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.

Unlocking the Potential:A Comprehensive Systematic Review of ChatGPT in Natural Language Processing Tasks

As Natural Language Processing(NLP)continues to advance,driven by the emergence of sophisticated large language models such as ChatGPT,there has been a notable growth in research activity.This rapid uptake reflects increasing interest in the field and induces critical inquiries into ChatGPT’s applicability in the NLP domain.This review paper systematically investigates the role of ChatGPT in diverse NLP tasks,including information extraction,Name Entity Recognition(NER),event extraction,relation extraction,Part of Speech(PoS)tagging,text classification,sentiment analysis,emotion recognition and text annotation.The novelty of this work lies in its comprehensive analysis of the existing literature,addressing a critical gap in understanding ChatGPT’s adaptability,limitations,and optimal application.In this paper,we employed a systematic stepwise approach following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)framework to direct our search process and seek relevant studies.Our review reveals ChatGPT’s significant potential in enhancing various NLP tasks.Its adaptability in information extraction tasks,sentiment analysis,and text classification showcases its ability to comprehend diverse contexts and extract meaningful details.Additionally,ChatGPT’s flexibility in annotation tasks reducesmanual efforts and accelerates the annotation process,making it a valuable asset in NLP development and research.Furthermore,GPT-4 and prompt engineering emerge as a complementary mechanism,empowering users to guide the model and enhance overall accuracy.Despite its promising potential,challenges persist.The performance of ChatGP Tneeds tobe testedusingmore extensivedatasets anddiversedata structures.Subsequently,its limitations in handling domain-specific language and the need for fine-tuning in specific applications highlight the importance of further investigations to address these issues.

Natural Language Processing with Optimal Deep Learning-Enabled Intelligent Image Captioning System

The recent developments in Multimedia Internet of Things(MIoT)devices,empowered with Natural Language Processing(NLP)model,seem to be a promising future of smart devices.It plays an important role in industrial models such as speech understanding,emotion detection,home automation,and so on.If an image needs to be captioned,then the objects in that image,its actions and connections,and any silent feature that remains under-projected or missing from the images should be identified.The aim of the image captioning process is to generate a caption for image.In next step,the image should be provided with one of the most significant and detailed descriptions that is syntactically as well as semantically correct.In this scenario,computer vision model is used to identify the objects and NLP approaches are followed to describe the image.The current study develops aNatural Language Processing with Optimal Deep Learning Enabled Intelligent Image Captioning System(NLPODL-IICS).The aim of the presented NLPODL-IICS model is to produce a proper description for input image.To attain this,the proposed NLPODL-IICS follows two stages such as encoding and decoding processes.Initially,at the encoding side,the proposed NLPODL-IICS model makes use of Hunger Games Search(HGS)with Neural Search Architecture Network(NASNet)model.This model represents the input data appropriately by inserting it into a predefined length vector.Besides,during decoding phase,Chimp Optimization Algorithm(COA)with deeper Long Short Term Memory(LSTM)approach is followed to concatenate the description sentences 4436 CMC,2023,vol.74,no.2 produced by the method.The application of HGS and COA algorithms helps in accomplishing proper parameter tuning for NASNet and LSTM models respectively.The proposed NLPODL-IICS model was experimentally validated with the help of two benchmark datasets.Awidespread comparative analysis confirmed the superior performance of NLPODL-IICS model over other models.

New Retrieval Method Based on Relative Entropy for LanguageModeling with Different Smoothing Methods

A language model for information retrieval is built by using a query language model to generate queries and a document language model to generate documents. The documents are ranked according to the relative entropies of estimated document language models with respect to the estimated query language model. Two popular and relatively efficient smoothing methods, the Jelinek- Mercer method and the absolute discounting method, are used to smooth the document language model in estimation of the document language, A combined model composed of the feedback document language model and the collection language model is used to estimate the query model. A performacne comparison between the new retrieval method and the existing method with feedback is made, and the retrieval performances of the proposed method with the two different smoothing techniques are evaluated on three Text Retrieval Conference （TREC） data sets. Experimental results show that the method is effective and performs better than the basic language modeling approach; moreover, the method using the Jelinek-Mercer technique performs better than that using the absolute discounting technique, and the perfomance is sensitive to the smoothing peramters.

