Natural Language Intelligences

Reality, Universal Ontology and Knowledge Systems ◽

10.4018/978-1-59904-966-3.ch011 ◽

2008 ◽

pp. 258-275

Author(s):

Azamat Abdoullaev

Keyword(s):

Web Search ◽

Question Answering ◽

Database Systems ◽

General Purpose ◽

Knowledge Systems ◽

Open Domain ◽

Keyword Query ◽

Trade Name ◽

Question Answering Systems ◽

Special Value

Of all possible intelligent NL applications and semantic artifacts, a special value is today ascribed to building the question answering systems (Q&A) with broad and wide ontological learning (Onto Query Project, 2004), classified as open-domain Q&A knowledge systems [Question Answering, From Wikipedia, 2006]. This line of research is considered as upgrading of a traditional keyword query processing in database systems, as endowing the Web search engines with answering deduction capacities. Ideally, such a general-purpose Q&A agent should be able to cover questions (matters, subjects, topics, issues, themes) from any branch of knowledge and domain of interest by giving answers to any meaningful questions, like the Digital Aristotle, “an application that will encompass much of the world’s scientific knowledge and be capable of answering novel questions and advanced problemsolving” (Project Halo, 2004). The trade name of the Digital Aristotle was inspired by the scholar mostly admired for the depth and width of his perception, whose mind spread over ontology, physics, logics, epistemology, biology, zoology, medicine, psychology, literary theory, politics, and art.

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Predicting Lexical Answer Types in Open Domain QA

International Journal on Semantic Web and Information Systems ◽

10.4018/jswis.2012070104 ◽

2012 ◽

Vol 8 (3) ◽

pp. 74-88 ◽

Cited By ~ 1

Author(s):

Alfio Massimiliano Gliozzo ◽

Aditya Kalyanpur

Keyword(s):

Knowledge Acquisition ◽

Knowledge Base ◽

Large Scale ◽

Question Answering ◽

General Purpose ◽

Open Domain ◽

Lexical Knowledge ◽

Research Challenge ◽

Lexical Knowledge Base

Automatic open-domain Question Answering has been a long standing research challenge in the AI community. IBM Research undertook this challenge with the design of the DeepQA architecture and the implementation of Watson. This paper addresses a specific subtask of Deep QA, consisting of predicting the Lexical Answer Type (LAT) of a question. Our approach is completely unsupervised and is based on PRISMATIC, a large-scale lexical knowledge base automatically extracted from a Web corpus. Experiments on the Jeopardy! data shows that it is possible to correctly predict the LAT in a substantial number of questions. This approach can be used for general purpose knowledge acquisition tasks such as frame induction from text.

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Document Summarization Using Sentence-Level Semantic Based on Word Embeddings

International Journal of Software Engineering and Knowledge Engineering ◽

10.1142/s0218194019500086 ◽

2019 ◽

Vol 29 (02) ◽

pp. 177-196 ◽

Cited By ~ 1

Author(s):

Kamal Al-Sabahi ◽

Zhang Zuping

Keyword(s):

Language Processing ◽

Web Search ◽

Question Answering ◽

Information Overload ◽

Good Representation ◽

Intelligence Analysis ◽

Word Embeddings ◽

Question Answering Systems ◽

Active Research ◽

News Recommendation

In the era of information overload, text summarization has become a focus of attention in a number of diverse fields such as, question answering systems, intelligence analysis, news recommendation systems, search results in web search engines, and so on. A good document representation is the key point in any successful summarizer. Learning this representation becomes a very active research in natural language processing field (NLP). Traditional approaches mostly fail to deliver a good representation. Word embedding has proved an excellent performance in learning the representation. In this paper, a modified BM25 with Word Embeddings are used to build the sentence vectors from word vectors. The entire document is represented as a set of sentence vectors. Then, the similarity between every pair of sentence vectors is computed. After that, TextRank, a graph-based model, is used to rank the sentences. The summary is generated by picking the top-ranked sentences according to the compression rate. Two well-known datasets, DUC2002 and DUC2004, are used to evaluate the models. The experimental results show that the proposed models perform comprehensively better compared to the state-of-the-art methods.

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Recent Trends in Deep Learning Based Open-Domain Textual Question Answering Systems

IEEE Access ◽

10.1109/access.2020.2988903 ◽

2020 ◽

Vol 8 ◽

pp. 94341-94356

Author(s):

Zhen Huang ◽

Shiyi Xu ◽

Minghao Hu ◽

Xinyi Wang ◽

Jinyan Qiu ◽

...

Keyword(s):

Deep Learning ◽

Question Answering ◽

Open Domain ◽

Question Answering Systems ◽

Recent Trends

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Designing an interactive open-domain question answering system

Natural Language Engineering ◽

10.1017/s1351324908004919 ◽

2009 ◽

Vol 15 (1) ◽

pp. 73-95 ◽

Cited By ~ 33

Author(s):

S. QUARTERONI ◽

S. MANANDHAR

Keyword(s):

Web Search ◽

Question Answering ◽

Management Model ◽

Open Domain ◽

Wizard Of Oz ◽

Question Answering System ◽

Design And Implementation ◽

Web Search Engine ◽

Interactive Question

AbstractInteractive question answering (QA), where a dialogue interface enables follow-up and clarification questions, is a recent although long-advocated field of research. We report on the design and implementation of YourQA, our open-domain, interactive QA system. YourQA relies on a Web search engine to obtain answers to both fact-based and complex questions, such as descriptions and definitions. We describe the dialogue moves and management model making YourQA interactive, and discuss the architecture, implementation and evaluation of its chat-based dialogue interface. Our Wizard-of-Oz study and final evaluation results show how the designed architecture can effectively achieve open-domain, interactive QA.

