Designing a Document Retrieval Method for University Digital Libraries Based on Hadoop Technology

With the development of big data,all walks of life in society have begun to venture into big data to serve their own enterprises and departments.Big data has been embraced by university digital libraries.The most cumbersome work for the management of university libraries is document retrieval.This article uses Hadoop algorithm to extract semantic keywords and then calculates semantic similarity based on the literature retrieval keyword calculation process.The fast-matching method is used to determine the weight of each keyword,so as to ensure an efficient and accurate document retrieval in digital libraries,thus completing the design of the document retrieval method for university digital libraries based on Hadoop technology.

Artificial neural network-based merging score for Meta search engine

Several users use metasearch engines directly or indirectly to access and gather data from more than one data sources. The effectiveness of a metasearch engine is majorly determined by the quality of the results and it returns and in response to user queries. The rank aggregation methods which have been proposed until now exploits very limited set of parameters such as total number of used resources and the rankings they achieved from each individual resource. In this work, we use the neural network to merge the score computation module effectively. Initially, we give a query to different search engines and the top n list from each search engine is chosen for further processing our technique. We then merge the top n list based on unique links and we do some parameter calculations such as title based calculation, snippet based calculation, content based calculation, domain calculation, position calculation and co-occurrence calculation. We give the solutions of the calculations with user given ranking of links to the neural network to train the system. The system then rank and merge the links we obtain from different search engines for the query we give. Experimentation results reports a retrieval effectiveness of about 80%, precision of about 79% for user queries and about 72% for benchmark queries. The proposed technique also includes a response time of about 76 ms for 50 links and 144 ms for 100 links.

Text Reasoning Chain Extraction for Multi-Hop Question Answering

With the advent of the information age, it will be more troublesome to search for a lot of relevant knowledge to find the information you need. Text reasoning is a very basic and important part of multi-hop question and answer tasks. This paper aims to study the integrity, uniformity, and speed of computational intelligence inference data capabilities. That is why multi-hop reasoning came into being, but it is still in its infancy, that is, it is far from enough to conduct multi-hop question and answer questions, such as search breadth, process complexity, response speed, comprehensiveness of information, etc. This paper makes a text comparison between traditional information retrieval and computational intelligence through corpus relevancy and other computing methods. The study finds that in the face of multi-hop question and answer reasoning, the reasoning data that traditional retrieval methods lagged behind in intelligence are about 35% worse. It shows that computational intelligence would be more complete, unified, and faster than traditional retrieval methods. This paper also introduces the relevant points of text reasoning and describes the process of the multi-hop question answering system, as well as the subsequent discussions and expectations.

DynamicRetriever:A Pre-trained Model-based IR System Without an Explicit Index

Web search provides a promising way for people to obtain information and has been extensively studied.With the surge of deep learning and large-scale pre-training techniques,various neural information retrieval models are proposed,and they have demonstrated the power for improving search(especially,the ranking)quality.All these existing search methods follow a common paradigm,i.e.,index-retrieve-rerank,where they first build an index of all documents based on document terms(i.e.,sparse inverted index)or representation vectors(i.e.,dense vector index),then retrieve and rerank retrieved documents based on the similarity between the query and documents via ranking models.In this paper,we explore a new paradigm of information retrieval without an explicit index but only with a pre-trained model.Instead,all of the knowledge of the documents is encoded into model parameters,which can be regarded as a differentiable indexer and optimized in an end-to-end manner.Specifically,we propose a pre-trained model-based information retrieval(IR)system called DynamicRetriever,which directly returns document identifiers for a given query.Under such a framework,we implement two variants to explore how to train the model from scratch and how to combine the advantages of dense retrieval models.Compared with existing search methods,the model-based IR system parameterizes the traditional static index with a pre-training model,which converts the document semantic mapping into a dynamic and updatable process.Extensive experiments conducted on the public search benchmark Microsoft machine reading comprehension(MS MARCO)verify the effectiveness and potential of our proposed new paradigm for information retrieval.