信息检索中信息需求域的研究
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摘要
信息检索作为信息获取的手段,是信息处理的重要内容,是当前信息处理研究领域中的研究热点。信息检索主要包括三个方面的内容：信息需求表达、文档表达和检索模型。其中,需求表达是一个重要的环节。只有当需求被正确地理解和表达时,才有可能得到好的检索结果；否则,如果不能很好地表达需求,再好的检索系统也无法得到好的检索结果。当前,机器检索本质上是基于关键词匹配的检索,将用户查询请求假定为是对用户信息需求的一个准确描述。但事实上,查询请求往往不能准确地描述用户的信息需求,这将不可避免地导致不太理想的检索结果。
为了更好地描述和表达用户的信息需求,通常使用相关反馈的方法。相关反馈(包括用户相关反馈和伪相关反馈)方法试图从反馈的文档中寻找一组关联词项以增强用户的初始查询请求。实验显示,这种处理方法有一定的效果。然而,注意到这些词项的选取只是一种启发式的想法,通常假定用户的信息需求具有一个准确的描述。相关反馈方法试图利用反馈信息寻求用户需求的准确描述,但实际上用户需求的准确描述是难以得到的。这种利用相关反馈的方法进行的查询扩展只是对用户信息需求的一种猜想,并不准确。
在本文中,我们采取了不同的方法。我们假定需求是一个语义范围。一开始,用户使用初始查询提出查询请求,当我们获得了一些反馈信息后(用户相关反馈或伪相关反馈),我们就能够建立一种对信息需求更好的描述,但这种描述不是试图去建立对信息需求的准确描述,而是概括性地去框定一个需求的范围。我们使用反馈信息建立需求的下界R和上界R,从而界定需求的一个范围。下界对应反馈文档的共有部分,上界对应反馈文档的全部内容。论文导出了需求的下界和上界,得到了需求域的两个边界,从而建立了需求域模型I=(R,R)。信息需求域具有以下特点：
(1)信息需求域的下界表达了信息需求集中关注的内容,代表了信息需求的精度,也代表了信息需求的内涵；
(2)信息需求域的上界包含了信息需求的延伸和扩展的内容,代表了信息需求的广度,也代表了信息需求的外延；
(3)信息需求域较为松散地界定了用户信息需求的一个范围。
论文中给出了建立需求域的两种机制：用户相关文档反馈机制和伪相关文档反馈机制。用户相关文档反馈机制要求用户从初始查询结果中标注反馈若干个相关文档,用这些文档建立信息需求域。伪相关文档反馈机制从初始检索结果中自动选取前n个(top n)文档,用这n个文档建立需求域。此方法的优点是自动化,无需用户参与,缺点是由于是伪相关文档反馈的结果,所反馈的文档不一定都是用户所需要的文档,因此,所得到的信息需求域是用户需求域的近似域。
论文在需求域基础上,分析了文档相似度的计算方法,建立了需求域基础上的相似度计算模型。论文在通用的TREC测试集上通过一系列实验对所建立的模型进行了模型训练和分析,并进一步进行了一系列检索性能对比实验,以验证其有效性。在检索性能对比实验中,将所建立的需求域基础上的相似度计算模型与三种经典的模型(伪相关反馈语言模型Mixfb_kl_dir、伪相关反馈tf_idf模型Fb_tf_idf以及伪相关反馈概率模型Fb_okapi)进行了对比,对比实验结果显示,需求域基础上的相似度计算模型的检索性能得到了提高,表明所建立的模型是有效的,结果令人满意。
与传统的方法相比较,传统的方法往往试图建立信息需求的一种准确的描述,而本文则是为信息需求建立一种较为松散的描述,使用需求域的方法去界定信息需求的一个范围。概况起来,论文研究的主要创新点为：
(1)提出了用户信息需求域的概念,给出了确定信息需求域的方法；
(2)提出了一种基于粗糙集的信息需求域的数学模型；
(3)提出了一种基于信息需求域模型基础上的相似度计算模型。
总之,论文研究的主要意义在于充实了信息需求的理论基础,并在此基础上建立了相应的相似度计算模型,提高了信息检索性能。从而为信息检索领域提供新的研究思路,充实新的理论和方法,并在实际应用中提高信息检索效率。
Information retrieval as a means of access to information is an important part of information processing and is the focus research area of information processing. Information retrieval requires three important aspects:specification of an information need, document description and retrieval model. Among them, the specification of information need is an important part of information retrieval. It is possible to produce good search results only when the information need is properly understood and expressed. At present, information retrieval is essentially implemented as a key words matching process, and user's query is assumed to be an accurate description of the user's information need. In reality, a user's query often cannot describe the underlying information need precisely. This unavoidably leads to unsatisfactory retrieval results.