Research on Cross-Language Retrieval Using Bilingual Word Vectors in Different Languages

Bilingual word vectors have been exploited a lot in cross-language information retrieval research. However, most of the research is currently focused on similar language pairs. There are very few studies exploring the impact of using bilingual word vectors for cross-language information retrieval in long-distance language pairs. In this paper, it systematically analyzes the retrieval performance of various European languages (English, German, Italian, French, Finnish, Dutch) as well as Asian languages (Chinese, Japanese) in the adhoc task of CLEF 2002–2003 campaign. Genetic proximity was used to visually represent the relationships between languages and compare their crosslingual retrieval performance in various settings. The results show that the differences in language vocabulary would dramatically affect the retrieval performance. At the same time, the term by term translation retrieval method performs slightly better than the simple vector addition retrieval methods. It proves that the translation-based retrieval model can still maintain its advantage under the new semantic scheme.

基于Language Model的地理信息检索模型(英文)

区别于传统的信息检索,地理信息检索通过一个查询范围词来限制用户的兴趣区域.目前的技术一般是把该查询范围词作为一个过滤器,将在该范围之外的文档排除在查询结果外.但是,词在地理空间的频率分布并不是均匀的,因此词在排序结果中的重要性应该随着查询范围的变化而有所改变.为此,提出了一种新的基于语言模型的地理信息查询模型,把查询范围引入到传统的语言模型中.在该模型中,引入了一个local model来描述查询词的地理分布特性.实验结果表明,新的检索模型优于TF-IDF与传统的语言模型.

Definitions of Natural-Language Spatial Relations: Combining Topology and Directions

Because SQL for querying data from spatial databa se s is ineffective, the query based on natural or visual language becomes an attra ctive research field gradually. However, how to define and represent natural lan guages related to spatial data are still gigantic problems. Because existing mod els of direction relations can’t describe by use of some common concepts. First of all, detailed direction relations are proposed to describe the directions re lated to the interior of spatial objects, such as "east part of a region","ea st boundary of a region", and so on. Secondly, by integrating the detailed dire ctions with exterior direction relations and topological relations, several NLSR s are defined, such as "a road goes across the east part of a lake", "a river goes along the east boundary of a province", etc. Finally, based on the NLSRs abovementioned, a natural spatial query language (NSQL) is formed to retrieve da ta from spatial databases.

Artificial Fish Swarm Optimization with Deep Learning Enabled Opinion Mining Approach

Sentiment analysis or opinion mining(OM)concepts become familiar due to advances in networking technologies and social media.Recently,massive amount of text has been generated over Internet daily which makes the pattern recognition and decision making process difficult.Since OM find useful in business sectors to improve the quality of the product as well as services,machine learning(ML)and deep learning(DL)models can be considered into account.Besides,the hyperparameters involved in the DL models necessitate proper adjustment process to boost the classification process.Therefore,in this paper,a new Artificial Fish Swarm Optimization with Bidirectional Long Short Term Memory(AFSO-BLSTM)model has been developed for OM process.The major intention of the AFSO-BLSTM model is to effectively mine the opinions present in the textual data.In addition,the AFSO-BLSTM model undergoes pre-processing and TF-IFD based feature extraction process.Besides,BLSTM model is employed for the effectual detection and classification of opinions.Finally,the AFSO algorithm is utilized for effective hyperparameter adjustment process of the BLSTM model,shows the novelty of the work.A complete simulation study of the AFSO-BLSTM model is validated using benchmark dataset and the obtained experimental values revealed the high potential of the AFSO-BLSTM model on mining opinions.

Information Extraction Based on Multi-turn Question Answering for Analyzing Korean Research Trends

Analyzing Research and Development(R&D)trends is important because it can influence future decisions regarding R&D direction.In typical trend analysis,topic or technology taxonomies are employed to compute the popularities of the topics or codes over time.Although it is simple and effective,the taxonomies are difficult to manage because new technologies are introduced rapidly.Therefore,recent studies exploit deep learning to extract pre-defined targets such as problems and solutions.Based on the recent advances in question answering(QA)using deep learning,we adopt a multi-turn QA model to extract problems and solutions from Korean R&D reports.With the previous research,we use the reports directly and analyze the difficulties in handling them using QA style on Information Extraction(IE)for sentence-level benchmark dataset.After investigating the characteristics of Korean R&D,we propose a model to deal with multiple and repeated appearances of targets in the reports.Accordingly,we propose a model that includes an algorithm with two novel modules and a prompt.A newly proposed methodology focuses on reformulating a question without a static template or pre-defined knowledge.We show the effectiveness of the proposed model using a Korean R&D report dataset that we constructed and presented an in-depth analysis of the benefits of the multi-turn QA model.

