Language Question Answering System Research Articles

Natural Language Processing Pretraining Language Model for Computer Intelligent Recognition Technology

Computer intelligent recognition technology refers to the use of computer vision, Natural Language Processing (NLP), machine learning and other technologies to enable computers to recognize, analyze, understand and answer human language and behavior. The common applications of computer intelligent recognition technology include image recognition, NLP, face recognition, target tracking, and other fields. NLP is a field of computer science, which involves the interaction between computers and natural languages. NLP technology can be used to process, analyze and generate natural language data, such as text, voice and image. Common NLP technology applications include language translation, emotion analysis, text classification, speech recognition and question answering system. Language model is a machine learning model, which uses a large number of text data for training to learn language patterns and relationships in text data. Although the language model has made great progress in the past few years, it still faces some challenges, including: poor semantic understanding, confusion in multilingual processing, slow language processing and other shortcomings. Therefore, in order to optimize these shortcomings, this article would study the pre-training language model based on NLP technology, which aimed at using NLP technology to optimize and improve the performance of the language model, thus optimizing the computer intelligent recognition technology. The model had a higher language understanding ability and more accurate prediction ability. In addition, the model could learn language rules and structures by using a large number of corpus, so as to better understand natural language. Through experiments, it could be known that the data size and total computing time of the traditional Generative Pretrained Transformer-2 (GPT-2) language model were 10 GB and 97 hours respectively. The data size and total computing time of BERT (Bidirectional Encoder Representations from Transformer) were 12 GB and 86 hours respectively. The data size and total computing time of the pre-training language model based on NLP were 18 GB and 71 hours respectively. Obviously, the pre-training language model based on NLP had a larger data size and shorter computing time. The experimental results showed that the NLP technology could better optimize the language model and effectively improve its various capabilities. This article opened up a new development direction for computer intelligent recognition technology and provided excellent technical support for the development of language models.

Enhancing service excellence: analyzing natural language question answering with advanced cosine similarity

<span lang="EN-US">Information related to student services in higher education must be produced and disseminated in various forms. Covid-19 pandemic, student services with a remote model related to this question and answer become very important. To carry out this automation process, the advanced cosine similarity method is used to check the similarity of the questions to the database and statistics to calculate the similarity value of each word. The proposed paper proceeds with three phases. The first stage to solve this problem is the data processed in question; the professional next step is word insertion. It converts alphanumeric words to vector format. Each word is a vector that represents a point in space with a certain dimension. The recommended advanced cosine similarity data still must be analyzed into a statistical approach. We will measure accuracy to get results so that optimal results and answers are obtained, research procedures are carried out based on literature study, initial data collection and observation, system development, system testing, system analysis, and system evaluation. This research implemented in universities with student chat automation applications providing an accuracy 83.90% given by natural language question answering system (NLQAS) so that it can improve excellent service in universities.</span>

SPARQL Generation with an NMT-based Approach

SPARQL is a powerful query language which has been widely used in various natural language question answering (QA) systems. As the advances of deep neural networks, Neural Machine Translation (NMT) models are employed to directly translate natural language questions to SPARQL queries in recent years. In this paper, we propose an NMT-based approach with Transformer model to generate SPARQL queries. Transformer model is chosen due to its relatively high efficiency and effectiveness. We design a format to encode a SPARQL query into a simple sequence with only RDF triples reserved. The main purpose of this step is to shorten the sequences and reduce the complexity of the target language. Moreover, we employ entity type tags to further resolve mistranslated problems. The proposed approach is evaluated against three open-domain question answering datasets (QALD-7, QALD-8, and LC-QuAD) on BLEU score and accuracy, and obtains outstanding results (83.49%, 90.13%, and 76.32% on BLEU score, respectively) which considerably outperform all known studies.

Study on Tibetan-Chinese Comparable Corpus Extraction

Tibetan-Chinese comparable corpus extraction is a basis work for Tibetan-Chinese cross language question answering system, information retrieval, machine translation and other researches. This paper is an exploration to solve the scarcity of Tibetan-Chinese comparable corpus. It will promote the knowledge sharing between different languages. In this paper, we propose a method to extract Tibetan-Chinese comparable corpus. The main work is in the following: (1) Tibetan-Chinese comparable corpus extraction model based on multi-feature of bilingual websites. (2) Extraction method based on entity link from naturally annotated resources. Finally, the experimental results show our approach is effective.

