The document summarizes research on developing an Arabic question answering system that can answer how and why questions. It outlines the methodology used, which involves question analysis, expansion, document retrieval from an Arabic text corpus, and answer extraction. The system was evaluated on 80 questions (40 how, 40 why) with precision, recall, and F1 score used to measure performance. Results found the system answered why questions with 64% accuracy compared to 56% for how questions, showing promise as one of the first systems to address Arabic causal questions. Future work to increase accuracy is discussed.
Question Focus Recognition in Question Answering Systems Waheeb Ahmed
Question Answering (QA) Systems are systems that attempts to answer questions posed by human in natural
language. As a part of the QA system comes the question processing module. The question processing module serves
several tasks including question classification and focus identification. Question classification and focus identification
play crucial role in Question Answering systems. This paper describes and evaluates the techniques we developed for
answer type detection based on question classification and focus identification in Arabic Question Answering systems.
Question classification helps in providing the type of the expected answer and hence directing the answer extraction
module to apply the proper technique for extracting the answer. While focus identification helps in ranking the
candidate answers. Consequently, that has increased the accuracy of answers produced by the QA system. Question
processing module involves analysing the questions in order to extract the important information for identifying what is
being asked and how to approach answering it, and this is one of the most important components of a QA system.
Therefore, we propose methods for solving two main problems in question analysis, namely question classification and
focus extraction.
Application of hidden markov model in question answering systemsijcsa
By the increase of the volume of the saved information on web, Question Answering (QA) systems have been very important for Information Retrieval (IR). QA systems are a specialized form of information retrieval. Given a collection of documents, a Question Answering system attempts to retrieve correct answers to questions posed in natural language. Web QA system is a sample of QA systems that in this system answers retrieval from web environment doing. In contrast to the databases, the saved information on web does not follow a distinct structure and are not generally defined. Web QA systems is the task of automatically answering a question posed in Natural Language Processing (NLP). NLP techniques are used in applications that make queries to databases, extract information from text, retrieve relevant documents from a collection, translate from one language to another, generate text responses, or recognize spoken words converting them into text. To find the needed information on a mass of the non-structured information we have to use techniques in which the accuracy and retrieval factors are implemented well. In this paper in order to well IR in web environment, The QA system in designed and also implemented based on the Hidden Markov Model (HMM)
Improving Semantic Search Using Query Log AnalysisStuart Wrigley
Despite the attention Semantic Search is continuously gaining, several challenges affecting tool performance and user experience remain unsolved. Among these are: matching user terms with the searchspace, adopting view-based interfaces in the Open Web as well as supporting users while building their queries. This paper proposes an approach to move a step forward towards tackling these challenges by creating models of usage of Linked Data concepts and properties extracted from semantic query logs as a source of collaborative knowledge. We use two sets of query logs from the USEWOD workshops to create our models and show the potential of using them in the mentioned areas.
Evaluating Semantic Search Systems to Identify Future Directions of ResearchStuart Wrigley
Recent work on searching the Semantic Web has yielded a wide range of approaches with respect to the style of input, the underlying search mechanisms and the manner in which results are presented. Each approach has an impact upon the quality of the information retrieved and the user's experience of the search process. This highlights the need for formalised and consistent evaluation to benchmark the coverage, applicability and usability of existing tools and provide indications of future directions for advancement of the state-of-the-art. In this paper, we describe a comprehensive evaluation methodology which addresses both the underlying performance and the subjective usability of a tool. We present the key outcomes of a recently completed international evaluation campaign which adopted this approach and thus identify a number of new requirements for semantic search tools from both the perspective of the underlying technology as well as the user experience.
From TREC to Watson: is open domain question answering a solved problem?Constantin Orasan
The document summarizes a presentation on question answering systems. It begins by providing context on information overload and defining question answering. It then discusses the evolution of QA systems from early databases to today's open-domain systems. The presentation focuses on IBM's Watson system, providing an overview of its unprecedented ability to answer open-domain questions as well as the massive resources required for its development. It concludes by arguing that open-domain QA remains unsolved and that closed-domain, interactive QA may be more practical for real-world applications.
Ontology Based Approach for Semantic Information Retrieval SystemIJTET Journal
Abstract—The Information retrieval system is taking an important role in current search engine which performs searching operation based on keywords which results in an enormous amount of data available to the user, from which user cannot figure out the essential and most important information. This limitation may be overcome by a new web architecture known as the semantic web which overcome the limitation of the keyword based search technique called the conceptual or the semantic search technique. Natural language processing technique is mostly implemented in a QA system for asking user’s questions and several steps are also followed for conversion of questions to the query form for retrieving an exact answer. In conceptual search, search engine interprets the meaning of the user’s query and the relation among the concepts that document contains with respect to a particular domain that produces specific answers instead of showing lists of answers. In this paper, we proposed the ontology based semantic information retrieval system and the Jena semantic web framework in which, the user enters an input query which is parsed by Standford Parser then the triplet extraction algorithm is used. For all input queries, the SPARQL query is formed and further, it is fired on the knowledge base (Ontology) which finds appropriate RDF triples in knowledge base and retrieve the relevant information using the Jena framework.
