Deep Learning Based Question Answering Search Engine
نویسندگان
چکیده
منابع مشابه
Visual Question Answering using Deep Learning
Multimodal learning between images and language has gained attention of researchers over the past few years. Using recent deep learning techniques, specifically end-to-end trainable artificial neural networks, performance in tasks like automatic image captioning, bidirectional sentence and image retrieval have been significantly improved. Recently, as a further exploration of present artificial...
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This project deals with the problem of Visual Question Answering (VQA). We develop neural network-based models to answer open-ended questions that are grounded in images. We used the newly released VQA dataset (with about 750K questions) to carry out our experiments. Our model makes use of two popular neural network architecture: Convolutional Neural Nets (CNN) and Long Short Term Memory Networ...
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This paper focuses on the task of knowledge-based question answering (KBQA). KBQA aims to match the questions with the structured semantics in knowledge base. In this paper, we propose a two-stage method. Firstly, we propose a topic entity extraction model (TEEM) to extract topic entities in questions, which does not rely on hand-crafted features or linguistic tools. We extract topic entities i...
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Recent work in semantic parsing for question answering has focused on long and complicated questions, many of which would seem unnatural if asked in a normal conversation between two humans. In an effort to explore a conversational QA setting, we present a more realistic task: answering sequences of simple but inter-related questions. We collect a dataset of 6,066 question sequences that inquir...
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We present here a preliminary analysis of the results of our runs in the Question Answering track of TREC9. We have developed a complete system, including our own indexer and search engine, GuruQA, which provides document result lists that our Answer Selection module processes to identify answer fragments. Some TREC participants use a standard set of result lists provided by AT&T’s running of t...
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ژورنال
عنوان ژورنال: International Journal of Scientific Research in Computer Science, Engineering and Information Technology
سال: 2021
ISSN: 2456-3307
DOI: 10.32628/cseit2172139