Framework for Improved Question Answering System

Background/Objectives: The proposed frame work uses statistical based similarity measure for QAS, which gives short and exact answer to user query. The entire frame work is focusing similarity computation between question and answer pair. Methods/Statistical Analysis: The involvement of similarity computation between question and answer pair should be investigated in this work QAS. Statistical based similarity measure is an important issue as compared to similarity computation in many fields such as, Natural Language Processing, Information Retrieval, Ontology Mapping, Knowledge Acquisition and Question Answering System. Semantic web has been used to present the structure knowledge representation between questions and answer keywords. Ontology can be used as important proposal to enable semantic similarity between keywords present in the question and answer pairs. Findings: The proposed frame work investigate, how the statistical based similarity measure used in these system helps to improve the performance of the QA system for all Wh-questions. We find the keywords having most similar meaning will return the answer as the final answer. The best performance is achieved by extracting the relevant snippets information form Google search engine. The performance of the system will change as the retrieval of the document is increases beyond 15; this could indicate that restriction in the retrieval of document is helps to optimize the performance of the system. Application/Improvements: This frame work is applicable to all type of search engines which helps to finds most relevant answer to user questions. In future, we implement our frame work for questions having more than five keywords.

Performance Evaluation of Different Similarity Functions and Classification Methods Using Web Based Hindi Language Question Answering System

Question Answering (QA) system is an approach to extract the correct answer for the query asked by the user in its own language. The work discussed is implemented for Hindi Language objective type questions and answers. The paper implements the comparison of nine different similarity functions and two classification methods used to retrieve the desired information. The results revealconclude that Smith Waterman outperforms the other similarity functions in perforamnce evaluation. The K-Nearest Neighbor(K-NN) algorithm gives 97%, 95.6% and Neasret Neighbor (NN) algorithm gives 93.3%,95% for two differtent test data sets, respectively.

A Survey of Natural Language Question Answering System

In Question answering (QA) system retrieves the precise information from large documents according to user query. This survey paper describe the different methods of natural language question answering system (NLQA) for general languages.QA System automatically retrieve correct responses to the question asked by the human in natural languages.

Natural Language Access to Enterprise Data

This paper describes USI Answers — a natural language question answering system for enterprise data. We report on the progress towards the goal of offering easy access to enterprise data to a large number of business users, most of whom are not familiar with the specific syntax or semantics of the underlying data sources. Additional complications come from the nature of the data, which comes both as structured and unstructured. The proposed solution allows users to express questions in natural language, makes apparent the system's interpretation of the query, and allows easy query adjustment and reformulation. The application is in use by more than 1500 users from Siemens Energy. We evaluate our approach on a data set consisting of fleet data.

Architecture of an Ontology-Based DomainSpecific Natural Language Question Answering System

Question answering (QA) system aims at retrieving precise information from a large collection of documents against a query. This paper describes the architecture of a Natural Language Question Answering (NLQA) system for a specific domain based on the ontological information, a step towards semantic web question answering. The proposed architecture defines four basic modules suitable for enhancing current QA capabilities with the ability of processing complex questions. The first module was the question processing, which analyses and classifies the question and also reformulates the user query. The second module allows the process of retrieving the relevant documents. The next module processes the retrieved documents, and the last module performs the extraction and generation of a response. Natural language processing techniques are used for processing the question and documents and also for answer extraction. Ontology and domain knowledge are used for reformulating queries and identifying the relations. The aim of the system is to generate short and specific answer to the question that is asked in the natural language in a specific domain. We have achieved 94 % accuracy of natural language question answering in our implementation.

Architecture of an Ontology-Based Domain-Specific Natural Language Question Answering System

Question answering (QA) system aims at retrieving precise information from a large collection of documents against a query. This paper describes the architecture of a Natural Language Question Answering (NLQA) system for a specific domain based on the ontological information, a step towards semantic web question answering. The proposed architecture defines four basic modules suitable for enhancing current QA capabilities with the ability of processing complex questions. The first module was the question processing, which analyses and classifies the question and also reformulates the user query. The second module allows the process of retrieving the relevant documents. The next module processes the retrieved documents, and the last module performs the extraction and generation of a response. Natural language processing techniques are used for processing the question and documents and also for answer extraction. Ontology and domain knowledge are used for reformulating queries and identifying the relations. The aim of the system is to generate short and specific answer to the question that is asked in the natural language in a specific domain. We have achieved 94 % accuracy of natural language question answering in our implementation.

