Query Expansion for Chinese Information Retrieval by Using a Decaying Co-occurrence Model

Query expansion with thesaurus is one of the useful techniques in modern information retrieval (IR). In this paper, a method of query expansion for Chinese IR by using a decaying co-occurrence model is proposed and realized. The model is an extension of the traditional co-occurrence model by adding a decaying factor that decreases the mutual information when the distance between the terms increases. Experimental results on TREC-9 collections show this query expansion method results in significant improvements over the IR without query expansion.

Query Expansion Based on Semantics and Statistics in Chinese Question Answering System

In Chinese question answering system, because there is more semantic relation in questions than that in query words, the precision can be improved by expanding query while using natural language questions to retrieve documents. This paper proposes a new approach to query expansion based on semantics and statistics Firstly automatic relevance feedback method is used to generate a candidate expansion word set. Then the expanded query words are selected from the set based on the semantic similarity and seman- tic relevancy between the candidate words and the original words. Experiments show the new approach is effective for Web retrieval and out-performs the conventional expansion approaches.

A new approach to query expansion in information retrieval

To eliminate the mismatch between words of relevant documents and user's query and more seriousnegative effects it has on the performance of information retrieval,a method of query expansion on the ba-sis of new terms co-occurrence representation was put forward by analyzing the process of producingquery.The expansion terms were selected according to their correlation to the whole query.At the sametime,the position information between terms were considered.The experimental result on test retrievalconference(TREC)data collection shows that the method proposed in the paper has made an improve-ment of 5%～19% all the time than the language modeling method without expansion.Compared to thepopular approach of query expansion,pseudo feedback,the precision of the proposed method is competi-tive.

Deep Neural Network and Pseudo Relevance Feedback Based Query Expansion

The neural network has attracted researchers immensely in the last couple of years due to its wide applications in various areas such as Data mining,Natural language processing,Image processing,and Information retrieval etc.Word embedding has been applied by many researchers for Information retrieval tasks.In this paper word embedding-based skip-gram model has been developed for the query expansion task.Vocabulary terms are obtained from the top“k”initially retrieved documents using the Pseudo relevance feedback model and then they are trained using the skip-gram model to find the expansion terms for the user query.The performance of the model based on mean average precision is 0.3176.The proposed model compares with other existing models.An improvement of 6.61%,6.93%,and 9.07%on MAP value is observed compare to the Original query,BM25 model,and query expansion with the Chi-Square model respectively.The proposed model also retrieves 84,25,and 81 additional relevant documents compare to the original query,query expansion with Chi-Square model,and BM25 model respectively and thus improves the recall value also.The per query analysis reveals that the proposed model performs well in 30,36,and 30 queries compare to the original query,query expansion with Chi-square model,and BM25 model respectively.

A Novel Web Query Automatic Expansion Based on Rough Set

One of important reasons caused low precision was presented, which was due to inaccurate express of the query. So a new method of automatic query expansion based on tolerance rough was put forward. In the algorithm, the uncertain connection between query terms and retrial documents was described as term tolerance class. The upper approximation set of query sentence was considered as query expansion. The new additional terms were also given weight numbers. The results of experiment on collection of Google 5 000 Web pages showed that the approach was effective on query expansion and high search precision was gained.

Choosing meaningful structure data for improving web search

In order to improve the quality of web search,a new query expansion method by choosing meaningful structure data from a domain database is proposed.It categories attributes into three different classes,named as concept attribute,context attribute and meaningless attribute,according to their semantic features which are document frequency features and distinguishing capability features.It also defines the semantic relevance between two attributes when they have correlations in the database.Then it proposes trie-bitmap structure and pair pointer tables to implement efficient algorithms for discovering attribute semantic feature and detecting their semantic relevances.By using semantic attributes and their semantic relevances,expansion words can be generated and embedded into a vector space model with interpolation parameters.The experiments use an IMDB movie database and real texts collections to evaluate the proposed method by comparing its performance with a classical vector space model.The results show that the proposed method can improve text search efficiently and also improve both semantic features and semantic relevances with good separation capabilities.

A dynamic knowledge base based search engine

Search engines have greatly helped us to find the desired information from the Internet. Most search engines use keywords matching technique. This paper discusses a Dynamic Knowledge Base based Search Engine （DKBSE）, which can expand the user＇s query using the keywords＇ concept or meaning. To do this, the DKBSE needs to construct and maintain the knowledge base dynamically via the system＇s searching results and the user＇s feedback information. The DKBSE expands the user＇s initial query using the knowledge base, and returns the searched information after the expanded query.

Strength Pareto fitness assignment for pseudo-relevance feedback： application to MEDLINE

Because of users＇ growing utilization of unclear and imprecise keywords when characterizing their informa- tion need, it has become necessary to expand their original search queries with additional words that best capture their actual intent. The selection of the terms that are suitable for use as additional words is in general dependent on the degree of relatedness between each candidate expansion term and the query keywords. In this paper, we propose two criteria for evaluating the degree of relatedness between a candidate expansion word and the query keywords： （1） co-occurrence frequency, where more importance is attributed to terms oc- curring in the largest possible number of documents where the query keywords appear; （2） proximity, where more im- portance is assigned to terms having a short distance from the query terms within documents. We also employ the strength Pareto fitness assignment in order to satisfy both criteria si- multaneously. The results of our numerical experiments on MEDLINE, the online medical information database, show that the proposed approach significantly enhances the re- trieval performance as compared to the baseline.

The Application of the Comparable Corpora in Chinese-English Cross-Lingual Information Retrieval

This paper proposes a novel Chinese-English Cross-Lingual Information Retrieval (CECLIR) model PME, in which bilingual dictionary and comparable corpora are used to translate the query terms. The Proximity and mutual information of the term-pairs in the Chinese and English comparable corpora are employed not only to resolve the translation ambiguities but also to perform the query expansion so as to deal with the out-of-vocabulary issues in the CECLIR. The evaluation results show that the query precision of PME algorithm is about 84.4% of the monolingual information retrieval.

Visual polysemy and synonymy:toward near-duplicate image retrieval

that are duplicate or near duplicate to a query image.One of the most popular and practical methods in near-duplicate image retrieval is based on bag-of-words(BoW)model.However,the fundamental deficiency of current BoW method is the gap between visual word and image’s semantic meaning.Similar problem also plagues existing text retrieval.A prevalent method against such issue in text retrieval is to eliminate text synonymy and polysemy and therefore improve the whole performance.Our proposed approach borrows ideas from text retrieval and tries to overcome these deficiencies of BoW model by treating the semantic gap problem as visual synonymy and polysemy issues.We use visual synonymy in a very general sense to describe the fact that there are many different visual words referring to the same visual meaning.By visual polysemy,we refer to the general fact that most visual words have more than one distinct meaning.To eliminate visual synonymy,we present an extended similarity function to implicitly extend query visual words.To eliminate visual polysemy,we use visual pattern and prove that the most efficient way of using visual pattern is merging visual word vector together with visual pattern vector and obtain the similarity score by cosine function.In addition,we observe that there is a high possibility that duplicates visual words occur in an adjacent area.Therefore,we modify traditional Apriori algorithm to mine quantitative pattern that can be defined as patterns containing duplicate items.Experiments prove quantitative patterns improving mean average precision(MAP)significantly.