Jian-Yun Nie

Query Clustering Using User Logs

Query clustering is a process used to discover frequently asked questions or most popular topics on a search engine. This process is crucial for search engines based on question-answering. Because of the short lengths of queries, approaches based on keywords are not suitable for query clustering. This paper describes a new query clustering method that makes use of user logs which allow us to identify the documents the users have selected for a query. The similarity between two queries may be deduced from the common documents the users selected for them. Our experiments show that a combination of both keywords and user logs is better than using either method alone.

Clustering user queries of a search engine

In order to increase retrieval precision, some new search engines provide manually verified answers to Frequently Asked Queries (FAQs). An underlying task is the identification of FAQs. This paper describes our attempt to cluster similar queries according to their contents as well as user logs. Our preliminary results show that the resulting clusters provide useful information for FAQ identification.

Probabilistic query expansion using query logs

Query expansion has long been suggested as an effective way to resolve the short query and word mismatching problems. A number of query expansion methods have been proposed in traditional information retrieval. However, these previous methods do not take into account the specific characteristics of web searching; in particular, of the availability of large amount of user interaction information recorded in the web query logs. In this study, we propose a new method for query expansion based on query logs. The central idea is to extract probabilistic correlations between query terms and document terms by analyzing query logs. These correlations are then used to select high-quality expansion terms for new queries. The experimental results show that our log-based probabilistic query expansion method can greatly improve the search performance and has several advantages over other existing methods.

Query expansion by mining user logs

Queries to search engines on the Web are usually short. They do not provide sufficient information for an effective selection of relevant documents. Previous research has proposed the utilization of query expansion to deal with this problem. However, expansion terms are usually determined on term co-occurrences within documents. In this study, we propose a new method for query expansion based on user interactions recorded in user logs. The central idea is to extract correlations between query terms and document terms by analyzing user logs. These correlations are then used to select high-quality expansion terms for new queries. Compared to previous query expansion methods, ours takes advantage of the user judgments implied in user logs. The experimental results show that the log-based query expansion method can produce much better results than both the classical search method and the other query expansion methods.

Selecting good expansion terms for pseudo-relevance feedback

Pseudo-relevance feedback assumes that most frequent terms in the pseudo-feedback documents are useful for the retrieval. In this study, we re-examine this assumption and show that it does not hold in reality - many expansion terms identified in traditional approaches are indeed unrelated to the query and harmful to the retrieval. We also show that good expansion terms cannot be distinguished from bad ones merely on their distributions in the feedback documents and in the whole collection. We then propose to integrate a term classification process to predict the usefulness of expansion terms. Multiple additional features can be integrated in this process. Our experiments on three TREC collections show that retrieval effectiveness can be much improved when term classification is used. In addition, we also demonstrate that good terms should be identified directly according to their possible impact on the retrieval effectiveness, i.e. using supervised learning, instead of unsupervised learning.

Cross-language information retrieval based on parallel texts and automatic mining of parallel texts from the Web

This paper describes the use of a probabilistic translation model to cross-language IR (CLIR). The performance of this approach is compared with that using machine translation (MT). It is shown that using a probabilistic model, we are able to obtain performances close to those using an MT system. In addition, we also investigated the possibility of automatically gather parallel texts from the Web in an attempt to construct a reasonable training corpus. The result is very encouraging. We showed that in several tests, such a training corpus is as good as a manually constructed one for CLIR purposes.

A Neural Network Approach to Context-Sensitive Generation of Conversational Responses

We present a novel response generation system that can be trained end to end on large quantities of unstructured Twitter conversations. A neural network architecture is used to address sparsity issues that arise when integrating contextual information into classic statistical models, allowing the system to take into account previous dialog utterances. Our dynamic-context generative models show consistent gains over both context-sensitive and non-context-sensitive Machine Translation and Information Retrieval baselines.

Query expansion using term relationships in language models for information retrieval

Language Modeling (LM) has been successfully applied to Information Retrieval (IR). However, most of the existing LM approaches only rely on term occurrences in documents, queries and document collections. In traditional unigram based models, terms (or words) are usually considered to be independent. In some recent studies, dependence models have been proposed to incorporate term relationships into LM, so that links can be created between words in the same sentence, and term relationships (e.g. synonymy) can be used to expand the document model. In this study, we further extend this family of dependence models in the following two ways: (1) Term relationships are used to expand query model instead of document model, so that query expansion process can be naturally implemented; (2) We exploit more sophisticated inferential relationships extracted with Information Flow (IF). Information flow relationships are not simply pairwise term relationships as those used in previous studies, but are between a set of terms and another term. They allow for context-dependent query expansion. Our experiments conducted on TREC collections show that we can obtain large and significant improvements with our approach. This study shows that LM is an appropriate framework to implement effective query expansion.

Integrating word relationships into language models

In this paper, we propose a novel dependency language modeling approach for information retrieval. The approach extends the existing language modeling approach by relaxing the independence assumption. Our goal is to build a language model in which various word relationships can be integrated. In this work, we integrate two types of relationship extracted from WordNet and co-occurrence relationships respectively. The integrated model has been tested on several TREC collections. The results show that our model achieves substantial and significant improvements with respect to the models without these relationships. These results clearly show the benefit of integrating word relationships into language models for IR.

Improving query translation for cross-language information retrieval using statistical models

Dictionaries have often been used for query translation in cross-language information retrieval (CLIR). However, we are faced with the problem of translation ambiguity, i.e. multiple translations are stored in a dictionary for a word. In addition, a word-by-word query translation is not precise enough. In this paper, we explore several methods to improve the previous dictionary-based query translation. First, as many as possible, noun phrases are recognized and translated as a whole by using statistical models and phrase translation patterns. Second, the best word translations are selected based on the cohesion of the translation words. Our experimental results on TREC English-Chinese CLIR collection show that these techniques result in significant improvements over the simple dictionary approaches, and achieve even better performance than a high-quality machine translation system.