Generating alternative queries, also known as query suggestion, has long been proved useful to help a user explore and express his information need. In many scenarios, such suggestions can be generated from a large scale graph of queries and other accessory information, such as the clickthrough. However, how to generate suggestions while ensuring their semantic consistency with the original query remains a challenging problem. In this work, we propose a novel query suggestion algorithm based on ranking queries with the hitting time on a large scale bipartite graph. Without involvement of twisted heuristics or heavy tuning of parameters, this method clearly captures the semantic consistency between the suggested query and the original query. Empirical experiments on a large scale query log of a commercial search engine and a scientific literature collection show that hitting time is effective to generate semantically consistent query suggestions. The proposed algorithm and its variations can successfully boost long tail queries, accommodating personalized query suggestion, as well as finding related authors in research.

This paper addresses the issue of query refinement, which involves reformulating ill-formed search queries in order to enhance relevance of search results. Query refinement typically includes a number of tasks such as spelling error correction, word splitting, word merging, phrase segmentation, word stemming, and acronym expansion. In previous research, such tasks were addressed separately or through employing generative models. This paper proposes employing a unified and discriminative model for query refinement. Specifically, it proposes a Conditional Random Field (CRF) model suitable for the problem, referred to as Conditional Random Field for Query Refinement (CRF-QR). Given a sequence of query words, CRF-QR predicts a sequence of refined query words as well as corresponding refinement operations. In that sense, CRF-QR differs greatly from conventional CRF models. Two types of CRF-QR models, namely a basic model and an extended model are introduced. One merit of employing CRF-QR is that different refinement tasks can be performed simultaneously and thus the accuracy of refinement can be enhanced. Furthermore, the advantages of discriminative models over generative models can be fully leveraged. Experimental results demonstrate that CRF-QR can significantly outperform baseline methods. Furthermore, when CRF-QR is used in web search, a significant improvement of relevance can be obtained.

This paper explores the use of clickthrough data for query spelling correction. First, large amounts of query-correction pairs are derived by analyzing users ' query reformulation behavior encoded in the clickthrough data. Then, a phrase-based error model that accounts for the transformation probability between multi-term phrases is trained and integrated into a query speller system. Experiments are carried out on a human-labeled data set. Results show that the system using the phrase-based error model outperforms significantly its baseline systems. 1

This paper makes three significant extensions to a noisy channel speller designed for standard written text to target the challenging domain of search queries. First, the noisy channel model is subsumed by a more general ranker, which allows a variety of features to be easily incorporated. Second, a distributed infrastructure is proposed for training and applying Web scale n-gram language models. Third, a new phrase-based error model is presented. This model places a probability distribution over transformations between multi-word phrases, and is estimated using large amounts of query-correction pairs derived from search logs. Experiments show that each of these extensions leads to significant improvements over the state-of-the-art baseline methods. 1

Challenging the implicit reliance on document collections, this paper discusses the pros and cons of using query logs rather than document collections, as self-contained sources of data in textual information extraction. The differences are quantified as part of a large-scale study on extracting prominent attributes or quantifiable properties of classes (e.g., top speed, price and fuel consumption for CarModel) from unstructured text. In a head-to-head qualitative comparison, a lightweight extraction method produces class attributes that are 45 % more accurate on average, when acquired from query logs rather than Web documents. Categories and Subject Descriptors

Text search is a key step in any kind of information access. For doing it effectively, we can use knowledge about the concerned writing systems. Methods based on such knowledge can give significantly better results for searching text, at least for some languages. This can improve information retrieval in particular and information access in general. In this paper, we present a method for fuzzy text search for languages which use Abugida scripts, e.g. Hindi, Bengali, Telugu, Amharic, Thai etc. We use characteristics of a writing system for fuzzy search and are able to take care of spelling variation, which is very common in these languages. Our method shows an improvement in F-measure of up to 30% over scaled edit distance.

String transformation, which maps a source string s into its desirable form t ∗ , is related to various applications including stemming, lemmatization, and spelling correction. The essential and important step for string transformation is to generate candidates to which the given string s is likely to be transformed. This paper presents a discriminative approach for generating candidate strings. We use substring substitution rules as features and score them using an L1-regularized logistic regression model. We also propose a procedure to generate negative instances that affect the decision boundary of the model. The advantage of this approach is that candidate strings can be enumerated by an efficient algorithm because the processes of string transformation are tractable in the model. We demonstrate the remarkable performance of the proposed method in normalizing inflected words and spelling variations. School of Computer Science,

In this paper, we study the problem of online spelling correction for query completions. Misspelling is a common phenomenon among search engines queries. In order to help users effectively express their information needs, mechanisms for automatically correcting misspelled queries are required. Online spelling correction aims to provide spell corrected completion suggestions as a query is incrementally entered. As latency is crucial to the utility of the suggestions, such an algorithm needs to be not only accurate, but also efficient. To tackle this problem, we propose and study a generative model for input queries, based on a noisy channel transformation of the intended queries. Utilizing spelling correction pairs, we train a Markov n-gram transformation model that captures user spelling behavior in an unsupervised fashion. To find the top spellcorrected completion suggestions in real-time, we adapt the A* search algorithm with various pruning heuristics to dynamically expand the search space efficiently. Evaluation of the proposed methods demonstrates a substantial increase in the effectiveness of online spelling correction over existing techniques.

This paper proposes a new method for approximate string search, specifically candidate generation in spelling error correction, which is a task as follows. Given a misspelled word, the system finds words in a dictionary, which are most “similar ” to the misspelled word. The paper proposes a probabilistic approach to the task, which is both accurate and efficient. The approach includes the use of a log linear model, a method for training the model, and an algorithm for finding the top k candidates. The log linear model is defined as a conditional probability distribution of a corrected word and a rule set for the correction conditioned on the misspelled word. The learning method employs the criterion in candidate generation as loss function. The retrieval algorithm is efficient and is guaranteed to find the optimal k candidates. Experimental results on large scale data show that the proposed approach improves upon existing methods in terms of accuracy in different settings. 1

data is suggesting alternative queries when user queries contain typographical errors. Query suggestion thus can improve users’ search experience by avoiding returning empty result or results of poor qualities. In this paper, we study the problem of effectively and efficiently providing quality query suggestions for keyword queries on an XML document. We illustrate certain biases in previous work and propose a principled and general framework, XClean, based on the state-of-the-art language model. Compared with previous methods, XClean can accommodate different error models and XML keyword query semantics without losing rigor. Algorithms have been developed that compute the top-k suggestions efficiently. We performed an extensive experiment study using two large-scale real datasets. The experiment results demonstrate the effectiveness and efficiency of the proposed methods. I.