Natural Language Processing based Question Answering Techniques: A Survey

One of the aims of Natural Language Processing (NLP) is to facilitate the use of computers by allowing users to interact with systems in natural language. Since such interactions often take the form of question-answering sessions, the process of question-answering is an important part of NLP. In this paper, we are concerned with the progress made towards building question-answering systems which are natural and satisfying to users, allowing them to express themselves freely and answering questions appropriately. Such systems are said to provide graceful interaction. We survey the evolution of question-answering programs, presenting steps and approaches that have been taken towards providing graceful man-machine interaction, and pointing out efficiencies of existing programs and theories. Our emphasis is on the various issues and difficulties encountered in building a user-friendly question-answering program.

Answer set Prolog, or AnsProlog in short, is one of the leading knowledge representation (KR) languages with a large body of theoretical and building block results, several implementations and reasoning and declarative problem solving applications. But it shares the problem associated with knowledge acquisition with all other KR languages; most knowledge is entered manually by people and that is a bottleneck. Recent advances in natural language processing have led to some systems that convert natural language sentences to a logical form. Although these systems are in their infancy, they suggest a direction to overcome the above mentioned knowledge acquisition bottleneck. In this paper we discuss some recent work by us on developing applications that process logical forms of natural language text and use the processed result together with AnsProlog rules to do reasoning and problem solving.

E-learning is a response to the new educational needs of society and an important development in information and communication technologies because it represents the future of the teaching and learning processes. However, this trend presents many challenges, such as the processing of online forums which generate a huge number of messages with an unordered structure and a great variety of topics. These forums provide an excellent platform for learning and connecting students of a subject but the difficulty of following and searching the vast volume of information that they generate may be counterproductive. The main goal of this paper is to review the approaches and techniques related to online courses in order to present a set of learning analytics techniques and a general architecture that solve the main challenges found in the state of the art by managing them in a more efficient way: 1) efficient tracking and monitoring of forums generated; 2) design of effective search mechanisms for questions and answers in the forums; and 3) extraction of relevant key performance indicators with the objective of carrying out an efficient management of online forums. In our proposal, natural language processing, clustering, information retrieval, question answering, and data mining techniques will be used.

Recent years have observed a significant progress in information retrieval and natural language processing with deep learning technologies being successfully applied into almost all of their major tasks. The key to the success of deep learning is its capability of accurately learning distributed representations (vector representations or structured arrangement of them) of natural language expressions such as sentences, and effectively utilizing the representations in the tasks. This tutorial aims at summarizing and introducing the results of recent research on deep learning for information retrieval, in order to stimulate and foster more significant research and development work on the topic in the future. The tutorial mainly consists of three parts. In the first part, we introduce the fundamental techniques of deep learning for natural language processing and information retrieval, such as word embedding, recurrent neural networks, and convolutional neural networks. In the second part, we explain how deep learning, particularly representation learning techniques, can be utilized in fundamental NLP and IR problems, including matching, translation, classification, and structured prediction. In the third part, we describe how deep learning can be used in specific application tasks in details. The tasks are search, question answering (from either documents, database, or knowledge base), and image retrieval.

Computer systems that can directly and accurately answer peoples' questions over a broad domain of human knowledge have been envisioned by scientists and writers since the advent of computers themselves. Open domain question answering holds tremendous promise for facilitating informed decision making over vast volumes of natural language content. Applications business intelligence, healthcare, customer support, enterprise knowledge management, social computing, science and government would all benefit from deep language processing. The DeepQA project (www.ibm.com/deepqa) is aimed at illustrating how the advancement and integration of Natural Language Processing (NLP), Information Retrieval (IR), Machine Learning (ML), massively parallel computation and Knowledge Representation and Reasoning (KR&R) can greatly advance open-domain automatic Question Answering. An exciting proof-point this challenge is to develop a computer system that can successfully compete against top human players at the Jeopardy! quiz show (www.jeopardy.com). Attaining champion-level performance Jeopardy! requires a computer to rapidly answer rich open-domain questions, and to predict its own performance on any given category/question. The system must deliver high degrees of precision and confidence over a very broad range of knowledge and natural language content and with a 3-second response time. To do this DeepQA generates, evidences and evaluates many competing hypotheses. A key to success is automatically learning and combining accurate confidences across an array of complex algorithms and over different dimensions of evidence. Accurate confidences are needed to know when to buzz in against your competitors and how much to bet. Critical for winning at Jeopardy!, High precision and accurate confidence computations are just as critical for providing real value business settings where helping users focus on the right content sooner and with greater confidence can make all the difference. The need for speed and high precision demands a massively parallel compute platform capable of generating, evaluating and combing 1000's of hypotheses and their associated evidence. In this talk I will introduce the audience to the Jeopardy! Challenge and describe our technical approach and our progress on this grand-challenge problem.

The Web enables to have access to silo-ed information describing news articles, often offering a multitude of viewpoints that, once combined, can provide a broader picture of the story being reported on the news. In this paper, we propose an approach that automatically extracts representative features of a news item, namely named entities, from textual content attached to a video item (subtitles) and from a set of documents from the Web collected using entity expansion techniques. Approaches relying on entity expansion generally try to collect and process the important facts behinds a particular news item, but they are often too dependent on frequency-based functions and information retrieval techniques thus neglecting the multi-dimensional relationships that are established among the entities. We propose a concentric-based approach that enables to represent the context of a news item, by harmonizing into a single model the representative entities, which can be extracted using information retrieval and natural language processing techniques (Core), and other entities that get prominent according to different dimensions such as informativeness, semantic connectivity, or popularity (Crust). We compare our approach with a baseline by analyzing the compactness of the generated summary on an existing gold standard available on the Web. Results of the experiments show that our approach converges faster to the ideal compact news snapshot with an improvement of 36.9% over the baseline.

The field of Question Answering (QA) is very multi-disciplinary as it requires expertise from a large number of areas such as natural language processing (NLP), artificial intelligence, machine learning, information retrieval, speech recognition and semantic technologies. In the past years a large number of QA systems were proposed using approaches from different fields and focusing on particular tasks in the QA process. Unfortunately, most of these systems cannot be easily reused, extended, and results cannot be easily reproduced since the systems are mostly implemented in a monolithic fashion, lack standardized interfaces and are often not open source or available as Web services. To address these issues we developed the knowledge-based Qanary methodology for choreographing QA pipelines distributed over the Web. Qanary employs the qa vocabulary as an exchange format for typical QA components. As a result, QA systems can be built using the Qanary methodology in a simpler, more flexible and standardized way while becoming knowledge-driven instead of being process-oriented. This paper presents the components and services that are integrated using the qa vocabulary and the Qanary methodology within the Qanary ecosystem. Moreover, we show how the Qanary ecosystem can be used to analyse QA processes to detect weaknesses and research gaps. We illustrate this by focusing on the Entity Linking (EL) task w.r.t. textual natural language input, which is a fundamental step in most QA processes. Additionally, we contribute the first EL benchmark for QA, as open source. Our main goal is to show how the research community can use Qanary to gain new insights into QA processes.

Humans have used electronic information retrieval (IR) systems for more than 50 years as they evolved from experimental systems to full-scale Web search engines and digital libraries. The fields of library and information science (LIS), cognitive science, human factors and computer science have historically been the leading disciplines in conducting research that seeks to model human interaction with IR systems for all kinds of information related behaviors. As technology problems have been mastered, the theoretical and applied framework for studying human interaction with IR systems has evolved from systems-centered to more user-centered, or cognitive-centered approaches. However, cognitive information retrieval (CIR) research that focuses on user interaction with IR systems is still largely under-funded and is often not included at computing and systems design oriented conferences. But CIR-focused research continues, and there are signs that some IR systems designers in academia and the Web search business are realizing that user behavior research can provide valuable insights into systems design and evaluation. The goal of our book is to provide an overview of new CIR research directions. This book does not provide a history of the research field of CIR. Instead, the book confronts new ways of looking at the human information condition with regard to our increasing need to interact with IR systems. The need has grown due to a number of factors, including the increased importance of information to more people in this information age. Also, IR was once considered document-oriented, but has now evolved to include multimedia, text, and other information objects. As a result, IR systems and their complexity have proliferated as users and user purposes for using them have also proliferated. Human interaction with IR systems can often be frustrating as people often lack an understanding of IR system functionality. New more holistic directions in CIR are emerging that conceptualize human-IR system interaction at the human-computer interaction level (taking into account interface issues), the information behavior level (taking into account the role of IR system interaction in the total range of people’s information behaviors), and the