The Relevance of the Apache Solr Semantic Knowledge GraphTrey Grainger
The Semantic Knowledge Graph is an Apache Solr plugin that can be used to discover and rank the relationships between any arbitrary queries or terms within the search index. It is a relevancy swiss army knife, able to discover related terms and concepts, disambiguate different meanings of terms given their context, cleanup noise in datasets, discover previously unknown relationships between entities across documents and fields, rank lists of keywords based upon conceptual cohesion to reduce noise, summarize documents by extracting their most significant terms, generate recommendations and personalized search, and power numerous other applications involving anomaly detection, significance/relationship discovery, and semantic search. This talk will walk you through how to setup and use this plugin in concert with other open source tools (probabilistic query parser, SolrTextTagger for entity extraction) to parse, interpret, and much more correctly model the true intent of user searches than traditional keyword-based search approaches.
Question Focus Recognition in Question Answering Systems Waheeb Ahmed
Question Answering (QA) Systems are systems that attempts to answer questions posed by human in natural
language. As a part of the QA system comes the question processing module. The question processing module serves
several tasks including question classification and focus identification. Question classification and focus identification
play crucial role in Question Answering systems. This paper describes and evaluates the techniques we developed for
answer type detection based on question classification and focus identification in Arabic Question Answering systems.
Question classification helps in providing the type of the expected answer and hence directing the answer extraction
module to apply the proper technique for extracting the answer. While focus identification helps in ranking the
candidate answers. Consequently, that has increased the accuracy of answers produced by the QA system. Question
processing module involves analysing the questions in order to extract the important information for identifying what is
being asked and how to approach answering it, and this is one of the most important components of a QA system.
Therefore, we propose methods for solving two main problems in question analysis, namely question classification and
focus extraction.
Application of hidden markov model in question answering systemsijcsa
By the increase of the volume of the saved information on web, Question Answering (QA) systems have been very important for Information Retrieval (IR). QA systems are a specialized form of information retrieval. Given a collection of documents, a Question Answering system attempts to retrieve correct answers to questions posed in natural language. Web QA system is a sample of QA systems that in this system answers retrieval from web environment doing. In contrast to the databases, the saved information on web does not follow a distinct structure and are not generally defined. Web QA systems is the task of automatically answering a question posed in Natural Language Processing (NLP). NLP techniques are used in applications that make queries to databases, extract information from text, retrieve relevant documents from a collection, translate from one language to another, generate text responses, or recognize spoken words converting them into text. To find the needed information on a mass of the non-structured information we have to use techniques in which the accuracy and retrieval factors are implemented well. In this paper in order to well IR in web environment, The QA system in designed and also implemented based on the Hidden Markov Model (HMM)
Improving Semantic Search Using Query Log AnalysisStuart Wrigley
Despite the attention Semantic Search is continuously gaining, several challenges affecting tool performance and user experience remain unsolved. Among these are: matching user terms with the searchspace, adopting view-based interfaces in the Open Web as well as supporting users while building their queries. This paper proposes an approach to move a step forward towards tackling these challenges by creating models of usage of Linked Data concepts and properties extracted from semantic query logs as a source of collaborative knowledge. We use two sets of query logs from the USEWOD workshops to create our models and show the potential of using them in the mentioned areas.
Evaluating Semantic Search Systems to Identify Future Directions of ResearchStuart Wrigley
Recent work on searching the Semantic Web has yielded a wide range of approaches with respect to the style of input, the underlying search mechanisms and the manner in which results are presented. Each approach has an impact upon the quality of the information retrieved and the user's experience of the search process. This highlights the need for formalised and consistent evaluation to benchmark the coverage, applicability and usability of existing tools and provide indications of future directions for advancement of the state-of-the-art. In this paper, we describe a comprehensive evaluation methodology which addresses both the underlying performance and the subjective usability of a tool. We present the key outcomes of a recently completed international evaluation campaign which adopted this approach and thus identify a number of new requirements for semantic search tools from both the perspective of the underlying technology as well as the user experience.
From TREC to Watson: is open domain question answering a solved problem?Constantin Orasan
The document summarizes a presentation on question answering systems. It begins by providing context on information overload and defining question answering. It then discusses the evolution of QA systems from early databases to today's open-domain systems. The presentation focuses on IBM's Watson system, providing an overview of its unprecedented ability to answer open-domain questions as well as the massive resources required for its development. It concludes by arguing that open-domain QA remains unsolved and that closed-domain, interactive QA may be more practical for real-world applications.
Ontology Based Approach for Semantic Information Retrieval SystemIJTET Journal
Abstract—The Information retrieval system is taking an important role in current search engine which performs searching operation based on keywords which results in an enormous amount of data available to the user, from which user cannot figure out the essential and most important information. This limitation may be overcome by a new web architecture known as the semantic web which overcome the limitation of the keyword based search technique called the conceptual or the semantic search technique. Natural language processing technique is mostly implemented in a QA system for asking user’s questions and several steps are also followed for conversion of questions to the query form for retrieving an exact answer. In conceptual search, search engine interprets the meaning of the user’s query and the relation among the concepts that document contains with respect to a particular domain that produces specific answers instead of showing lists of answers. In this paper, we proposed the ontology based semantic information retrieval system and the Jena semantic web framework in which, the user enters an input query which is parsed by Standford Parser then the triplet extraction algorithm is used. For all input queries, the SPARQL query is formed and further, it is fired on the knowledge base (Ontology) which finds appropriate RDF triples in knowledge base and retrieve the relevant information using the Jena framework.
