instead of enriching feature representation, we aim at drastically increasing the amounts of training data by semi-supervised learning with click graphs. Specifically, we infer class memberships of unlabeled queries from those of labeled ones according to their proximities in a click graph. Moreover, we regularize the learning with click graphs by content-based classification to avoid propagating erroneous labels. We demonstrate the effectiveness of our algorithms in two different applications, product intent and job intent classification. In both cases, we expand the training data with automatically labeled queries by over two orders of magnitude, leading to significant improvements in classification performance. An additional finding is that with a large amount of training data obtained in this fashion, classifiers using only query words phrases as features can work remarkably well. Behind every search query is a high-level mission that the user wants to accomplish. While current search engines can often provide relevant information in response to well-specified queries, they place the heavy burden of making a plan for achieving a mission on the user. We take the alternative approach of tackling users' high-level missions directly by introducing a human computation system that generates simple plans, by decomposing a mission into goals and retrieving search results tailored to each goal. Results show that our system is able to provide users with diverse, actionable search results and useful roadmaps for accomplishing their missions. People often implicitly or explicitly express their needs in social media in the form of "user status text". Such text can be very useful for service providers and product manufacturers to proactively provide relevant services or products that satisfy people's immediate needs. In this paper, we study how to infer a user's intent based on the user's "status text" and retrieve relevant mobile apps that may satisfy the user's needs. We address this problem by framing it as a new entity retrieval task where the query is a user's status text and the entities to be retrieved are mobile apps. We first propose a novel approach that generates a new representation for each query. Our key idea is to leverage social media to build parallel corpora that contain implicit intention text and the corresponding explicit intention text. Specifically, we model various user intentions in social media text using topic models, and we predict user intention in a query that contains implicit intention. Then, we retrieve relevant mobile apps with the predicted user intention. We evaluate the mobile app retrieval task using a new data set we create. Experiment results indicate that the proposed model is effective and outperforms the state-of-the-art retrieval models.