Question answering

About: Question answering is a research topic. Over the lifetime, 14024 publications have been published within this topic receiving 375482 citations. The topic is also known as: QA & question-answering.

Deep question answering-based questions and answers clarifying method and device

Abstract: The invention discloses a deep question answering-based questions and answers clarifying method and device. The method comprises the following steps of receiving an input query statement, recalling a corresponding answer title and/or historic query statement according to the query statement, analyzing the answer title and/or historic query statement to acquire a corresponding dependency tree, clustering the answer title and/or historic query statement according to the dependency tree to generate at least one question and answer cluster, generalizing at least one question and answer to generate a corresponding candidate clarified question and answer, and displaying the candidate clarified question and answer. In the method, called answer title and/or historic query statement related to the query statement are clustered and generalized to generate the candidate clarified question and answer and display the candidate clarified question and answer to the user, so the user can acquire precise retrieval result according to the candidate clarified question and answer; and retrieval efficiency can be improved.

VANiLLa : Verbalized Answers in Natural Language at Large Scale.

Abstract: In the last years, there have been significant developments in the area of Question Answering over Knowledge Graphs (KGQA). Despite all the notable advancements, current KGQA datasets only provide the answers as the direct output result of the formal query, rather than full sentences incorporating question context. For achieving coherent answers sentence with the question's vocabulary, template-based verbalization so are usually employed for a better representation of answers, which in turn require extensive expert intervention. Thus, making way for machine learning approaches; however, there is a scarcity of datasets that empower machine learning models in this area. Hence, we provide the VANiLLa dataset which aims at reducing this gap by offering answers in natural language sentences. The answer sentences in this dataset are syntactically and semantically closer to the question than to the triple fact. Our dataset consists of over 100k simple questions adapted from the CSQA and SimpleQuestionsWikidata datasets and generated using a semi-automatic framework. We also present results of training our dataset on multiple baseline models adapted from current state-of-the-art Natural Language Generation (NLG) architectures. We believe that this dataset will allow researchers to focus on finding suitable methodologies and architectures for answer verbalization.

Fine-grained opinion analysis: structure-aware approaches

Abstract: Natural language reflects the affective nature of the human mind. Accordingly, expressions of affect and opinion appear profusely in natural language utterances—either explicitly or implicitly. Recognizing and interpreting the subjective information, beyond factual information such as topics and events thereby constitute an important aspect of natural language understanding. Indeed in recent years, there has been a great surge of research interest to help computers understand the subjective side of natural language. In this dissertation, we explore computational methods that can push the envelope for sentiment analysis in text. There are two distinctive themes in our contributions: First, our focus will be on fine-grained opinion analysis, which has been relatively less explored than coarse-grained analysis (e.g., document-level classification). Second, the approaches developed in our work are structure-aware in that we design the inference and/or learning algorithms reflecting the task-specific linguistic structure. We tackle five different sets of problems under these themes, and the key results are summarized in the paragraphs below: Joint extraction of opinion elements and relations. In this work, we present a system for extracting fine-grained opinion elements such as opinion expressions and the sources of opinions, and the relations among those elements, using machine learning techniques and integer linear programming. The extracted opinion elements can then be used as building blocks for various opinion applications, such as opinion summarization or opinion-oriented question answering. Joint extraction of opinions and their attributes. We recognize that the task of determining polarity is related to the task of determining intensity. Based on this observation, we develop a hierarchical sequential learning technique to extract opinion expressions and their attributes—polarity and intensity—simultaneously. Polarity inference in light of compositional semantics. In this work, we investigate methods for fine-grained polarity classification by drawing a connection to compositional semantics, one of the classic branches of research across linguistics and logic. This work attempts to bridge the gap between theories in compositional semantics and practical approaches based on machine learning techniques, by incorporating simple compositional rules based on syntactic patterns as structural inference for the learning algorithm. Lexicon adaptation as constraint optimization. Although there has been plentiful research in the creation of lexical resources for sentiment analysis, most is conducted in isolation from actual applications. As a result, a purportedly better lexical resource might not lead to better performance when utilized for a specific natural language application. To address this problem, we develop a method that adapts a general-purpose polarity lexicon into a domain-specific one in the context of a specific NLP task, by casting the problem as a constraint optimization problem using integer linear programming. Structured local training for coreference resolution. Once we have identified fine-grained opinion elements in text, we need to determine whether some of the extracted phrases are referring to an identical entity—namely, coreference resolution. In this work, we develop “structured local training”, a machine learning technique based on Conditional Random Fields (CRFs) that directly incorporates the interaction between local decisions and global decisions into the learning procedure. We also propose “biased potential functions” that can empirically drive CRFs towards performance improvements with respect to the preferred evaluation measure.

Agentized, Contextualized Filters for Information Management

Abstract: When people read or write documents, they spontaneously generate new information needs: for example, to understand the text they are reading; to find additional information related to the points they are making in their drafts. Simultaneously, each Information Object (IO) (i.e., word, entity, term, concept, phrase, proposition, sentence, paragraph, section, document, collection, etc.) someone reads or writes also creates context for the other IOs in the same discourse. We present a conceptual model of Agentized, Contextualized Filters (ACFs)–agents that identify an appropriate context for an information object and then actively fetch and filter relevant information concerning the information object in other information sources the user has access to. We illustrate the use of ACFs in a prototype knowledge management system called ViviDocs.

Research and Development of Intelligent Q&A System for Medical Industry

Abstract: In order to realize the intelligent question answering system for disease diagnosis and treatment in medical informationization,this article proposed a method for understanding the user'question described by nature language based on human-computer interaction.On the basis of the Keywords library,the method use the maximum matching algorithm for lexical analysing of the user consulting.And then classify the issues of medical field,the system take the data retrieval according to the categories of issues.The system will also use the method of template matching for extracting the answer,with the sentence as the basic unit of answer.Finally,intelligent question answering system of medical field is realized based on B/S model on the platform of JSP and JAVA,it prove the effectiveness of this method.