The Relevance of the Apache Solr Semantic Knowledge GraphTrey Grainger
The Semantic Knowledge Graph is an Apache Solr plugin that can be used to discover and rank the relationships between any arbitrary queries or terms within the search index. It is a relevancy swiss army knife, able to discover related terms and concepts, disambiguate different meanings of terms given their context, cleanup noise in datasets, discover previously unknown relationships between entities across documents and fields, rank lists of keywords based upon conceptual cohesion to reduce noise, summarize documents by extracting their most significant terms, generate recommendations and personalized search, and power numerous other applications involving anomaly detection, significance/relationship discovery, and semantic search. This talk will walk you through how to setup and use this plugin in concert with other open source tools (probabilistic query parser, SolrTextTagger for entity extraction) to parse, interpret, and much more correctly model the true intent of user searches than traditional keyword-based search approaches.
Dokumen tersebut membahas rencana pengembangan jaringan bus di Jakarta untuk meningkatkan layanan transportasi umum. Rencananya meliputi integrasi dan perluasan jaringan BRT serta penyesuaian jaringan dan ukuran kendaraan non-BRT berdasarkan pola permintaan penumpang yang berubah. Hal ini bertujuan meningkatkan efisiensi dengan mengurangi kelebihan kendaraan dan meningkatkan pemanfaatan kendaraan berkapasitas besar.
1) O documento descreve o trabalho de pesquisa realizado por Franklin Cascaes sobre as manifestações culturais da Ilha de Santa Catarina, especialmente lendas e crendices.
2) Franklin Cascaes estudou por 30 anos a cultura dos descendentes de açorianos e registrou suas descobertas no livro "O Fantástico na Ilha de Santa Catarina".
3) O livro contém 24 contos que recriam lendas locais misturando elementos reais e fantásticos, com foco nas histórias de bruxas segundo a tradição oral
El nitrógeno atmosférico es absorbido por las plantas y convertido en compuestos orgánicos a través de la fotosíntesis. Luego de la muerte y descomposición de plantas y animales, las bacterias descomponedoras convierten el nitrógeno orgánico en amoníaco y nitratos, que son absorbidos por otras plantas gracias a las bacterias fijadoras de nitrógeno.
Pleasurable Fayre include solutions to get a productive camping out holiday break in england as well as European union. Our own array of inexpensive camping out equipment involves high quality tents along with asleep carriers.
O documento discute como o handebol pode melhorar a sociedade. Ele descreve como o esporte pode ser usado para motivar jovens e adultos e melhorar sua qualidade de vida, mantendo-os longe do uso de drogas. Também discute como o handebol pode assumir um lugar significativo na sociedade.
Notre brochure pour la réalisation de vidéos au Gabon à Port-Gentil et Libreville avec une description des avantages.
Nos réalisations sont effectués par l'équipe de Créative World Gabon.
El documento presenta dos trabajos realizados en la Feria de las Artes y las Ciencias. El trabajo de arte consistió en la obra "Juanito dormido" de Antonio Berni, donde se explica el proceso creativo y el uso de materiales de desecho. Los experimentos con agua incluyeron pruebas sobre la flotación de objetos en agua salada y dulce, y cómo el peso y la forma afectan si un objeto flota o se hunde.
Dự án phòng khám lưu động cho cán bộ công nhân viênThaoNguyenXanh2
Tư vấn lập dự án: http://www.lapduan.com.vn/
Tư vấn môi trường: http://thaonguyenxanhgroup.com/
Liên Hệ:
ÔNG TY MÔI TRƯỜNG THẢO NGUYÊN XANH
Trụ sở: 158 Nguyễn Văn Thủ, P. Đakao, Quận 1, Hồ Chí Minh
Hotline: 0839118552 - 0918755356
Fax: 0839118579
Dự án phòng khám lưu động cho cán bộ công nhân viên
Dự án phòng khám lưu động cho cán bộ công nhân viên
Dự án phòng khám lưu động cho cán bộ công nhân viên
Este documento presenta varias gráficas sobre el rendimiento de producción, peso de huevo y peso corporal de las gallinas ponedoras marrones Nick a lo largo de 79 semanas de producción. La guía proporciona información sobre la administración de estas gallinas para lograr el éxito.
This document provides information on the STAF and STAF-SG balancing valves including:
- They are flanged cast iron or ductile iron valves used for balancing, pre-setting, measuring, and shutting off flow in heating and cooling systems.
- Key features include an accurate handwheel with digital readout and self-sealing measuring points.
- They are available in sizes from DN 20-400 and pressure classes PN 16 and PN 25 with maximum working temperatures up to 120°C.
- Detailed specifications, diagrams, and technical descriptions are given to size the valves for different flow rates and pressure drops.
The document provides a summary of global and Indian stock market activity as well as news from several Indian companies. In the US, stock markets rose as investors expected pro-business policies from President-elect Trump. Japanese shares also rose on a weaker yen. In India, the equity market is expected to open flat to positive tracking Asian markets. Several companies including GMR, Tatas, ONGC, Cairn India, Balrampur Chini, and HDFC made announcements. Technical recommendations were given to buy two stocks, Snowman Logistics and Prism Cement.
Techniques For Deep Query UnderstandingAbhay Prakash
The document summarizes techniques for deep query understanding in search systems. It discusses query understanding, which involves understanding a user's information need from their query. This allows for query correction, suggestion, expansion, classification and semantic tagging. Query correction reformulates ill-formed queries. Query suggestion provides similar queries. Query expansion adds synonyms to broaden results. Query classification determines the intent or topic of the query. Semantic tagging identifies entities in the query. The document outlines various models for these techniques, including using contextual information and graph representations of search logs.
DBPEDIA BASED FACTOID QUESTION ANSWERING SYSTEMIJwest
The document describes a factoid question answering system called SELNI that is based on the DBpedia knowledge base. It discusses the system's architecture which involves three main steps: 1) question classification and generating decision models using machine learning, 2) question processing to extract resources and keywords from the question, and 3) formulating and executing SPARQL queries on DBpedia to obtain answers. It also provides details on using support vector machines for question classification and generating models to determine the answer type for a given question. The system aims to answer simple factual questions by utilizing the structured data in DBpedia.
Dokumen tersebut membahas rencana pengembangan jaringan bus di Jakarta untuk meningkatkan layanan transportasi umum. Rencananya meliputi integrasi dan perluasan jaringan BRT serta penyesuaian jaringan dan ukuran kendaraan non-BRT berdasarkan pola permintaan penumpang yang berubah. Hal ini bertujuan meningkatkan efisiensi dengan mengurangi kelebihan kendaraan dan meningkatkan pemanfaatan kendaraan berkapasitas besar.
1) O documento descreve o trabalho de pesquisa realizado por Franklin Cascaes sobre as manifestações culturais da Ilha de Santa Catarina, especialmente lendas e crendices.
2) Franklin Cascaes estudou por 30 anos a cultura dos descendentes de açorianos e registrou suas descobertas no livro "O Fantástico na Ilha de Santa Catarina".
3) O livro contém 24 contos que recriam lendas locais misturando elementos reais e fantásticos, com foco nas histórias de bruxas segundo a tradição oral
El nitrógeno atmosférico es absorbido por las plantas y convertido en compuestos orgánicos a través de la fotosíntesis. Luego de la muerte y descomposición de plantas y animales, las bacterias descomponedoras convierten el nitrógeno orgánico en amoníaco y nitratos, que son absorbidos por otras plantas gracias a las bacterias fijadoras de nitrógeno.
Pleasurable Fayre include solutions to get a productive camping out holiday break in england as well as European union. Our own array of inexpensive camping out equipment involves high quality tents along with asleep carriers.
O documento discute como o handebol pode melhorar a sociedade. Ele descreve como o esporte pode ser usado para motivar jovens e adultos e melhorar sua qualidade de vida, mantendo-os longe do uso de drogas. Também discute como o handebol pode assumir um lugar significativo na sociedade.
Notre brochure pour la réalisation de vidéos au Gabon à Port-Gentil et Libreville avec une description des avantages.
Nos réalisations sont effectués par l'équipe de Créative World Gabon.
El documento presenta dos trabajos realizados en la Feria de las Artes y las Ciencias. El trabajo de arte consistió en la obra "Juanito dormido" de Antonio Berni, donde se explica el proceso creativo y el uso de materiales de desecho. Los experimentos con agua incluyeron pruebas sobre la flotación de objetos en agua salada y dulce, y cómo el peso y la forma afectan si un objeto flota o se hunde.
Dự án phòng khám lưu động cho cán bộ công nhân viênThaoNguyenXanh2
Tư vấn lập dự án: http://www.lapduan.com.vn/
Tư vấn môi trường: http://thaonguyenxanhgroup.com/
Liên Hệ:
ÔNG TY MÔI TRƯỜNG THẢO NGUYÊN XANH
Trụ sở: 158 Nguyễn Văn Thủ, P. Đakao, Quận 1, Hồ Chí Minh
Hotline: 0839118552 - 0918755356
Fax: 0839118579
Dự án phòng khám lưu động cho cán bộ công nhân viên
Dự án phòng khám lưu động cho cán bộ công nhân viên
Dự án phòng khám lưu động cho cán bộ công nhân viên
Este documento presenta varias gráficas sobre el rendimiento de producción, peso de huevo y peso corporal de las gallinas ponedoras marrones Nick a lo largo de 79 semanas de producción. La guía proporciona información sobre la administración de estas gallinas para lograr el éxito.
This document provides information on the STAF and STAF-SG balancing valves including:
- They are flanged cast iron or ductile iron valves used for balancing, pre-setting, measuring, and shutting off flow in heating and cooling systems.
- Key features include an accurate handwheel with digital readout and self-sealing measuring points.
- They are available in sizes from DN 20-400 and pressure classes PN 16 and PN 25 with maximum working temperatures up to 120°C.
- Detailed specifications, diagrams, and technical descriptions are given to size the valves for different flow rates and pressure drops.
The document provides a summary of global and Indian stock market activity as well as news from several Indian companies. In the US, stock markets rose as investors expected pro-business policies from President-elect Trump. Japanese shares also rose on a weaker yen. In India, the equity market is expected to open flat to positive tracking Asian markets. Several companies including GMR, Tatas, ONGC, Cairn India, Balrampur Chini, and HDFC made announcements. Technical recommendations were given to buy two stocks, Snowman Logistics and Prism Cement.