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Using a Dialogue Manager to Improve Semantic Web Search

International Journal on Semantic Web and Information Systems ◽

10.4018/ijswis.2016010104 ◽

2016 ◽

Vol 12 (1) ◽

pp. 62-78 ◽

Cited By ~ 2

Author(s):

Dora Melo ◽

Irene Pimenta Rodrigues ◽

Vitor Beires Nogueira

Keyword(s):

Semantic Web ◽

Natural Language ◽

Knowledge Base ◽

System Performance ◽

Web Search ◽

Question Answering ◽

Strategies for Improving the Efficacy of Fusion Question Answering Systems

Principles and Applications of Business Intelligence Research ◽

10.4018/978-1-4666-2650-8.ch013 ◽

2012 ◽

pp. 181-198

Author(s):

José Antonio Robles-Flores ◽

Gregory Schymik ◽

Julie Smith-David ◽

Robert St. Louis

Keyword(s):

Search Engines ◽

Web Site ◽

Web Search ◽

Question Answering ◽

Irrelevant Information ◽

Web Pages ◽

Crowd Sourcing ◽

Complete Answer ◽

Question Answering Systems

Web search engines typically retrieve a large number of web pages and overload business analysts with irrelevant information. One approach that has been proposed for overcoming some of these problems is automated Question Answering (QA). This paper describes a case study that was designed to determine the efficacy of QA systems for generating answers to original, fusion, list questions (questions that have not previously been asked and answered, questions for which the answer cannot be found on a single web site, and questions for which the answer is a list of items). Results indicate that QA algorithms are not very good at producing complete answer lists and that searchers are not very good at constructing answer lists from snippets. These findings indicate a need for QA research to focus on crowd sourcing answer lists and improving output format.

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Implementing clarification dialogues in open domain question answering

Natural Language Engineering ◽

10.1017/s1351324905003682 ◽

2005 ◽

Vol 11 (4) ◽

pp. 343-361 ◽

Cited By ~ 12

Author(s):

MARCO DE BONI ◽

SURESH MANANDHAR

Keyword(s):

Question Answering ◽

Open Domain ◽

Question Answering Systems

We examine the implementation of clarification dialogues, a mechanism for ensuring that question answering systems take into account user goals by allowing them to ask series of related questions either by refining or expanding on previous questions with follow-up questions, in the context of open domain Question Answering systems. We develop an algorithm for clarification dialogue recognition through the analysis of collected data on clarification dialogues and examine the importance of clarification dialogue recognition for question answering. The algorithm is evaluated and shown to successfully recognize the start and continuation of clarification dialogues in 94% of cases. We then show the usefulness of the algorithm by demonstrating how the recognition of clarification dialogues can simplify the task of answer retrieval.

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Semantic Web Search Through Natural Language Dialogues

Innovations, Developments, and Applications of Semantic Web and Information Systems - Advances in Web Technologies and Engineering ◽

10.4018/978-1-5225-5042-6.ch012 ◽

2018 ◽

pp. 329-349 ◽

Cited By ~ 2

Author(s):

Dora Melo ◽

Irene Pimenta Rodrigues ◽

Vitor Beires Nogueira

Keyword(s):

Semantic Web ◽

Natural Language ◽

System Performance ◽

Web Search ◽

Question Answering ◽

Small Dimension ◽

User Intentions ◽

Question Answering Systems ◽

Dialogue Manager ◽

User Intervention

The Semantic Web as a knowledge base gives to the Question Answering systems the capabilities needed to go well beyond the usual word matching in the documents and find a more accurate answer, without needing the user intervention to interpret the documents returned. In this chapter, the authors introduce a Dialogue Manager that, throughout the analysis of the question and the type of expected answer, provides accurate answers to the questions posed in Natural Language. The Dialogue Manager not only represents the semantics of the questions but also represents the structure of the discourse, including the user intentions and the questions' context, adding the ability to deal with multiple answers and providing justified answers. The system performance is evaluated by comparing with similar question answering systems. Although the test suite is of small dimension, the results obtained are very promising.

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A new Passage Retrieval Method in Arabic Question Answering Systems

10.21203/rs.3.rs-119562/v1 ◽

2020 ◽

Author(s):

Lana Alsabbagh ◽

Oumayma AlDakkak ◽

Nada Ghneim

Keyword(s):

Query Expansion ◽

Question Answering ◽

Document Retrieval ◽

Open Domain ◽

Retrieval Method ◽

Passage Retrieval ◽

Question Analysis ◽

The Core ◽

Question Answering Systems ◽

Retrieval Phase

Abstract In this paper, we present our approach to improve the performance of open-domain Arabic Question Answering systems. We focus on the passage retrieval phase which aims to retrieve the most related passages to the correct answer. To extract passages that are related to the question, the system passes through three phases: Question Analysis, Document Retrieval and Passage Retrieval. We define the passage as the sentence that ends with a dot ".". In the Question Processing phase, we applied the traditional NLP steps of tokenization, stopwords and unrelated symbols removal, and replacing the question words with their stems. We also applied Query Expansion by adding synonyms to the question words. In the Document Retrieval phase, we used the Vector Space Model (VSM) with TF-IDF vectorizer and cosine similarity. For the Passage Retrieval phase, which is the core of our system, we measured the similarity between passages and the question by a combination of the BM25 ranker and Word Embedding approach. We tested our system on ACRD dataset, which contains 1395 questions in different domains, and the system was able to achieve correct results with a precision of 92.2% and recall of 79.9% in finding the top-3 related passages for the query.

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