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.

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.

Enhancing Amharic Information Retrieval System Based on Statistical Co-Occurrence Technique

Information retrieval (IR) systems are designed to help information seekers retrieving relevant information from vast document. The need for relevant information from a vast amount of document gave birth to IR systems. Even though different IR systems exist, they cannot meet all users’ expectations. A different level of users’ knowledge makes queries to be expressed in different ways. As a result, the system may miss the core meaning of users query and retrieve dissatisfactory results. This happens mainly because of the ambiguities of words involved in the natural languages and expression mismatch among users and authors. The existing ambiguities in Amharic language have negative impacts on the performance of Amharic IR system. Some of the ambiguities for this type of problem are: spelling variants of the same word, polysemous and synonymous terms. If users are not fully knowledgeable about the information domain area, they will mostly formulate weak queries to retrieve documents. Thus, they end up frustrated with the results found from an IR system. This research has been conducted, aiming at augmenting the recall of previous work. Statistical co-occurrence technique has been used in order to expand query terms. The main reason for performing query expansion is to provide relevant documents as per users’ query that can satisfy their information need. Statistical co-occurrence method considers, frequently appearing terms with the query term, regardless of their position. The efficiency of proposed technique has been tested on the prototype system and the result found compared with the result of previous study. Accordingly, 6% recall and 2% f-measure improvement has been made. Hence, the statistical co-occurrence method outperformed the bi-gram based IR system.

Dominant Meaning Method for Intelligent Topic-Based Information Agent towards More Flexible MOOCs

The use of agent technology in a dynamic environment is rapidly growing as one of the powerful technologies and the need to provide the benefits of the Intelligent Information Agent technique to massive open online courses, is very important from various aspects including the rapid growing of MOOCs environments, and the focusing more on static information than on updated information. One of the main problems in such environment is updating the information to the needs of the student who interacts at each moment. Using such technology can ensure more flexible information, lower waste time and hence higher earnings in learning. This paper presents Intelligent Topic-Based Information Agent to offer an updated knowledge including various types of resource for students. Using dominant meaning method, the agent searches the Internet, controls the metadata coming from the Internet, filters and shows them into a categorized content lists. There are two experiments conducted on the Intelligent Topic-Based Information Agent: one measures the improvement in the retrieval effectiveness and the other measures the impact of the agent on the learning. The experiment results indicate that our methodology to expand the query yields a considerable improvement in the retrieval effectiveness in all categories of Google Web Search API. On the other hand, there is a positive impact on the performance of learning session.

基于知识图谱的零样本文档检索伪查询生成

为提高文档检索模型性能,减轻手工标记训练数据的工作量,提出一种基于知识图谱的零样本文档检索伪查询生成方法KGQG。该方法利用知识图谱增强伪查询,将外部信息与伪查询相结合,以生成更丰富、更具信息量的伪查询。实验结果表明,在BEIR基准测试的12个公开数据集中,KGQG方法比经典的稀疏检索模型、稠密检索模型以及最新的基于外部知识扩展的零样本稠密检索模型在归一化折现累计效益(NDCG)指标方面分别提升了4.6、11.88、7.96个百分点。KGQG方法不仅能提高检索性能,而且减少了手动标记训练数据需求,为文档检索模型的未来研究与应用提供了有益参考。

基于用户日志的查询扩展统计模型

信息检索长期存在着用词歧义性问题,在Web搜索上的表现更加突出.提出了一种基于用户查询日志的查询扩展统计模型,将用户查询中使用的词或短语与文档中出现的相应词或短语以条件概率的形式连接,利用贝叶斯公式挑选出文档中与该查询关联最紧密的词加入原查询,以达到扩展优化的目的.实验结果表明,该方法更适宜改进Web上的信息检索,相对传统的查询扩展算法可以大幅度提高查询精度.