Techniques For Deep Query UnderstandingAbhay Prakash
The document summarizes techniques for deep query understanding in search systems. It discusses query understanding, which involves understanding a user's information need from their query. This allows for query correction, suggestion, expansion, classification and semantic tagging. Query correction reformulates ill-formed queries. Query suggestion provides similar queries. Query expansion adds synonyms to broaden results. Query classification determines the intent or topic of the query. Semantic tagging identifies entities in the query. The document outlines various models for these techniques, including using contextual information and graph representations of search logs.
DBPEDIA BASED FACTOID QUESTION ANSWERING SYSTEMIJwest
The document describes a factoid question answering system called SELNI that is based on the DBpedia knowledge base. It discusses the system's architecture which involves three main steps: 1) question classification and generating decision models using machine learning, 2) question processing to extract resources and keywords from the question, and 3) formulating and executing SPARQL queries on DBpedia to obtain answers. It also provides details on using support vector machines for question classification and generating models to determine the answer type for a given question. The system aims to answer simple factual questions by utilizing the structured data in DBpedia.
In this research work we have develop a new scoring mathematical model that works on the five types of questions. The question text failures are first extracted and a score is found based on its structure with respect to its template structure and then answer score is calculated again the question as well as paragraph. Text to finally reach at the index of the most probable answer with respect to question.
Répondre à la question automatique avec le webAhmed Hammami
This document summarizes an automatic question answering system that goes beyond answering simple factual questions. The system is trained on a corpus of 1 million question/answer pairs collected from frequently asked question pages on the web. It uses statistical models like a question chunker, answer/question translation model, and answer language model. The evaluation shows the system achieves reasonable performance on a variety of complex, non-factual questions by leveraging large web collections to find answers rather than assuming answers are short facts.
Architecture of an ontology based domain-specific natural language question a...IJwest
The document summarizes the architecture of an ontology-based domain-specific natural language question answering system. The proposed architecture defines four main modules: 1) question processing which analyzes and classifies questions and reformulates queries, 2) document retrieval which retrieves relevant documents, 3) document processing which processes retrieved documents, and 4) answer extraction which extracts and generates responses. Natural language processing techniques and ontologies are used to analyze questions and documents and extract relationships and answers. The system aims to generate concise, specific answers to natural language questions in a given domain and achieved 94% accuracy in testing.
Open domain question answering system using semantic role labelingeSAT Publishing House
1. The document describes a proposed open domain question answering system that uses semantic role labeling to extract answers from documents retrieved from the web.
2. The system consists of three modules: question processing, document retrieval, and answer extraction. Semantic role labeling is used in the answer extraction module to identify answers based on the question type.
3. An evaluation of the proposed system showed it achieved higher accuracy compared to a baseline system using only pattern matching for answer extraction.
Development and evaluation of a web based question answering system for arabi...csandit
Question Answering (QA) systems are gaining great importance due to the increasing amount of
web content and the high demand for digital information that regular information retrieval
techniques cannot satisfy. A question answering system enables users to have a natural
language dialog with the machine, which is required for virtually all emerging online service
systems on the Internet. The need for such systems is higher in the context of the Arabic
language. This is because of the scarcity of Arabic QA systems, which can be attributed to the
great challenges they present to the research community, including the particularities of Arabic,
such as short vowels, absence of capital letters, complex morphology, etc. In this paper, we
report the design and implementation of an Arabic web-based question answering system, which
we called “JAWEB”, the Arabic word for the verb “answer”. Unlike all Arabic questionanswering
systems, JAWEB is a web-based application, so it can be accessed at any time and
from anywhere. Evaluating JAWEB showed when compared to ask.com, the well-established
web-based QA system, JAWEB provided 15-20% higher recall. These promising results give
clear evidence that JAWEB has great potential as a QA platform and is much needed by Arabicspeaking
Internet users across the world.
Diacritic Oriented Arabic Information Retrieval SystemCSCJournals
Arabic language support in search engines and operating systems is improved in recent years. Searching in the Internet is reliable and can be compared to the excellent support for several other languages, including English. However, for text with diacritics there are some limitations. For this reason, most Information retrieval (IR) systems remove diacritics from text and ignore it for its complexity. Searching text with diacritics is important for some kinds of documents, such as those of religious books, some newspapers and children stories. This research shows the design and development of the system that overcome the problem. The proposed system considers diacritics. The proposed system includes the design complexity in the retrieving algorithm rather than the information repository, which is database in this study. Also, this study analyses the results and the performance. Results are promising and performance analysis shows methods to enhance design and increase the performance. The proposed system can be integrated in search engines, text editors and any information retrieval system that include Arabic text. Performance analysis of the proposed system shows that this system is reliable. The proposed system is applied on database of Hadeeth, which is religious book includes the prophet action and statements. The system can be applied in any kind of data repository.
Car-Following Parameters by Means of Cellular Automata in the Case of EvacuationCSCJournals
This study is attention to the car-following model, an important part in the micro traffic flow. Different from Nagel–Schreckenberg’s studies in which car-following model without agent drivers and diligent ones, agent drivers and diligent ones are proposed in the car-following part in this work and lane-changing is also presented in the model. The impact of agent drivers and diligent ones under certain circumstances such as in the case of evacuation is considered. Based on simulation results, the relations between evacuation time and diligent drivers are obtained by using different amounts of agent drivers; comparison between previous (Nagel–Schreckenberg) and proposed model is also found in order to find the evacuation time. Besides, the effectiveness of reduction the evacuation time is presented for various agent drivers and diligent ones.
