An Algorithm based on Semantic Similarity to Extract Candidate Answers in Question Answering Systems

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Ghaderi Seyed Himan; Ghafouri Arash; Naderi Hassan

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Seminar Paper

Paper Information

Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2024 | Issue:10

Download Full-Text

View:

Download:

Information Seminar Paper

Title

An Algorithm based on Semantic Similarity to Extract Candidate Answers in Question Answering Systems

Author(s)

Ghaderi Seyed Himan | Ghafouri Arash | Naderi Hassan

Keywords

Information Retrieval

Text Mining Techniques

Multi-Hop Question Answering

Deep Neural Networks

Abstract

Answering complex questions based on multi-hop reasoning on textual data requires retrieving different documents. The main challenge in answering complex questions is the existence of few lexical relations between documents or paragraphs containing answers and questions. This Paper introduces an iterative retrieval method based on text processing techniques, which uses the structure of links inside Wikipedia while recognizing the type of question. This method allows retrieving documents related to open domain multi-step questions on the Wikipedia encyclopedia. The proposed method uses linguistic models based on a deep neural network to recognize the type of question, extract entities and keywords, and finally retrieve and rank documents. The evaluation results of the proposed method on the HotpotQA dataset show an improvement in the performance of the proposed method compared to the basic methods.

Cites

No record.

References

No record.

Cite

APA: Copy

Ghaderi, Seyed Himan, Ghafouri, Arash, & Naderi, Hassan. (2024). An Algorithm based on Semantic Similarity to Extract Candidate Answers in Question Answering Systems. INTERNATIONAL CONFERENCE ON WEB RESEARCH. SID. https://sid.ir/paper/1147675/en

Vancouver: Copy

Ghaderi Seyed Himan, Ghafouri Arash, Naderi Hassan. An Algorithm based on Semantic Similarity to Extract Candidate Answers in Question Answering Systems. 2024. Available from: https://sid.ir/paper/1147675/en

IEEE: Copy

Seyed Himan Ghaderi, Arash Ghafouri, and Hassan Naderi, “An Algorithm based on Semantic Similarity to Extract Candidate Answers in Question Answering Systems,” presented at the INTERNATIONAL CONFERENCE ON WEB RESEARCH. 2024, [Online]. Available: https://sid.ir/paper/1147675/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops