scholarly journals Finding Answers to Questions, in Text Collections or Web, in Open Domain or Specialty Domains

2012 ◽  
pp. 344-370
Author(s):  
Brigitte Grau
Keyword(s):  
Natural Language ◽  
Information Management ◽  
Question Answering ◽  
Target Language ◽  
Open Domain ◽  
Text Collections ◽  
Factual Question ◽  
Structured Knowledge ◽  
Answering Questions ◽  
The Web

This chapter is dedicated to factual question answering, i.e., extracting precise and exact answers to question given in natural language from texts. A question in natural language gives more information than a bag of word query (i.e., a query made of a list of words), and provides clues for finding precise answers. The author first focuses on the presentation of the underlying problems mainly due to the existence of linguistic variations between questions and their answerable pieces of texts for selecting relevant passages and extracting reliable answers. The author first presents how to answer factual question in open domain. The author also presents answering questions in specialty domain as it requires dealing with semi-structured knowledge and specialized terminologies, and can lead to different applications, as information management in corporations for example. Searching answers on the Web constitutes another application frame and introduces specificities linked to Web redundancy or collaborative usage. Besides, the Web is also multilingual, and a challenging problem consists in searching answers in target language documents other than the source language of the question. For all these topics, this chapter presents main approaches and the remaining problems.

scholarly journals Geographic Question Answering: Challenges, Uniqueness, Classification, and Future Directions

2021 ◽  
Vol 2 ◽  
pp. 1-21
Author(s):  
Gengchen Mai ◽  
Krzysztof Janowicz ◽  
Rui Zhu ◽  
Ling Cai ◽  
Ni Lao
Keyword(s):  
Artificial Intelligence ◽  
Natural Language ◽  
Question Answering ◽  
Future Research ◽  
Open Domain ◽  
Future Directions ◽  
Research Directions ◽  
Classification Framework ◽  
Substantial Progress ◽  
Future Research Directions

Abstract. As an important part of Artificial Intelligence (AI), Question Answering (QA) aims at generating answers to questions phrased in natural language. While there has been substantial progress in open-domain question answering, QA systems are still struggling to answer questions which involve geographic entities or concepts and that require spatial operations. In this paper, we discuss the problem of geographic question answering (GeoQA). We first investigate the reasons why geographic questions are difficult to answer by analyzing challenges of geographic questions. We discuss the uniqueness of geographic questions compared to general QA. Then we review existing work on GeoQA and classify them by the types of questions they can address. Based on this survey, we provide a generic classification framework for geographic questions. Finally, we conclude our work by pointing out unique future research directions for GeoQA.

Dynamic Graph Reasoning for Conversational Open-Domain Question Answering

2022 ◽  
Vol 40 (4) ◽  
pp. 1-24
Author(s):  
Yongqi Li ◽  
Wenjie Li ◽  
Liqiang Nie
Keyword(s):  
Information Needs ◽  
Question Answering ◽  
Relevant Information ◽  
Conversational Agents ◽  
Open Domain ◽  
Transition Flow ◽  
Dynamic Graph ◽  
Answer Extraction ◽  
Exact Answer ◽  
The Web

In recent years, conversational agents have provided a natural and convenient access to useful information in people’s daily life, along with a broad and new research topic, conversational question answering (QA). On the shoulders of conversational QA, we study the conversational open-domain QA problem, where users’ information needs are presented in a conversation and exact answers are required to extract from the Web. Despite its significance and value, building an effective conversational open-domain QA system is non-trivial due to the following challenges: (1) precisely understand conversational questions based on the conversation context; (2) extract exact answers by capturing the answer dependency and transition flow in a conversation; and (3) deeply integrate question understanding and answer extraction. To address the aforementioned issues, we propose an end-to-end Dynamic Graph Reasoning approach to Conversational open-domain QA (DGRCoQA for short). DGRCoQA comprises three components, i.e., a dynamic question interpreter (DQI), a graph reasoning enhanced retriever (GRR), and a typical Reader, where the first one is developed to understand and formulate conversational questions while the other two are responsible to extract an exact answer from the Web. In particular, DQI understands conversational questions by utilizing the QA context, sourcing from predicted answers returned by the Reader, to dynamically attend to the most relevant information in the conversation context. Afterwards, GRR attempts to capture the answer flow and select the most possible passage that contains the answer by reasoning answer paths over a dynamically constructed context graph . Finally, the Reader, a reading comprehension model, predicts a text span from the selected passage as the answer. DGRCoQA demonstrates its strength in the extensive experiments conducted on a benchmark dataset. It significantly outperforms the existing methods and achieves the state-of-the-art performance.

