Orbit Weighting Scheme in the Context of Vector Space Information Retrieval

This study introduces the Orbit Weighting Scheme(OWS),a novel approach aimed at enhancing the precision and efficiency of Vector Space information retrieval(IR)models,which have traditionally relied on weighting schemes like tf-idf and BM25.These conventional methods often struggle with accurately capturing document relevance,leading to inefficiencies in both retrieval performance and index size management.OWS proposes a dynamic weighting mechanism that evaluates the significance of terms based on their orbital position within the vector space,emphasizing term relationships and distribution patterns overlooked by existing models.Our research focuses on evaluating OWS’s impact on model accuracy using Information Retrieval metrics like Recall,Precision,InterpolatedAverage Precision(IAP),andMeanAverage Precision(MAP).Additionally,we assessOWS’s effectiveness in reducing the inverted index size,crucial for model efficiency.We compare OWS-based retrieval models against others using different schemes,including tf-idf variations and BM25Delta.Results reveal OWS’s superiority,achieving a 54%Recall and 81%MAP,and a notable 38%reduction in the inverted index size.This highlights OWS’s potential in optimizing retrieval processes and underscores the need for further research in this underrepresented area to fully leverage OWS’s capabilities in information retrieval methodologies.

Region-Aware Fashion Contrastive Learning for Unified Attribute Recognition and Composed Retrieval

Clothing attribute recognition has become an essential technology,which enables users to automatically identify the characteristics of clothes and search for clothing images with similar attributes.However,existing methods cannot recognize newly added attributes and may fail to capture region-level visual features.To address the aforementioned issues,a region-aware fashion contrastive language-image pre-training(RaF-CLIP)model was proposed.This model aligned cropped and segmented images with category and multiple fine-grained attribute texts,achieving the matching of fashion region and corresponding texts through contrastive learning.Clothing retrieval found suitable clothing based on the user-specified clothing categories and attributes,and to further improve the accuracy of retrieval,an attribute-guided composed network(AGCN)as an additional component on RaF-CLIP was introduced,specifically designed for composed image retrieval.This task aimed to modify the reference image based on textual expressions to retrieve the expected target.By adopting a transformer-based bidirectional attention and gating mechanism,it realized the fusion and selection of image features and attribute text features.Experimental results show that the proposed model achieves a mean precision of 0.6633 for attribute recognition tasks and a recall@10(recall@k is defined as the percentage of correct samples appearing in the top k retrieval results)of 39.18 for composed image retrieval task,satisfying user needs for freely searching for clothing through images and texts.

Image Retrieval with Text Manipulation by Local Feature Modification

The demand for image retrieval with text manipulation exists in many fields, such as e-commerce and Internet search. Deep metric learning methods are used by most researchers to calculate the similarity between the query and the candidate image by fusing the global feature of the query image and the text feature. However, the text usually corresponds to the local feature of the query image rather than the global feature. Therefore, in this paper, we propose a framework of image retrieval with text manipulation by local feature modification(LFM-IR) which can focus on the related image regions and attributes and perform modification. A spatial attention module and a channel attention module are designed to realize the semantic mapping between image and text. We achieve excellent performance on three benchmark datasets, namely Color-Shape-Size(CSS), Massachusetts Institute of Technology(MIT) States and Fashion200K(+8.3%, +0.7% and +4.6% in R@1).

A Full Text Retrieval System in a Digital Library Environment

The volume of information being created, generated and stored is huge. Without adequate knowledge of Information Retrieval (IR) methods, the retrieval process for information would be cumbersome and frustrating. Studies have further revealed that IR methods are essential in information centres (for example, Digital Library environment) for storage and retrieval of information. Therefore, with more than one billion people accessing the Internet, and millions of queries being issued on a daily basis, modern Web search engines are facing a problem of daunting scale. The main problem associated with the existing search engines is how to avoid irrelevant information retrieval and to retrieve the relevant ones. In this study, the existing system of library retrieval was studied. Problems associated with them were analyzed in order to address this problem. The concept of existing information retrieval models was studied, and the knowledge gained was used to design a digital library information retrieval system. It was successfully implemented using a real life data. The need for a continuous evaluation of the IR methods for effective and efficient full text retrieval system was recommended.

Text retrieval algorithm that decreases confusion

To overcome the problem that the confusion between texts limits the precision in text re- trieval, a new text retrieval algorithm that decrease confusion （DCTR） is proposed. The algorithm constructs the searching template to represent the user＇ s searching intention through positive and negative training. By using the prior probabilities in the template, the supported probability and anti- supported probability of each text in the text library can be estimated for discrimination. The search- ing result can be ranked according to similarities between retrieved texts and the template. The com- plexity of DCTR is close to term frequency and mversed document frequency （TF-IDF）. Its distin- guishing ability to confusable texts could be advanced and the performance of the result would be im- proved with increasing of training times.

CLIP4Video-Sampling: Global Semantics-Guided Multi-Granularity Frame Sampling for Video-Text Retrieval

Video-text retrieval (VTR) is an essential task in multimodal learning, aiming to bridge the semantic gap between visual and textual data. Effective video frame sampling plays a crucial role in improving retrieval performance, as it determines the quality of the visual content representation. Traditional sampling methods, such as uniform sampling and optical flow-based techniques, often fail to capture the full semantic range of videos, leading to redundancy and inefficiencies. In this work, we propose CLIP4Video-Sampling: Global Semantics-Guided Multi-Granularity Frame Sampling for Video-Text Retrieval, a global semantics-guided multi-granularity frame sampling strategy designed to optimize both computational efficiency and retrieval accuracy. By integrating multi-scale global and local temporal sampling and leveraging the CLIP (Contrastive Language-Image Pre-training) model’s powerful feature extraction capabilities, our method significantly outperforms existing approaches in both zero-shot and fine-tuned video-text retrieval tasks on popular datasets. CLIP4Video-Sampling reduces redundancy, ensures keyframe coverage, and serves as an adaptable pre-processing module for multimodal models.

Ontology-based similarity measure for text clustering

A method that combines category-based and keyword-based concepts for a better information retrieval system is introduced. To improve document clustering, a document similarity measure based on cosine vector and keywords frequency in documents is proposed, but also with an input ontology. The ontology is domain specific and includes a list of keywords organized by degree of importance to the categories of the ontology, and by means of semantic knowledge, the ontology can improve the effects of document similarity measure and feedback of information retrieval systems. Two approaches to evaluating the performance of this similarity measure and the comparison with standard cosine vector similarity measure are also described.

基于Oracle Text电子政务全文检索技术的应用

全文检索技术是智能信息管理的关键技术之一,Oracle Text作为Oracle的一个组件,提供了强大的全文检索功能,用Oracle做后台数据库,就可以充分利用其全文检索技术,构建复杂的大型文档管理系统。本文主要介绍了Oracle Text的体系结构及其在电子政务系统中的应用与实现,讨论了采用Oracle Text为组件进行电子政务全文检索应用系统的设计思想,并着重讨论了Oracle Text体系架构,在Oracle Text上如何实现全文检索做了某些研究,结合电子政务典型业务流程实例进行了具体实践的描述,对以后电子政务全文检索开发设计有一定的现实意义。

DI-VTR:Dual inter-modal interaction model for video-text retrieval

Video-text retrieval is a challenging task for multimodal information processing due to the semantic gap between different modalities.However,most existing methods do not fully mine the intra-modal interactions,as with the temporal correlation of video frames,which results in poor matching performance.Additionally,the imbalanced semantic information between videos and texts also leads to difficulty in the alignment of the two modalities.To this end,we propose a dual inter-modal interaction network for video-text retrieval,i.e.,DI-vTR.To learn the intra-modal interaction of video frames,we design a contextual-related video encoder to obtain more fine-grained content-oriented video representations.We also propose a dual inter-modal interaction module to accomplish accurate multilingual alignment between the video and text modalities by introducing multilingual text to improve the representation ability of text semantic features.Extensive experimental results on commonly-used video-text retrieval datasets,including MSR-VTT,MSVD and VATEX,show that the proposed method achieves significantly improved performance compared with state-of-the-art methods.