Application of graph neural network and feature information enhancement in relation inference of sparse knowledge graph

At present,knowledge embedding methods are widely used in the field of knowledge graph(KG)reasoning,and have been successfully applied to those with large entities and relationships.However,in research and production environments,there are a large number of KGs with a small number of entities and relations,which are called sparse KGs.Limited by the performance of knowledge extraction methods or some other reasons(some common-sense information does not appear in the natural corpus),the relation between entities is often incomplete.To solve this problem,a method of the graph neural network and information enhancement is proposed.The improved method increases the mean reciprocal rank(MRR)and Hit@3 by 1.6%and 1.7%,respectively,when the sparsity of the FB15K-237 dataset is 10%.When the sparsity is 50%,the evaluation indexes MRR and Hit@10 are increased by 0.8%and 1.8%,respectively.

辅助任务增强的中文跨域NL2SQL算法

自然语言到结构化查询语言(natural language to structured query language,NL2SQL)任务旨在将自然语言询问转化为数据库可执行的结构化查询语言(structured query language,SQL)语句。本文提出了一种辅助任务增强的中文跨域NL2SQL算法,其核心思想是通过在解码阶段添加辅助任务以结合原始模型来进行多任务训练,提升模型的准确率。辅助任务的设计是通过将数据库模式建模成图,预测自然语言询问与数据库模式图中的节点的依赖关系,显式地建模自然语言询问和数据库模式之间的依赖关系。针对特定的自然语言询问,通过辅助任务的提升,模型能够更好地识别数据库模式中哪些表/列对预测目标SQL更有效。在中文NL2SQL数据集DuSQL上的实验结果表明,添加辅助任务后的算法相对于原始模型取得了更好的效果,能够更好地处理跨域NL2SQL任务。

预训练大语言模型发展对中国数字创意产业的启示

预训练大语言模型与数字创意产业的结合,一方面可以促进预训练大语言模型技术研发和应用,推动自然语言处理相关产业发展,另一方面也可以为数字创意产业提供更高效、精准的解决方案,促进产业数字化转型升级。然而,目前中国预训练大语言模型在数字创意产业的运用主要侧重于文本识别生成和语音生成等领域。为此,通过阐述预训练大语言模型以及中国数字创意产业的发展现状,梳理预训练大语言模型在数字创意产业的应用范畴和商业布局,综合分析作为新质生产力引擎的预训练大语言模型在中国数字创意产业发展中的机遇与挑战,并为中国数字创意产业的发展提出建议。研究发现:融合发展是中国数字创意产业的重要趋势,网络文学、动漫游戏、短视频等细分产业开始发展出完整的产业链条;预训练大语言模型可提升数字创意产业的内容生成效率、丰富艺术创意、拓展数字娱乐形式,也可以加强社交媒体分析监测、提高跨语言应用的效率、辅助科研教育,带来提升数字创意产业的智能化水平、增强用户黏性、数字创意生产者身份多元化等机遇,但同时也面临数据成本、隐私安全、知识产权等问题。提出未来在预训练大语言模型应用于数字创意产业的发展中,重视构建相关监管评估框架和知识产权保护体系,提升多模态技术水平,强化智能算力体系建设,以推动数字创意产业的智能化发展。

基于自适应损失函数的句子级远程监督关系抽取

远程监督关系抽取是一种关系抽取方法,现有方法主要采用多实例学习,在具有相同实体对的样例包上进行关系抽取。但是,包级方法只能缓解却并不能完全解决错误标签问题。基于此,文中首先分析了干净数据和噪声数据的分布,提出了一种新的自适应损失函数;在此基础上,提出了一种基于自适应损失函数的句子级远程监督关系抽取方法。在公开数据集NYT-10以及基于TACRED的合成数据集上的实验结果表明:文中提出的方法优于对比文献中的方法,能够更有效地区分错误标签噪声样例和干净样例,提高了句子级远程监督关系抽取的准确率。