This document discusses various techniques for question answering and relation extraction in natural language processing. It provides an overview of question answering systems and approaches, including examples like START, Ask Jeeves and Siri. It also discusses using search engines for question answering, relation extraction from questions, and common evaluation metrics for question answering systems like accuracy and mean reciprocal rank.
This document summarizes two Arabic question answering systems: QASAL and QARAB. It describes the main components of each system, including question analysis, passage retrieval, and answer extraction. It also discusses how each system handles yes/no questions in Arabic. The document concludes by comparing the performance of the two systems and different techniques for Arabic question answering.
A Review on Neural Network Question Answering Systemsijaia
This document provides a review of recent research on neural network question answering systems. It identifies three main research directions for these systems: knowledge base question answering, visual question answering, and community question answering. For each direction, it discusses common research topics and challenges addressed. It also summarizes solutions proposed for some of the main challenges, such as using neural networks to measure similarity between questions and potential answers in order to select the best answer. The document aims to give researchers an overview of the current state of neural network question answering.
Developemnt and evaluation of a web based question answering system for arabi...ijnlc
Question Answering (QA) systems are gaining great importance due to the increasing amount of web
content and the high demand for digital information that regular information retrieval techniques cannot
satisfy. A question answering system enables users to have a natural language dialog with the machine,
which is required for virtually all emerging online service systems on the Internet. The need for such
systems is higher in the context of the Arabic language. This is because of the scarcity of Arabic QA
systems, which can be attributed to the great challenges they present to the research community,including
theparticularities of Arabic, such as short vowels, absence of capital letters, complex morphology, etc. In
this paper, we report the design and implementation of an Arabic web-based question answering
system,which we called “JAWEB”, the Arabic word for the verb “answer”. Unlike all Arabic questionanswering
systems, JAWEB is a web-based application,so it can be accessed at any time and from
anywhere. Evaluating JAWEBshowed that it gives the correct answer with 100% recall and 80% precision
on average. When comparedto ask.com, the well-established web-based QA system, JAWEBprovided 15-
20% higher recall.These promising results give clear evidence that JAWEB has great potential as a QA
platform and is much needed by Arabic-speaking Internet users across the world.
QUESTION ANALYSIS FOR ARABIC QUESTION ANSWERING SYSTEMS ijnlc
The first step of processing a question in Question Answering(QA) Systems is to carry out a detailed analysis of the question for the purpose of determining what it is asking for and how to perfectly approach answering it. Our Question analysis uses several techniques to analyze any question given in natural language: a Stanford POS Tagger & parser for Arabic language, a named entity recognizer, tokenizer,
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Question Classification using Semantic, Syntactic and Lexical featuresIJwest
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Answer extraction and passage retrieval forWaheeb Ahmed
—Question Answering systems (QASs) do the task of
retrieving text portions from a collection of documents that
contain the answer to the user’s questions. These QASs use a
variety of linguistic tools that be able to deal with small
fragments of text. Therefore, to retrieve the documents which
contains the answer from a large document collections, QASs
employ Information Retrieval (IR) techniques to minimize the
number of documents collections to a treatable amount of
relevant text. In this paper, we propose a model for passage
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the purpose of Arabic QASs. We first segment each the top five
ranked documents returned by the IR module into passages.
Then, we compute the similarity score between the user’s
question terms and each passage. The top five passages (with
high similarity score) are retrieved are retrieved. Finally,
Answer Extraction techniques are applied to extract the final
answer. Our method achieved an average for precision of
87.25%, Recall of 86.2% and F1-measure of 87%.
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Answer Extraction for how and why Questions in Question Answering Systems
1. ISSN (e): 2250 – 3005 || Volume, 06 || Issue, 12|| December – 2016 ||
International Journal of Computational Engineering Research (IJCER)
www.ijceronline.com Open Access Journal Page 18
Answer Extraction for how and why Questions in
Question Answering Systems
Waheeb Ahmed1,
Dr.BabuAnto P2
1
Research Scholar, Department of Information Technology, Kannur University, Kerala, India,
2
Associate Professor, Department of Information Technology, Kannur University, Kerala, India
I. INTRODUCTION
Question Answering is a popular application of Natural language processing. It is concerned with building
systems that accepts questions given in natural language by humans and tries to produce the required answer.
This field is emerged due to the high demand for systems that accept a question from user in natural language
rather than a set of keywords and consequently supply a concise answer. Traditional search engines like Google
and Yahoo usually return a list of links [1]. However, they do not give specific answers to users. It is the task of
the user to look for the answer in these links by browsing them and searching for it and this may consume a
considerable amount of time. Recently, both of the information growth and the high demand for an efficient
access to information has increased the motivation of research in QASs[2].
