Improve Quora Question Pair Dataset for Question Similarity Task

Le, Hanh N. D.; Cao, Dung; Bui, Trung; Luong, Long T.; Nguyen, Huy

doi:10.1109/rivf51545.2021.9642071

Cited by 5 publications

(2 citation statements)

References 4 publications

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“…Many times, professionals have to answer multiple versions of the same inquiry. This is where Quora's question similarity comes into play [12].…”

Section: A Introduction To Question-answer Forumsmentioning

confidence: 99%

A systematic review of similar Questions Retrieval Approaches

Khodke,

Dhande

2023

AJCT

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A renowned online community known as Quora enables members to post queries, receive insightful responses, and share knowledge. The capacity of Quora to find related questions based on a user's search is a distinctive feature that makes it simple for users to access pertinent information and add it to the platform's knowledge base. The retrieval of comparable questions from Quora is the topic of this paper. We assess various systems that classify related queries and quickly deliver pertinent responses to information searchers. Our assessment of machine learning and natural language processing methods focuses on how well these methods work when obtaining queries from the large Quora question database that serves related objectives. Our thorough research paper provides a summary of the literature on comparable question retrieval in Quora while highlighting the benefits and drawbacks of various approaches. Our evaluation identifies prospective topics for more research and development and acts as a guide for future scholars interested in this field. By enhancing similar question retrieval on Quora, we hope to encourage knowledge-sharing and community development on this important platform. Users can find the most pertinent responses to their inquiries on Quora by using the study's findings.

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“…Many times, professionals have to answer multiple versions of the same inquiry. This is where Quora's question similarity comes into play [12].…”

Section: A Introduction To Question-answer Forumsmentioning

confidence: 99%

A systematic review of similar Questions Retrieval Approaches

Khodke,

Dhande

2023

AJCT

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“…It is not possible to find and remove duplicate questions manually. The duplicate inquiries or sentences should be identified automatically using some autodetection approaches [4,5,6]. In order to find similarities between two sentences, we www.ijacsa.thesai.org conducted our research using Quora's question-pair dataset.…”

Section: Introductionmentioning

confidence: 99%

Strengthening Sentence Similarity Identification Through OpenAI Embeddings and Deep Learning

Korade,

Salunke,

Bhosle

et al. 2024

IJACSA

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Discovering similarity between sentences can be beneficial to a variety of systems, including chatbots for customer support, educational platforms, e-commerce customer inquiries, and community forums or question-answering systems. One of the primary issues that online question-answering platforms and customer service chatbots have is the large number of duplicate inquiries that are placed on the platform. In addition to cluttering up the platform, these repetitive queries degrade the content's quality and make it harder for visitors to locate pertinent information. Therefore, it is necessary to automatically detect sentence similarity in order to improve the user experience and quickly match user expectations. The present study makes use of the Quora dataset to construct a framework for similarity discovery in sentence pairs. As part of our research, we have built additional attributes based on textual data for improving the accuracy of similarity prediction. The study investigates several vectorization methods and their influence on accuracy. To convert preprocess text input to a numerical vector, we implemented Word2Vec, FastText, Term Frequency-Inverse Document Frequency (TF-IDF), CountVectorizer (CV), and OpenAI embedding. In order to judge sentence similarity, the embedding offered by several approaches was used with various models, including cosine similarity, Random Forest (RF), AdaBoost, XGBoost, LSTM, and CNN. The result demonstrates that all algorithms trained on OpenAI embedding yield excellent outcomes. The OpenAI-created embedding offers excellent information to models trained on it and has significant potential for capturing sentence similarity.

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