In this paper we explore the challenges to effectively use natural language processing (NLP) for information retrieval. First, we briefly cover current NLP uses and research areas in the intersection of both fields, namely summarization, information extraction, and question answering. Second, we motivate other possible challenging uses of NLP for information retrieval such as determining context, semantic search, and supporting the Semantic Web. We end with a particular use of NLP for a new problem, searching the future, that poses additional NLP challenges.KeywordsInformation RetrievalNatural Language ProcessingInformation ExtractionInformation Retrieval SystemQuestion AnsweringThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

In today's environment of information overload, Question Answering (QA) is a critically important research area. QA is the task of automatically extracting a precise answer from one or more data sources to a question posed in natural language. A twostage strategy is typically adopted when designing a QA system; the first stage is an Information Retrieval (IR) process which returns a set of candidate documents relevant to the question and the second stage narrows the information contained in those passages down to a single response (sentence or entity) that answers the question, typically using Information Extraction (IE) or Natural Language Processing methods. This research proposes novel techniques for QA by enhancing the user's original query with latent semantic information from the corpus. This enhanced query is then applied to both the first and second stages of the QA architecture. To build the enhanced query, we propose the Aspect-Based Relevance Language Model as an approach that uses statistical language modeling techniques to measure the likelihood of relevance of a concept (oraspect as defined by Probabilistic Latent Semantic Analysis) to a question. We then use terms from the aspects that have the highest likelihood of relevance to design a model for a semantic Question Context, which includes sense-disambiguated terms than amplify the user's query. Question Context is incorporated into the first state of QA as query expansion to improve recall. We then derive a novel measure called Answer Credibility from the Question Context. Answer Credibility may be thought of as a statistical measure of the reliability of a candidate answer with respect to a question and the source text from which the candidate answer was derived. We incorporate Answer Credibility in the Answer Validation process; the answer with the highest score after the application of Answer Credibility is returned to the user. Our techniques show performance improvements over state-of-the-art approaches, and have the advantage that they use statistical techniques to derive semantic information to aid the process of QA.

<span>In the biomedical field due to tremendous medical research, every year the medical papers generated is very huge. In order to process these tremendous amounts of textual data, the information retrieval techniques are combined with machine learning methods like natural language processing (NLP) for finding the useful insights in data for NLP tasks. In this work, we have addressed the challenges involved in existing NLP techniques like Word2Vec, Doc2Vec, and Biosent2Vec and followed by the traditional classification of articles with Concept2Vec and random forest classification. Concept vector techniques are emerging methods in NLP and in information retrieval systems. Pretrained concept encoders are available in Machine learning, whereas no such exists in biomedical domain. In this paper a proposal on Concept2Vec trained from biomedical text is highlighted. Our work accurately emphasizes on the quality of semantics defined between the medical terms, which can help the medical practitioners in taking errorless decisions. In this work 20 thousand biomedical documents are considered for Concept2Vec embeddings and the proposed approach have shown best concept similarity in terms of relevant measures compared with existing approaches such as Word2Vec, global vectors (GloVe), fastText, and BioSentVec, bidirectional encoder representations from transformers (BERT), biomedical-BERT.</span>