1.1 Categories of Questions
The research in QA deals with a variety of questions including:
Factual: Questions that ask for factual information [who, what, where, when].This type of questions require
a short answer in the form of a single word or phrase. e.g. “Who invented the Piano?”(الثٍاًى؟ اختشع هي)
Definition: Questions that looks for definition of a term. e.g.”What is Geoinformatics?”( الوعلىهاخ ًظن ًه ها
الجغشافٍح؟)
Listing: Questions that requirelists of facts or entities. e.g. “List the action movies of 2016?”( األكشي أفالم اركش
لعام2016؟ )
Causal questions[why,how]: Questions that seek for explanations about an entity.e.g. “How can we measure
the speed of light?”(لضىء؟ سشعح ًقٍس كٍف)
Yes/No questions: Questions that require a yes/no answer. e.g. “Does the water have color?”(لىى؟ للواء هل)
QASs are classified into two domains depending on the source of information from which the QA returns the
answer: open domain and closed domain. Open domain QASs return the answer from the web and they are not
restricted to a specific field of knowledge. In contrary, closed domain QASs retrieves the answer from a
database or knowledge base which is limited to a specific field or area like Medicine, Biology, Weather
forecasting etc. Many QAs has been developed for answering factoid questions like who, what, where and
ABSTRACT
With the increasing amount of Arabic text on the web and in the information repositories and the
demand of users to have specific answers to their questions, the need for Question Answering (QA)
Systems became a necessity. Our Question Answering System answers two types of Questions: How
and Why Questions. The system takes a question given in natural language expressed in the Arabic
language and attempts to produce concise answers. The system's main source of knowledge is a
collection of Arabic text documents extracted from the Arabic Wikipedia. The reasons behind
developing this system is due to the absence of Arabic Questions Answering Systems(QASs) which
deals with How and Why questions and this is because of the complexity of extracting the answers
that satisfy this type of questions. Information Retrieval (IR) module is used to retrieve the target
document from the corpus. The IR is coupled with Natural Language (NLP) Tools to process the
given question and to extract the answer. The major goal of the proposed system is to extract the
passage which is likely to contain the answer based on the semantic similarity between question
keywords and the sentences of the passage. We used Precision, Recall and F1 Measure to calculate
the accuracy of the system.
Keywords:Answer Extraction, Artificial Intelligence, Information Retrieval, Information
Extraction, Natural Language Computing,Question Answering System, Question Analysis.
2. Answer Extraction for how and why Questions in Question Answering Systems
www.ijceronline.com Open Access Journal Page 19
when. However, questions like how and why that need descriptive answers need complex processing.
Answering How and Why questions is considered hard since these questions may need long answers.
1.2 Arabic Language Challenges
There are several challenges posed by the Arabic language which makes Arabic language processing a hard
task[3][4]:
Morphological complexity
Lack of basic NLP tools for processing the language like (morphological analyzers, information extraction
tools) and lack of other linguistic resources like specialized dictionaries,corpora,lexicon etc.
Highly inflectional and highly derivational. This means the same context may appear in several forms,
which impose the need for a huge corpus in order to get a representative frequency of all the forms in which
a context might appear or to make a solution to minimize the number of these forms into a smaller one.
The direction of writing is from Right-To-Left and a group of its letters change their forms according to
their position/appearance in the word.
Ambiguity where the same word has different meanings.Lack of capitalization that makes it difficult to extract
named entities.The above challenges slowed down the development of Arabic QASs especially for questions
which requires explanations as answers like How and Why questions.
II. RELATED WORK
AQAS is knowledge-based system which returns answers from structured data but not from plain text
(unstructured text). AQAS tries to answer simple factoid questions like Who, What, Where and
When[5];Besides that no results for their system are reported. QARAB is a closed domain simple factoid
question answering that answers questions like Who, Whom, When, What, Where but it does not address How
and Why questions and the corpus consists of documents which are extracted from a newspaper called the Al-
Raya published in Qatar[6].QASAL is a QA system for Arabic language for answering factoid questions. It is
built on the NooJ platform[7], and no experimental results or performance has been published for this system
[8].Bdour and Gharaibeh developed a system for Yes/No questions only [9].Our proposed work concentrates
onprocessing and answering causal questions [How(كٍف), Why(لوارا)] for Arabic language.
III. METHODOLOGY
We used natural language tools for processing the question and IR module using the term frequency-inverse
document frequency(tf-idf) weighing for retrieving the relevant documents from the corpus. Our corpus consists
of 500 documents extracted from the Arabic Wikipedia. The question set consists of 80 questions which is
divided into two sets: one set consist of 40 How questions and the other set consists of 40 Why questions. The
user will supply a question in Natural Language to the QA system. The QAS will process the question and
deliver the answer. The following steps are performed to analyze the given question and retrieve the candidate
answer:
1. Question Analysis.
2. Question Expansion.
3. Document Retrieval.
4. Answer Extraction.
3.1 Question Analysis
The question analysis phase consists of three steps:
1. Question classification.
2. Tokenization
3. Identification of Question Focus.
Question Classification:Question Classification seeks identifying what the question is looking for. If a question
starts with Why( لوارا ), then the question is classified as REASON. That is, the question is looking for reason.
For example, (الٌهاس؟ أثٌاء صسقاء السواء تثذوا لوارا) “Why does the sky look blue during day?”
The question is classified as REASON. If the question starts with How(كٍف), it is classified as MANNER. That
is, the question is seeking an answer of type MANNER. The main purpose of classifying the question is that this
information(Question Class either MANNER or REASON) will be sent to the Answer Extraction(AE) module
to extract the proper answer from the retrieved document.