In order to improve the description of the query, relevance feedback is commonly used. This process tries to determine a set of related terms from the (pseudo-) relevant documents to enhance the user's original query. The experiments have shown that the process is effective. However, we observe that terms is usually performed using heuristics. It is noted that the selection of these words is a heuristic idea, and usually assume that the user's information need is an accurate description. Relevance feedback method attempts to use feedback to seek the accurate description of the user need. Indeed, it is usually impossible to arrive at the accurate description of the information need. The expanded query is only our best guess of the information need, which is still inaccurate.
In this dissertation, we take a different approach. We assume that an information need is a semantic range. At the beginning, the only description of information need is the original query. When we get some feedback documents (user relevance feedback or pseudo-relevance feedback), we can build a better description of the information need, but this description is not trying to establish an accurate description of the information need, but frame a range of the information need. The feedback information can provide us with a lower bound R and an upper bound R:The lower bound corresponds to what a relevant document should contain (e.g. common terms shared by all the relevant documents), and the upper bound corresponds to what a relevant document may contain (e.g. all terms that appear in the relevant documents). The information need can be bounded with the domain Ⅰ=(R,R). The lower bound and upper bound of information need are derived in the dissertation, two boundaries of the domain are gotten, and the information need domain model Ⅰ=(R, R) is established.The information need domain has the following characteristics.
(1) The lower bound of the information need domain expresses the core of information need that the user focuses on.
(2) The upper bound of the information need domain contains the extended and extensive contents of information need, represents the breadth of the information need.
(3) The information need domain loosely frames a range of user's information need.
The dissertation uses two mechanisms to establish the information need domain:user true relevant document feedback and pseudo relevant document feedback. In the former case, a set of relevant documents identified by the user will be used to derive a description of R and R. In the second case, the top n documents from the initial retrieval results are assumed to be relevant. This method has the advantage to be automatic, but may include irrelevant feedback documents. So the resulting information need domain is only the approximation.
Based on the information need domain, the dissertation analyses the document similarity calculation method and establishes a similarity model. The dissertation trains and analyzes the model through a series of experiments on standard TREC test corpora. The new similarity model based on the information need domain is compared with three classic models---pseudo-relevance feedback language model:Mixfb_kl_dir, pseudo-relevance feedback tf_idf model:Fb_tf_idf and pseudo-relevance feedback probability model:Fb_okapi. The experimental results show that the retrieval performances of the similarity model based on the information need domain are improved.
Compared with traditional methods, traditional methods often attempt to establish an accurate description of the information need. We establish a loose description of the information need and using a domain to frame a range for the information need. In summary, the main contributions of the research work are:
(1)We propose the concept of the information need domain for IR and provide the method to determine the information need of domain.
(2)We propose the mathematical model of the information need domain based on fuzzy set.
(3)We propose the similarity model based on the information need domain.
The main significance of the research work is to establish and improve the theoretical basis of information need, and on this basis, to establish appropriate similarity model and to improve information retrieval performance. The information need domain provides a new research idea, enriches new theories and methods for the field of information retrieval, and improves information retrieval performance in practical applications.
为了更好地描述和表达用户的信息需求,通常使用相关反馈的方法。相关反馈(包括用户相关反馈和伪相关反馈)方法试图从反馈的文档中寻找一组关联词项以增强用户的初始查询请求。实验显示,这种处理方法有一定的效果。然而,注意到这些词项的选取只是一种启发式的想法,通常假定用户的信息需求具有一个准确的描述。相关反馈方法试图利用反馈信息寻求用户需求的准确描述,但实际上用户需求的准确描述是难以得到的。这种利用相关反馈的方法进行的查询扩展只是对用户信息需求的一种猜想,并不准确。
在本文中,我们采取了不同的方法。我们假定需求是一个语义范围。一开始,用户使用初始查询提出查询请求,当我们获得了一些反馈信息后(用户相关反馈或伪相关反馈),我们就能够建立一种对信息需求更好的描述,但这种描述不是试图去建立对信息需求的准确描述,而是概括性地去框定一个需求的范围。我们使用反馈信息建立需求的下界R和上界R,从而界定需求的一个范围。下界对应反馈文档的共有部分,上界对应反馈文档的全部内容。论文导出了需求的下界和上界,得到了需求域的两个边界,从而建立了需求域模型I=(R,R)。信息需求域具有以下特点：
(1)信息需求域的下界表达了信息需求集中关注的内容,代表了信息需求的精度,也代表了信息需求的内涵；
(2)信息需求域的上界包含了信息需求的延伸和扩展的内容,代表了信息需求的广度,也代表了信息需求的外延；
(3)信息需求域较为松散地界定了用户信息需求的一个范围。
论文中给出了建立需求域的两种机制：用户相关文档反馈机制和伪相关文档反馈机制。用户相关文档反馈机制要求用户从初始查询结果中标注反馈若干个相关文档,用这些文档建立信息需求域。伪相关文档反馈机制从初始检索结果中自动选取前n个(top n)文档,用这n个文档建立需求域。此方法的优点是自动化,无需用户参与,缺点是由于是伪相关文档反馈的结果,所反馈的文档不一定都是用户所需要的文档,因此,所得到的信息需求域是用户需求域的近似域。
论文在需求域基础上,分析了文档相似度的计算方法,建立了需求域基础上的相似度计算模型。论文在通用的TREC测试集上通过一系列实验对所建立的模型进行了模型训练和分析,并进一步进行了一系列检索性能对比实验,以验证其有效性。在检索性能对比实验中,将所建立的需求域基础上的相似度计算模型与三种经典的模型(伪相关反馈语言模型Mixfb_kl_dir、伪相关反馈tf_idf模型Fb_tf_idf以及伪相关反馈概率模型Fb_okapi)进行了对比,对比实验结果显示,需求域基础上的相似度计算模型的检索性能得到了提高,表明所建立的模型是有效的,结果令人满意。