人工智能在外科学教育领域的应用前景

在高等教育中,人工智能和虚拟现实等前沿教育技术被广泛应用于开发虚拟学习资源。因此,人工智能(artificial intelligence,AI)在临床实践中的应用被认为是医学教育中一个很有前景的扩展领域。AI能够基于学习者的表现数据和个性化需求,定制教育路径和提供精准的学习建议。这种个性化的支持不仅增强了教育效果,还可以帮助医师快速地掌握复杂的临床技能和决策能力。AI的4个关键组成部分是机器学习、自然语言处理、人工神经网络和视觉处理,每个部分都在外科学教育中具有潜在的应用前景。在一个医患关系紧张、医学生源相对饱和及手术机会减少的时代,AI还能够分析大量的临床数据,预测患者的康复路径和可能的并发症,为医疗团队提供决策支持。通过优化资源利用和流程管理,AI还有助于降低医疗成本,提供更经济高效的医疗护理服务。文章阐述了目前AI技术的应用及其在促进外科学教育方面的前景。

诠释学视域下GPT语言模型的本质及特征

GPT语言模型的设计思路具有自然语言理解的诠释学思维特征,但本质上该模型并不具备诠释学语言理解的属人性基础;GPT语言模型将智能视为一种本体论层面以语言作为媒介的整体性系统“涌现”结果,缺乏诠释的“本体论—主体性”地位和“本体论—整体性”结构;GPT语言模型采用的生成式和预训练的设计思路凸显了诠释学理解和解释的“历史性”特征,而数据训练的强化和“思维链”的对话机制使该模型具有“效果历史”和“视域融合”的语言理解特征;GPT语言模型在模拟人类“偏见性”认知方面取得进步,但与人类的“偏见—个性化”和“偏见—创造性”能力相比存在根本差异;GPT语言模型形成了“诠释学循环”的语言对话机制,但其并不具有自身独立的“诠释学循环”实践基础,因此,难以达成诠释学意义上的语言理解共识。

语言智能:语言人工智能研究的历史新方位——“语言智能科学”理论与方法论构建(三)

寓身于人肉身之内的语言智能是语言自然智能,离身于人的肉身靠人造装置实现的语言智能属于语言人工智能。当今,致力于研究前者的典型学科是语言学,致力于研究后者的学科则有机器翻译、计算语言学、自然语言处理等。本文站在10~30年后的学科未来看历史与现今,透过学科指涉名称上的差异解析其背后的思想理路、主线脉络、方法重心、技术逻辑、历时承继与未来趋向,提出新的主张与判断:语言人工智能研究已经走过了思想乌托邦(前机器翻译)、泛机械(机器翻译)、语言学主导的符号主义(计算语言学)、计算机科学主导的连接主义(自然语言处理)四个历史方位,正在迎来第五个崭新的历史方位——“智能科学主导的机制主义”。新近提出的“语言智能”概念是这一历史方位恰当的代名词。

基于图神经网络的人工自然语言语义挖掘仿真

语义挖掘工具可从批量非结构化人工自然语言文本数据中准确提取有用信息,但是由于网络环境文本具备半结构化、多尺度、海量、复杂关联等属性,导致文本数据通常维度较高,且仅有小部分节点存在明确标签,因此语义挖掘难度较大。提出基于图神经网络的人工自然语言语义挖掘方法。结合多头注意力机制和半监督图卷积神经网络对人工自然语言文本降维处理。联合改进的模糊C均值聚类算法和免疫单亲遗传算法,构建人工自然语言语义挖掘算法。实验结果表明,研究方法的聚类纯度、准确率和召回率均高于95%,说明上述方法的应用性能较优。

挑战与前景:ChatGPT在图书馆智能客服系统中的效能和应用

智能客服系统在现代图书馆中扮演着重要角色,作为一种基于人工智能的自然语言处理模型,ChatGPT为图书馆智能客服系统的构建和质量提升提供了有力工具。文章旨在探索ChatGPT在图书馆智能客服中的应用场景效能,分析了ChatGPT在上下文理解、模糊查询处理、对抗攻击和多语言支持等方面的应用潜力和方向,并针对该领域面临的挑战提出了相应的解决方案,以为推动图书馆智能客服的创新和发展提供参考思路。