Question Answering from Procedural Semantics to Model Discovery

2006 ◽  
pp. 479-485
Author(s):  
John Kontos ◽  
Ioanna Malagardi
Keyword(s):  
Natural Language ◽  
World Wide ◽  
Question Answering ◽  
Knowledge Bases ◽  
Procedural Semantics ◽  
Short Answer ◽  
Text Collections ◽  
The World ◽  
Fact Retrieval ◽  
Parameter Values

Question Answering (QA) is one of the branches of Artificial Intelligence (AI) that involves the processing of human language by computer. QA systems accept questions in natural language and generate answers often in natural language. The answers are derived from databases, text collections, and knowledge bases. The main aim of QA systems is to generate a short answer to a question rather than a list of possibly relevant documents. As it becomes more and more difficult to find answers on the World Wide Web (WWW) using standard search engines, the technology of QA systems will become increasingly important. A series of systems that can answer questions from various data or knowledge sources are briefly described. These systems provide a friendly interface to the user of information systems that is particularly important for users who are not computer experts. The line of development of ideas starts with procedural semantics and leads to interfaces that support researchers for the discovery of parameter values of causal models of systems under scientific study. QA systems historically developed roughly during the 1960-1970 decade (Simmons, 1970). A few of the QA systems that were implemented during this decade are: • The BASEBALL system (Green et al., 1961) • The FACT RETRIEVAL System (Cooper, 1964) • The DELFI systems (Kontos & Kossidas, 1971; Kontos & Papakontantinou, 1970)

scholarly journals Natural Language Generation at Scale: A Case Study for Open Domain Question Answering

2019 ◽  
Author(s):  
Alessandra Cervone ◽  
Chandra Khatri ◽  
Rahul Goel ◽  
Behnam Hedayatnia ◽  
Anu Venkatesh ◽  
...  
Keyword(s):  
Natural Language ◽  
Question Answering ◽  
Natural Language Generation ◽  
Open Domain ◽  
Language Generation

scholarly journals Break It Down: A Question Understanding Benchmark

2020 ◽  
Vol 8 ◽  
pp. 183-198
Author(s):  
Tomer Wolfson ◽  
Mor Geva ◽  
Ankit Gupta ◽  
Matt Gardner ◽  
Yoav Goldberg ◽  
...  
Keyword(s):  
Natural Language ◽  
Formal Language ◽  
Question Answering ◽  
Semantic Parsing ◽  
Open Domain ◽  
Meaning Representation

Understanding natural language questions entails the ability to break down a question into the requisite steps for computing its answer. In this work, we introduce a Question Decomposition Meaning Representation (QDMR) for questions. QDMR constitutes the ordered list of steps, expressed through natural language, that are necessary for answering a question. We develop a crowdsourcing pipeline, showing that quality QDMRs can be annotated at scale, and release the Break dataset, containing over 83K pairs of questions and their QDMRs. We demonstrate the utility of QDMR by showing that (a) it can be used to improve open-domain question answering on the HotpotQA dataset, (b) it can be deterministically converted to a pseudo-SQL formal language, which can alleviate annotation in semantic parsing applications. Last, we use Break to train a sequence-to-sequence model with copying that parses questions into QDMR structures, and show that it substantially outperforms several natural baselines.

A Brief Survey of Question Answering Systems

2021 ◽  
Vol 12 (5) ◽  
pp. 01-07
Author(s):  
Michael Caballero
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
Open Domain ◽  
Knowledge Based ◽  
Current State ◽  
Introductory Overview ◽  
Building Systems ◽  
Question Answering Systems

Question Answering (QA) is a subfield of Natural Language Processing (NLP) and computer science focused on building systems that automatically answer questions from humans in natural language. This survey summarizes the history and current state of the field and is intended as an introductory overview of QA systems. After discussing QA history, this paper summarizes the different approaches to the architecture of QA systems -- whether they are closed or open-domain and whether they are text-based, knowledge-based, or hybrid systems. Lastly, some common datasets in this field are introduced and different evaluation metrics are discussed.

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