与传统的方法相比较,传统的方法往往试图建立信息需求的一种准确的描述,而本文则是为信息需求建立一种较为松散的描述,使用需求域的方法去界定信息需求的一个范围。概况起来,论文研究的主要创新点为：
(1)提出了用户信息需求域的概念,给出了确定信息需求域的方法；
(2)提出了一种基于粗糙集的信息需求域的数学模型；
(3)提出了一种基于信息需求域模型基础上的相似度计算模型。
总之,论文研究的主要意义在于充实了信息需求的理论基础,并在此基础上建立了相应的相似度计算模型,提高了信息检索性能。从而为信息检索领域提供新的研究思路,充实新的理论和方法,并在实际应用中提高信息检索效率。
Information retrieval as a means of access to information is an important part of information processing and is the focus research area of information processing. Information retrieval requires three important aspects:specification of an information need, document description and retrieval model. Among them, the specification of information need is an important part of information retrieval. It is possible to produce good search results only when the information need is properly understood and expressed. At present, information retrieval is essentially implemented as a key words matching process, and user's query is assumed to be an accurate description of the user's information need. In reality, a user's query often cannot describe the underlying information need precisely. This unavoidably leads to unsatisfactory retrieval results.
In order to improve the description of the query, relevance feedback is commonly used. This process tries to determine a set of related terms from the (pseudo-) relevant documents to enhance the user's original query. The experiments have shown that the process is effective. However, we observe that terms is usually performed using heuristics. It is noted that the selection of these words is a heuristic idea, and usually assume that the user's information need is an accurate description. Relevance feedback method attempts to use feedback to seek the accurate description of the user need. Indeed, it is usually impossible to arrive at the accurate description of the information need. The expanded query is only our best guess of the information need, which is still inaccurate.
In this dissertation, we take a different approach. We assume that an information need is a semantic range. At the beginning, the only description of information need is the original query. When we get some feedback documents (user relevance feedback or pseudo-relevance feedback), we can build a better description of the information need, but this description is not trying to establish an accurate description of the information need, but frame a range of the information need. The feedback information can provide us with a lower bound R and an upper bound R:The lower bound corresponds to what a relevant document should contain (e.g. common terms shared by all the relevant documents), and the upper bound corresponds to what a relevant document may contain (e.g. all terms that appear in the relevant documents). The information need can be bounded with the domain Ⅰ=(R,R). The lower bound and upper bound of information need are derived in the dissertation, two boundaries of the domain are gotten, and the information need domain model Ⅰ=(R, R) is established.The information need domain has the following characteristics.
(1) The lower bound of the information need domain expresses the core of information need that the user focuses on.
(2) The upper bound of the information need domain contains the extended and extensive contents of information need, represents the breadth of the information need.
(3) The information need domain loosely frames a range of user's information need.
The dissertation uses two mechanisms to establish the information need domain:user true relevant document feedback and pseudo relevant document feedback. In the former case, a set of relevant documents identified by the user will be used to derive a description of R and R. In the second case, the top n documents from the initial retrieval results are assumed to be relevant. This method has the advantage to be automatic, but may include irrelevant feedback documents. So the resulting information need domain is only the approximation.
Based on the information need domain, the dissertation analyses the document similarity calculation method and establishes a similarity model. The dissertation trains and analyzes the model through a series of experiments on standard TREC test corpora. The new similarity model based on the information need domain is compared with three classic models---pseudo-relevance feedback language model:Mixfb_kl_dir, pseudo-relevance feedback tf_idf model:Fb_tf_idf and pseudo-relevance feedback probability model:Fb_okapi. The experimental results show that the retrieval performances of the similarity model based on the information need domain are improved.
Compared with traditional methods, traditional methods often attempt to establish an accurate description of the information need. We establish a loose description of the information need and using a domain to frame a range for the information need. In summary, the main contributions of the research work are:
(1)We propose the concept of the information need domain for IR and provide the method to determine the information need of domain.
(2)We propose the mathematical model of the information need domain based on fuzzy set.
(3)We propose the similarity model based on the information need domain.
The main significance of the research work is to establish and improve the theoretical basis of information need, and on this basis, to establish appropriate similarity model and to improve information retrieval performance. The information need domain provides a new research idea, enriches new theories and methods for the field of information retrieval, and improves information retrieval performance in practical applications.
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