Tokenization: The question is tokenized into individual tokens and these tokens are stored in a list. Stop-words
are removed. Stop-words are words that appears very frequently and have less important meaning like
prepositions and conjunctions(in, from, to, about, on , and, or)( أو ، و ، على ، عي ، الى ،هي).These words are
removed from the question. After that, a chunker is used to get the named entities and noun phrases. For
3. Answer Extraction for how and why Questions in Question Answering Systems
www.ijceronline.com Open Access Journal Page 20
example: "Why did the Egyptian scientist “Ahmed Zewail” become famous?(” صوٌل أحوذ الوصشي العالن أصثح لوارا
هشهىسا؟”). We have developed a simple rule-based the named entities based on the output of Stanford Part-Of-
Speech (POS) Tagger for Arabic language. The chunker will extract “Ahmed Zewail”( صوٌل أحوذ) as a named
entity.The list of keywords after tokenization and chunking [“Ahmed Zewail”, “Egyptian”, “scientist”,
“become”, “famous”]. That is, [“صوٌل أحوذ”,”الوصشي ”,”العالن ”, “أصثح”, “هشهىسا”].
Identification of Question Focus: Question focus is a word or a phrase extracted from the question that helps
in identifying the type of the expected answer. The question class along with the question focus will benefit the
AE module in ranking the candidate answers. For example, the question ( األدب ًف ًىتل جائضج هحفىظ ًجٍة هٌح لوارا
1988)“Why was Naguib Mahfouz awarded the Noble Prize in Literature 1988?”. The focus of this question is
looking for something related to “Naguib Mahfouz”. The focus here is the Noun Phrase(NP) “the Noble Prize
in Literature”( األدب ًف ًىتل جائضج) and this is done using the chunker. The answer type in figure-1 is the defined
by the combination of the question classification and the question focus.
The flow of our QA system is shown in the following figure:
Figure1.QA Architecture
3.2 Question Expansion
In question expansion alternative synonyms for some keywords in the question(verbs and adjectives) are used.
We used Arabic WordNet(AWN)[10] ( available as open source software) to extract the synonyms for the verbs
and adjectives in the question. The reason for question expansion is that the same verb/adjective in the question
may not be available in the answer. So, we have to expand the question by adding synonyms for some words in
the question. These synonyms are fed into the list of question terms that will be sent to the IR module and this
will increase the chance of getting the answer. For example, ( الطٍىس ًٌتغ لوارا؟ ) “Why do birds sing?” The
synonyms for (ًٌغُت/sing) include (غشدُت, ثلثلُت) are added to the question keywords list.
3.3 Documents Retrieval
We used Vector Space Model for developing our IR module for retrieving the relevant documents from
ArabicWikipedia corpus. Vector Space Model is an algebraic model that represents query strings and text
documents as vectors [11]. After getting the available named entities and the noun phrases and other keywords
extracted from the question, these extracted keywords are received by the IR module which search for them in
the index to retrieve the relevant document which contains all or most of the question keywords.
3.4 Answer Extraction
Our proposed method for extracting the answer from the top ranked document retrieved by the IR module is
implemented in the following procedures:
4. Answer Extraction for how and why Questions in Question Answering Systems
www.ijceronline.com Open Access Journal Page 21
1. If the question class is REASON. The keywords [(because, due to , reason) لزلك,لهزا,تسثة,ألى,ألًه ] are added to
the list of question keywords. If the question class is MANNER, the keywords [(by, using) تاستخذام,تىاسطح,عي
طشٌق] are added to the list of question keywords.
2. The top ranked document which is retrieved by the IR module is divided into passages at the discourse
level.
3. Passage which contains the question focus is given weight=1 and passages that do not contain the question
focus is given weight=0.
4. Cosine similarity between the question and every sentence in the passage is calculated using the following
formula:
A=Sum( ), B=Sum( ) , C=Sum( )
Where,
qi is representing the tf-idf of the term i in the question.
si is the tf-idf of the term i in the sentence.
5. Total similarity between the question and every sentence S in the passage p is calculated by
S(p)=S1+S2+…+Sn+weight
6. S(p) is calculated using the equation in step 4 for all passages.
7. The passage with the highest S(p) score is extracted as answer and presented to the user.
IV. RESULTS AND PERFORMANCE EVALUATION
There are many evaluation metrics that are used for evaluating question QA systems. The following metrics are
used inText Retrieval Conference(TREC-8) project: Precision, Recall and F-measure. Where,
Precision=
Recall = .
F measure is the combination of the precision and recall with equal weight given to both of them:
F1 measure = [12].
The above measures are the common measures used for evaluating any QA system including TREC project
series and many other question answering systems on different languages in the literature.
Table 1.Experiment results for our QAS
Figure 2. Distribution of accuracy of the QAS for HOW & WHY Questions
5. Answer Extraction for how and why Questions in Question Answering Systems
www.ijceronline.com Open Access Journal Page 22
The obtained Precision of the system for total 40 How questions is 61% and the Recall is 52%. The F1 measure
is 56%.For the total 40 Why questions the obtained precision is 67% and the Recall is 62%. The F1 measure is
64%. The performance of the QAS for answering the Why questions was 64% which is higher than the result
got for the How questions by 8%. The result is promising and it is the first system that deals with Arabic How &
Why questions comparing to the literature on Arabic QASs[5][6][8][9].
V. CONCLUSION
Our QAS attempts to answer Arabic Why and How) questions. The proposed system uses NLP tools for
question analysis and IR for document retrieval. The process of retrieving the candidate passage which is likely
to contain the answer is done by computing the similarity between the How/Why question and the sentences in
all the passages in the retrieved document. Passage with the highest score is extracted and presented to the user.
This system is the first attempt to answer complex how & why questions. As a future work more features will be
used to increase the system accuracy.
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