Speech recognition and text summarization, plagiarism detection, machine translation, chatbots, sentiment analysis (SA), question answering (QA), and dialogue systems are all products of natural language processing (NLP), a branch of AI concerned with modeling natural languages for the purpose of developing relevant applications. NLP draws on several disciplines, not only computer science and linguistics, for its research and development. These include cognitive science, psychology, mathematics, and more. More than 1.5 billion Muslims throughout the world depend on the Arabic language for their daily five-times-prayer practice, and Arabic is one of six official languages used by more than 422 million people in the Arab world, according to UNESCO. Dialectal Arabic is the slang language spoken informally in everyday life and varies from country to country; Classical Arabic is a reflection of the language spoken by the Arabs more than fourteen centuries ago. Modern Standard Arabic is an evolving variety of Arabic that borrows and innovates regularly to meet the changing needs of its speakers. Complexity is added to the Arabic language by the fact that it encompasses not one but three different varieties of spoken language: classical, contemporary, and colloquial. However, Arabic language processing on computers remains difficult for a variety of reasons. Because of the extensive inflection and derivational processes that occur in Arabic, a single lemma may be utilized to produce several words with distinct meanings.

: If we want to build intelligent information retrieval systems, we will have to give them the capabilities of understanding Natural language, automatically organizing and reorganizing their memories, and using intelligent heuristics for searching their memories. These systems will have to analyze and understand both new text and Natural Language queries. In answering questions, they will have to direct memory search to reasonable places. This requires good organization of both the conceptual content of text and knowledge necessary for understanding those texts and accessing memory. The CYRUS and FRUMP systems (Kolodner (1978), Schank and Kolodner (1979), Dejong (1979)) comprise an information retrieval system called CyFr. Together, they have the analysis and retrieval capabilities mentioned above. FRUMP analysis news stories from the UPI wire for their conceptual content, and produces summaries of those stories. It sends summaries of stories about important people to CYRUS automatically adds those stories to its memory, and can then retrieve that information to answer question posed to it in natural language. This paper describes the problems involved in building such an intelligent system. It proposes solutions to some of those problems bases on recent research in Artificial Intelligence and Natural Language processing, and describes the CyFr system, which implements those solutions. The solutions we propose and implement are based on a model of human understanding and memory retrieval. (Author)

Machine learning techniques have long been the foundations of speech processing. Bayesian classiflcation, decision trees, unsupervised clustering, the EM algorithm, maximum entropy, etc. are all part of existing speech recognition systems. The success of statistical speech recognition has led to the rise of statistical and empirical methods in natural language processing. Indeed, many of the machine learning techniques used in language processing, from statistical part-of-speech tagging to the noisy channel model for machine translation have roots in work conducted in the speech fleld. However, advances in learning theory and algorithmic machine learning approaches in recent years have led to signiflcant changes in the direction and emphasis of the statistical and learning centered research in natural language processing and made a mark on natural language and speech processing. Approaches such as memory based learning, a range of linear classiflers such as Boosting, SVMs and SNoW and others have been successfully applied to a broad range of natural language problems, and these now inspire new research in speech retrieval and recognition. We have seen an increasingly close collaboration between voice and language processing researchers in some of the shared tasks such as spontaneous speech recognition and understanding, voice data information extraction, and machine translation. The purpose of this special issue was to invite speech and language researchers to communicate with each other, and with the machine learning community on the latest machine learning advances in their work. The call for papers was met with great enthusiasm from the speech and natural language community. Thirty six submissions were received; each paper was reviewed by at least three reviewers. Only ten papers were selected re∞ecting not only some of the best work on machine learning in the areas of natural language and spoken language processing but also what we view as a collection of papers that represent current trends in these areas of research both from the perspective of

Building natural language interfaces to relational databases is an important and challenging problem in natural language processing (NLP), it requires a system that is able to understand natural language questions and generate corresponding SQL queries. In this paper, we present our idea of using type information and database content to better understand rare entities and numbers in natural language questions, in order to improve the model SyntaxSQLNet as the state of the art in Text-to-SQL task. We also present the global architecture and techniques that can be used in the implementation of our Neural Network (NN) model Text2SQLNet, with the integration of our idea that consists of using type information to better understand rare entities and numbers in natural language questions. We can also use the database content to better understand the user query if it is not well-formed. The implementation of this idea can further improve performance in the Text-to-SQL task.