Automatic User Goals Identification Based on Anchor Text and Click-Through Data

Understanding the underlying goal behind a user＇s Web query has been proved to be helpful to improve the quality of search. This paper focuses on the problem of automatic identification of query types according to the goals. Four novel entropy-based features extracted from anchor data and click-through data are proposed, and a support vector machines （SVM） classifier is used to identify the user＇s goal based on these features. Experi- mental results show that the proposed entropy-based features are more effective than those reported in previous work. By combin- ing multiple features the goals for more than 97% of the queries studied can be correctly identified. Besides these, this paper reaches the following important conclusions： First, anchor-based features are more effective than click-through-based features; Second, the number of sites is more reliable than the number of links; Third, click-distribution- based features are more effective than session-based ones.

A Text Categorization System with Soft Real-Time Guarantee

In order to provide predictable runtime performante for text categorization （TC） systems, an innovative system design method is proposed for soft real time TC systems. An analyzable mathematical model is established to approximately describe the nonlinear and time-varying TC systems. According to this mathematical model, the feedback control theory is adopted to prove the system＇s stableness and zero steady state error. The experiments result shows that the error of deadline satisfied ratio in the system is kept within 4 of the desired value. And the number of classifiers can be dynamically adjusted by the system itself to save the computa tion resources. The proposed methodology enables the theo retical analysis and evaluation to the TC systems, leading to a high-quality and low cost implementation approach.

Text Rank for Domain Specific Using Field Association Words

Text Rank is a popular tool for obtaining words or phrases that are important for many Natural Language Processing (NLP) tasks. This paper presents a practical approach for Text Rank domain specific using Field Association (FA) words. We present the keyphrase separation technique not for a single document, although for a particular domain. The former builds a specific domain field. The second collects a list of ideal FA terms and compounds FA terms from the specific domain that are considered to be contender keyword phrases. Therefore, we combine two-word node weights and field tree relationships into a new approach to generate keyphrases from a particular domain. Studies using the changed approach to extract key phrases demonstrate that the latest techniques including FA terms are stronger than the others that use normal words and its precise words reach 90%.

E-textbook全文检索

基于电子教材的特殊应用的需求,在传统的web页面全文检索技术基础上,设计了电子教材的全文检索系统.它包含教材文档处理模块、索引服务模块和检索服务模块.根据电子教材的结构需求,定义了索引文件数据结构、文本文件数据结构、索引条目数据结构及结果排序的分数模型.通过系统的实现,为电子教材学习者提供了快速准确的检索服务,提高学习者学习效率.

A Hybrid Algorithm for Stemming of Nepali Text

In this paper, a new context free stemmer is proposed which consists of the combination of traditional rule based system with string similarity approach. This algorithm can be called as hybrid algorithm. It is language dependent algorithm. Context free stemmer means that stemmer which stems the word that is not based on the context i.e., for every context such rule is applied. After stripping the words using traditional context free rule based approach, it may over stem or under stem the inflected words which are overcome by applying string similarity function of dynamic programming. For measuring the string similarity function, edit distance is used. The stripped inflected word is compared with the words stored in a text database available. That word having minimum distance is taken as the substitution of the stripped inflected word which leads to the stem of it. The concept of traditional rule based system and corpus based approach is heavily used in this approach. This algorithm is tested for Nepali Language which is based on Devanagari Script. The approach has given better result in comparison to traditional rule based system particularly for Nepali Language only. The total accuracy of this hybrid algorithm is 70.10% whereas the total accuracy of traditional rule based system is 68.43%.

Contextual Text Mining Framework for Unstructured Textual Judicial Corpora through Ontologies

Digitalization has changed the way of information processing, and newtechniques of legal data processing are evolving. Text mining helps to analyze andsearch different court cases available in the form of digital text documents toextract case reasoning and related data. This sort of case processing helps professionals and researchers to refer the previous case with more accuracy in reducedtime. The rapid development of judicial ontologies seems to deliver interestingproblem solving to legal knowledge formalization. Mining context informationthrough ontologies from corpora is a challenging and interesting field. Thisresearch paper presents a three tier contextual text mining framework throughontologies for judicial corpora. This framework comprises on the judicial corpus,text mining processing resources and ontologies for mining contextual text fromcorpora to make text and data mining more reliable and fast. A top-down ontologyconstruction approach has been adopted in this paper. The judicial corpus hasbeen selected with a sufficient dataset to process and evaluate the results.The experimental results and evaluations show significant improvements incomparison with the available techniques.