USI Answers: Natural Language Question Answering Over (Semi-) Structured Industry Data

This paper describes USI Answers a natural language question answering system for semi-structured industry data. The paper reports on the progress towards the goal of offering easy access to enterprise data to a large number of business users, most of whom are not familiar with the specific syntax or semantics of the underlying data sources. Additional complications come from the nature of the data, which comes both as structured and unstructured. The proposed solution allows users to express questions in natural language, makes apparent the system’s interpretation of the query, and allows easy query adjustment and reformulation. The application is in use by more than 1500 users from Siemens Energy. We evaluate our approach on a data set consisting of fleet data.

Logic-Based Question Answering

Question answering systems aim to provide concise and correct responses to arbitrary questions, communicating with the user in a natural language. This way they help making the knowledge of large textual sources accessible in an intuitive manner which goes beyond the capabilities of conventional search engines. In the LogAnswer project the universities of Hagen and Koblenz cooperate to build a German language question answering system which combines computational linguistics and automated reasoning to deduce answers from a knowledge base derived from Wikipedia.

Computing intensional answers to questions – An inductive logic programming approach

Research on natural language interfaces has mainly concentrated on question interpretation as well as answer computation, but not focused as much on answer presentation. In most natural language interfaces, answers are in fact provided extensionally as a list of all those instances satisfying the query description. In this paper, we aim to go beyond such a mere listing of facts and move towards producing additional descriptions of the query results referred to as intensional answers. We define an intensional answer (IA) as a logical description of the actual set of answer items to a given query in terms of properties that are shared by exactly these answer items. We argue that IAs can enhance a user’s understanding of the answer itself but also of the underlying knowledge base. In particular, we present an approach for computing an intensional answer given an extensional answer (i.e. a set of entities) returned as a result of a question. In our approach, an intensional answer is represented by a clause and computed based on inductive logic programming (ILP) techniques, in particular bottom-up clause generalization. The approach is evaluated in terms of usefulness and time performance, and we discuss its potential for helping to detect flaws in the knowledge base as well as to interactively enrich it with new knowledge. While the approach is used in the context of a natural language question answering system in our settings, it clearly has applications beyond, e.g. in the context of research on generating referring expressions.

Question Answering from Frequently Asked Question Files: Experiences with the FAQ Finder System

This technical report describes FAQ Finder, a natural language question answering system that uses files of frequently asked questions as its knowledge base. Unlike AI question-answering systems that focus on the generation of new answers, FAQ Finder retrieves existing ones found in frequently-asked question files. Unlike information retrieval approaches that rely on a purely lexical metric of similarity between query and document, FAQ Finder uses a semantic knowledge base (WordNet) to improve its ability to match question and answer. We describe the design and the current implementation of the system and its support components, including results from an evaluation of the system''s performance against a corpus of user questions. An important finding was that a combination of semantic and statistical techniques works better than any single approach. We analyze failures of the system and discuss future research aimed at addressing them.

An efficient easily adaptable system for interpreting natural language queries

This paper gives an overall account of a prototype natural language question answering system, called Chat-80. Chat-80 has been designed to be both efficient and easily adaptable to a variety of applications. The system is implemented entirely in Prolog, a programming language based on logic. With the aid of a logic-based grammar formalism called extraposition grammars, Chat-80 translates English questions into the Prolog subset of logic. The resulting logical expression is then transformed by a planning algorithm into efficient Prolog, cf. query optimisation in a relational database. Finally, the Prolog form is executed to yield the answer. On a domain of world geography, most questions within the English subset are answered in well under one second, including relatively complex queries.

An English language question answering system for a large relational database

By typing requests in English, casual users will be able to obtain explicit answers from a large relational database of aircraft flight and maintenance data using a system called PLANES. The design and implementation of this system is described and illustrated with detailed examples of the operation of system components and examples of overall system operation. The language processing portion of the system uses a number of augmented transition networks, each of which matches phrases with a specific meaning, along with context registers (history keepers) and concept case frames; these are used for judging meaningfulness of questions, generating dialogue for clarifying partially understood questions, and resolving ellipsis and pronoun reference problems. Other system components construct a formal query for the relational database, and optimize the order of searching relations. Methods are discussed for handling vague or complex questions and for providing browsing ability. Also included are discussions of important issues in programming natural language systems for limited domains, and the relationship of this system to others.