Question answering method for infrastructure damage information retrieval from textual data using bidirectional encoder representations from transformers

Knowledge Graphs are applied in many fields such as search engines, semantic analysis, and question answering in recent years. However, there are many obstacles for building knowledge graphs as methodologies, data and tools. This paper introduces a novel methodology to build knowledge graph from heterogeneous documents. We use the methodologies of Natural Language Processing and deep learning to build this graph. The knowledge graph can use in Question answering systems and Information retrieval especially in Computing domain

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LIS4: Lesk Inspired Sense Specific Semantic Similarity using WordNet

Journal of Information & Knowledge Management ◽

10.1142/s0219649221500064 ◽

2021 ◽

pp. 2150006

Author(s):

Saravanakumar Kandasamy ◽

Aswani Kumar Cherukuri

Keyword(s):

Information Retrieval ◽

Natural Language Processing ◽

Natural Language ◽

Semantic Similarity ◽

Language Processing ◽

Gold Standard ◽

Question Answering ◽

Knowledge Based ◽

Benchmark Datasets ◽

Processing Information

Semantic similarity quantification between concepts is one of the inevitable parts in domains like Natural Language Processing, Information Retrieval, Question Answering, etc. to understand the text and their relationships better. Last few decades, many measures have been proposed by incorporating various corpus-based and knowledge-based resources. WordNet and Wikipedia are two of the Knowledge-based resources. The contribution of WordNet in the above said domain is enormous due to its richness in defining a word and all of its relationship with others. In this paper, we proposed an approach to quantify the similarity between concepts that exploits the synsets and the gloss definitions of different concepts using WordNet. Our method considers the gloss definitions, contextual words that are helping in defining a word, synsets of contextual word and the confidence of occurrence of a word in other word’s definition for calculating the similarity. The evaluation based on different gold standard benchmark datasets shows the efficiency of our system in comparison with other existing taxonomical and definitional measures.

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Information Retrieval and Question Answering for Assisting Readers of the Late Antique to Medieval Corpora of the Aggadic Midrash

Language, Culture, Computation. Computational Linguistics and Linguistics - Lecture Notes in Computer Science ◽

10.1007/978-3-642-45327-4_6 ◽

2014 ◽

pp. 82-102 ◽

Cited By ~ 1

Author(s):

Yaakov HaCohen-Kerner ◽

Ephraim Nissan

Keyword(s):

Information Retrieval ◽

Question Answering ◽

Late Antique

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Analysis and Assessment of Cross-Language Question Answering Systems

Encyclopedia of Information Science and Technology, Fourth Edition ◽

10.4018/978-1-5225-2255-3.ch388 ◽

2018 ◽

pp. 4471-4479

Author(s):

Juncal Gutiérrez-Artacho ◽

María-Dolores Olvera-Lobo

Keyword(s):

Information Retrieval ◽

Applied Research ◽

Question Answering ◽

General Domain ◽

Linguistic Resources ◽

Question Answering Systems ◽

Cross Language ◽

Language Question ◽

The Web

Within the sphere of the Web, the overload of information is more notable than in other contexts. Question answering systems (QAS) are presented as an alternative to the traditional Information Retrieval (IR) systems, seeking to offer precise and understandable answers to factual questions instead of showing the user a list of documents related to a given search . Given that the QAS is presented as a substantial advance in the improvement of IR, it becomes necessary to determine its effectiveness for the final user. With this aim, 7 studies were undertaken to evaluate: a) in the first two, the linguistic resources and tools used in these systems for multilingual retrieval (Research 1; Research 2); and b) the performance and quality of the answers of the main monolingual and multilingual QA of general domain and specialized domain in the Web in response to different types of questions and subjects, so that different evaluation means can be applied (Research 3, Research 4, Research 5, Research 6, Research 7).

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Analysis and Assessment of Cross-Language Question Answering Systems

Advances in Library and Information Science - Advanced Methodologies and Technologies in Library Science, Information Management, and Scholarly Inquiry ◽

10.4018/978-1-5225-7659-4.ch018 ◽

2019 ◽

pp. 226-236

Author(s):

Juncal Gutiérrez-Artacho ◽

María-Dolores Olvera-Lobo

Keyword(s):

Information Retrieval ◽

Applied Research ◽

Question Answering ◽

General Domain ◽

Linguistic Resources ◽

Question Answering Systems ◽

Cross Language ◽

Language Question ◽

The Web

Within the sphere of the web, the overload of information is more notable than in other contexts. Question answering systems (QAS) are presented as an alternative to the traditional information retrieval (IR) systems seeking to offer precise and understandable answers to factual questions instead of showing the user a list of documents related to a given search. Given that the QAS is presented as a substantial advance in the improvement of IR, it becomes necessary to determine its effectiveness for the final user. With this aim, seven studies were undertaken to evaluate: 1) in the first two, the linguistic resources and tools used in these systems for multilingual retrieval (Research 1, Research 2), and 2) the performance and quality of the answers of the main monolingual and multilingual QA of general domain and specialized domain in the web in response to different types of questions and subjects, so that different evaluation means can be applied (Research 3, Research 4, Research 5, Research 6, Research 7).

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