基于融合矩阵的文本相似度计算实现检索结果聚类

目的/意义弥补医学文本语义表示方面的不足,实现PubMed数据库检索结果聚类。方法/过程采用Jaccard系数和TF-IDF构建融合矩阵方法,建立短语间、文档间、短语与文档内容间的相似性关系融合矩阵,训练聚类算法,将PubMed数据库检索结果集合分组,随后生成类别标签,描述每一类簇文档的含义。结果/结论基于融合矩阵的聚类效果较好,提取出描述类别的高频词能很好地区分类别含义,对检索结果文本聚类任务有效。

数字化转型、分析师关注与企业创新绩效

企业与数字技术深度融合是实体经济发展的重要趋势。根据2008—2020年A股制造业微观企业数据,基于分析师关注视角研究企业数字化转型对创新绩效的作用及内在机理。结果显示:企业数字化进程明显促进了创新绩效的改善,且在一系列稳健性检验后该结论仍然成立;机制检验表明,数字化转型可以通过提高分析师关注度和降低分析师信息搜寻成本,进而改善企业创新绩效表现;异质性分析发现,微观层面,董事、监事等高管拥有海外背景的企业,数字化转型更有助于提高企业创新绩效水平;中观层面,供应链集中度较高和劳动密集型行业中的企业,数字化转型对企业创新绩效的激励效果会更弱;宏观层面,数字化转型对于处在环境不确定性较低和东部地区的企业,更有利于改善创新绩效。基于分析师关注视角研究有效拓展了数字化转型与企业创新的相关研究,为政府通过完善金融市场中介来推动创新驱动发展战略和现代化产业体系建设提供了经验证据。

文本语义哈希技术研究进展

文本语义哈希是在满足语义相似性约束下将文本转化为低维二值数据的神经编码技术,支持基于汉明距离的高效检索,以解决有限计算资源约束下海量文本的相似性计算问题。文本语义哈希技术存在诸多挑战,包括如何在低维二值编码中融入类别信息、如何丰富编码的语义信息以提升模型鲁棒性、如何解决离散输出的模型梯度估计等关键问题。文章首先综述文本语义哈希任务的重要研究发展,详细讨论了无监督文本语义哈希模型和融合类别信息的有监督文本语义哈希模型的技术细节,分析基于近邻文本、隐式主题等信息的语义增强技术以及模型优化等关键技术;然后,综述文本语义哈希任务相关数据集和评估指标,对比了各类文本语义哈希技术的特点和性能;最后,讨论了文本语义哈希技术的未来发展方向。

一种基于RoBERTa模型的文本搜索排序方法

针对日益增长的资料快速检索共享需求,利用鲁棒性优化的BERT方法(Robustly optimized BERT approach,RoBERTa)预训练模型对现有资料进行训练,基于Transformer自注意力机制的语言学习模型,生成文本嵌入向量,将文本向量作为全文本的上下文表征。通过将关键搜索词向量化,使用欧氏距离计算向量与其他向量之间的距离,并使用快速排序算法,以找到最相似的向量输出显示,解决基于内容和上下文语义搜索的应用需求。

面向业务的资源按需解析模型构建研究

针对在项目开发过程中新需求来临时,需要对自然语言处理工具和资源解析插件进行重新需求分析、重复开发等问题,提出了一套面向业务的资源按需解析方案。首先,提出了一种从需求到代码的资源按需解析方法,针对需求文本本身进行需求概念标引模型的构建。构建的需求概念标引模型的准确率、召回率、F1值等指标均高于其他分类模型。然后,针对需求文本与代码的关联,建立从需求文本到代码库类别的映射机制。对于模型的映射结果,使用前K准确率(percision@K)作为评价指标,最终准确率达到60%,具有一定的实用价值。综上所述,探索了一套具有需求解析能力、实现需求与代码关联的资源按需解析关键技术,并贯穿需求文本分类、需求代码库分类、代码库检索到插件生成的整个流程,形成了完整的“需求-代码-插件-解析”的业务闭环,通过实验验证了所提方法对于资源按需解析的有效性,为业务需求分析与软件复用提供了思路,与现有用于业务需求的解析和代码生成的大语言模型相比,所提方法聚焦于具体业务领域内的含有业务特点的插件代码复用全流程的实现。