查询扩展技术进展与展望

查询扩展技术是改善信息检索中查全率和查准率的关键技术之一,并成为近年来研究的热点。以查询扩展技术的发展为主线,将目前的研究方法归纳为三类:关键词查询扩展技术、查询词权重调整技术和语义概念查询扩展技术。对这三类扩展技术进行了整体性阐述和比较性研究,对一些关键性问题进行了探讨,并展望未来的发展趋势。

一种基于上下文的中文信息检索查询扩展

在中文信息检索的研究和实践中 ,由于查询中所使用的词可能与文件集中使用的词不匹配而导致一些相关的文件不能被成功地检索出来 ,这是影响检索效果的一个很关键的问题。查询扩展可以在一定程度上解决这种词的不匹配现象 ,然而 ,实验表明 ,通常简单的查询扩展并不能稳定地提高中文信息检索的检索效果。本论文中提出并实现了一种基于上下文的查询扩展方法 ,可以根据查询的上下文对扩展词进行选择 ,是一种相对“智能”的查询扩展方法。在TREC - 9中文信息检索测试集上进行的实验表明 ,相对于通常简单的查询扩展 ,基于上下文的查询扩展方法取得了具有统计意义提高的检索效果。

基于矩阵加权关联规则挖掘的伪相关反馈查询扩展

提出一种面向查询扩展的矩阵加权关联规则挖掘算法,给出与其相关的定理及其证明过程.该算法采用4种剪枝策略,挖掘效率得到极大提高.实验结果表明,其挖掘时间比原来的平均时间减少87.84%.针对现有查询扩展的缺陷,将矩阵加权关联规则挖掘技术应用于查询扩展,提出新的查询扩展模型和更合理的扩展词权重计算方法.在此基础上提出一种伪相关反馈查询扩展算法——基于矩阵加权关联规则挖掘的伪相关反馈查询扩展算法,该算法能够自动地从前列n篇初检文档中挖掘与原查询相关的矩阵加权关联规则,构建规则库,从中提取与原查询相关的扩展词,实现查询扩展.实验结果表明,该算法的检索性能确实得到了很好的改善.与现有查询扩展算法相比,在相同的查全率水平级下,其平均查准率有了明显的提高.

中医药信息智能检索系统构建方法研究

针对现有中医药信息检索中存在的某些问题 ,将基于词表的扩展检索、相似性检索等方法与数据挖掘技术相结合 ,提出了一类智能化中医药信息检索系统的构建思想 ,据此建立了相应的智能检索系统 .该系统提供文献间的相似性检索 ,实现了数据的关联分析 ,提高了中医药文献的查全率 .经实际应用表明 ,本文方法具有查全率高 ,使用方便等优点 ,为建立新型中医药信息检索系统提供了有效途径 .

基于上下文的查询扩展

针对信息检索查询所使用的词可能与文档集中使用的词不匹配从而影响检索效果这一信息检索关键问题,提出了一种基于上下文的查询扩展方法,该方法根据查询的上下文信息对扩展词进行选择,同时考虑到扩展词与整个查询句以及与查询词的位置关系.在TREC信息检索测试集上进行的实验表明,相对于通常简单的语言模型,方法取得了5%～19%的提高.与流行的基于伪反馈的查询扩展方法相比,提出的方法也具有相当的平均准确率.

专利查询扩展的词向量方法研究

查询扩展技术被广泛地应用于信息检索系统中。为提高专利检索的结果,采用查询扩展方法进行优化,利用相关专利文本训练词向量,并选择与原始查询相似度高的候选词作为查询扩展词,加入原始查询中。提出4种方法运用词向量获取查询扩展词,并提出两种方法进行扩展词相关性排序,改进已有的查询扩展词选择方法。在TREC数据集上的实验显示,将词向量模型进行扩展词选择的方法与传统的TF-IDF扩展词选择方法相融合,可以有效提高查询扩展模型的性能,对于理解用户的查询意图有着很好的促进作用。

一种基于概念的信息检索查询扩展

文章针对信息检索中存在的查询词“表达差异”问题,提出一种基于概念的查询扩展方法。一方面将用户查询中使用的词或短语与文档中抽出的概念相连接加入原查询,同时将作为扩展词的概念进行分类查询并采用整合排序算法调整结果;另一方面引入概念图供用户手动调节来进行查询扩展,以达到查询优化的目的。试验结果表明,该方法适宜改进Web上的信息检索,相对没有扩展的查询可以大幅度提高查询精度。

一种基于局部分析面向事件的查询扩展方法

针对用户获取事件类信息的需求，提出了一种基于局部分析面向事件LA-EO（localanalysis-eventoriented）的查询扩展方法，该方法将查询项区分为事件项和限定项两类分别处理。文章重点讨论了面向事件的查询项分析、事件项的扩展以及查询项与文本相似度的计算等问题。围绕突发事件领域，使用搜索引擎和定点采集相结合的方法收集了4011篇文本，设置了10个查询项对本文提出的方法进行了实验比较。结果表明：LA—EO与Rocchio机制（记作LA—Rocchio）和局部上下文分析（记作LA—LCA）扩展方法相比，对事件类信息的检索，LA—EO具有更优的检索性能。

面向旅游领域的语义查询扩展方法

针对传统语义查询扩展方法存在返回结果多以及准确率不高的问题,以旅游领域为背景,提出一种语义加权查询扩展方法。利用本体推理能力与本体中的实体相关度进行查询扩展,结合TF-IDF算法的词频加权与语义相关度加权改善检索结果的排序。实验结果表明,相比其他2种方法,该方法能使更多符合要求的查询结果靠前排列,提高了旅游信息检索的正确率。

一种基于局部共现的查询扩展方法

针对信息检索中文档与查询之间的词不匹配问题,本文提出了一种基于局部共现的查询扩展方法LOCOOC。LOCOOC利用词项与所有查询词在局部文档集合中的共现程度来评估扩展词的质量,并整合了词项在语料集中的全局统计信息,使得选取的扩展词与初始查询所表征的主题或概念具有更好的相关性。实验结果表明:与未进行查询扩展时相比,采用LOCOOC方法进行扩展后,平均准确率提高40%以上;与传统的局部反馈方法以及局部上下文分析方法(LCA,Local ContextAnalysis)相比,LOCOOC不仅具有更优的检索性能,而且有着更好的鲁棒性。

基于语义的概念查询扩展

针对当前信息检索系统中所存在查准率低和查全率低的情况,分析了当前检索系统中常用的方法后,提出了一种基于语义的概念查询扩展方法。该方法结合概念语义空间来实现用户检索的概念查询扩展,以达到提高查准率和查全率的目的。实验结果表明,该方法相对于传统方法可以大幅提高用户检索的查准率和查全率。

一种基于同义词词典的模糊查询扩展方法

在信息检索系统中,查询扩展是一种非常有效的改进检索性能的方法.为此,提出一种基于同义词词典的模糊查询扩展方法.该方法中的同义词词典是基于著名的语义词典WordNet中的同义词集合建立的,同义词之间的贴近度[0,1]使用Tanimoto系数获得.利用该词典,能够进行较好的查询扩展.将该方法与向量空间模型结合应用于文本信息检索系统中,所构造的检索模型相当于一种简单的语义模型,并且可以根据阈值来控制查询扩展的程度.所得试验结果表明,使用该查询扩展方法的信息检索系统较常规信息检索系统的检索性能有一定改善.

基于不同信息资源专利查询扩展方法的研究

近年来查询扩展方法已被证明能有效地提升专利检索的平均性能,而大多数查询扩展方法仅使用实验数据集进行扩展词选择,很少有研究将外部信息源利用于专利检索,提高检索准确率。因此,本文在实验数据集基础上,同时采用一种利用外部资源的方法提升专利检索性能。该方法利用谷歌搜索引擎优化查询扩展方法的性能,并采用排序学习方法LambdaMART方法融合不同查询扩展方法,对信息源中的不同文本域进行加权处理,从而提升专利检索性能。在TREC数据集上的实验结果表明,基于本文所采用的信息资源进行查询重构的方法有效地提升了专利检索的性能。