Question answering system on education acts using NLP techniques

Lende, Sweta P.; Raghuwanshi, M. M.

doi:10.1109/startup.2016.7583963

Cited by 49 publications

(20 citation statements)

References 6 publications

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“…Liu et al [7] proposed a method that applied dynamic LSTM networks to solve the problem of long-range dependence of RNN. Lende and Raghuwanshi [8] proposed a closed domain Q&A system for processing documents about the education acts, and improves the accuracy of retrieval answers by using NLP techniques. In particular, the remarkable improvement for reading comprehension in the long text has also led to the improvement of answer generation methods.…”

Section: Related Workmentioning

confidence: 99%

Multi-Layer Transformer Aggregation Encoder for Answer Generation

2020

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Answer generation is one of the most important tasks in natural language processing, and deep learning-based methods have shown their strength over traditional machine learning based methods. However, most previous deep learning-based answer generation models were built on traditional recurrent neural networks or convolutional neural networks. The former model cannot well exploit contextual correlation preserved in paragraphs due to their inherent computation complexity. For the latter, since the size of the convolutional kernel is fixed, the model cannot extract complete semantic information features. In order to alleviate this problem, based on multi-layer Transformer aggregation coder, we propose an end-toend answer generation model (AG-MTA). AG-MTA consists of a multi-layer attention Transformer unit and a multi-layer attention Transformer aggregation encoder (MTA). It can focus on information representation at different positions and aggregate nodes at same layer to combine the context information. Thereby, it fuses semantic information from base layer to top layer, enhancing the information representation of the encoder. Furthermore, based on trigonometric function, a novel position encoding method is also proposed. Experiments are conducted on public datasets SQuAD. AG-MTA reaches the state-of-the-art performance, EM score achieves 71.1 and F1 score achieves 80.3. INDEX TERMS Question answering system, natural language processing, self-attention mechanism, transformer coding structure.

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Section: Related Workmentioning

confidence: 99%

Multi-Layer Transformer Aggregation Encoder for Answer Generation

2020

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“…The first one is that it answers the user's question with a precise answer [76], [78]. The second is that it has a fairly high accuracy rate for specific domains (such as medicine or automotive maintenance) [76], [79]. However, this type has the following two disadvantages.…”

Section: Recommendation Systemmentioning

confidence: 99%

A Study of Optimizing Search Engine Results Through User Interaction

Chen

2020

IEEE Access

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In this paper, we propose a method for optimizing search engine results based on user interaction. This method generates different search results mainly through the user's operation on different search topics. There are two main differences between our method and the traditional personalized search method: personal privacy and storage space. First, traditional personal search methods need to record search and click records for individual users. However, these records have great personal privacy issues for users; especially in recent years, there have been personal privacy leaks that occurred in many large online companies (such as Facebook and Yahoo). Secondly, because different users need to record their own search records, the size of the storage space is closely related to the number of users and the amount of search records stored. However, the sum of individual users' search and click records is a huge storage space in today's Internet environment. To avoid these two issues for traditional personalized search methods, this study proposes a storage-free approach based on individual users operating on different search topics. In general, in addition to avoiding personal privacy and storage space issues, our method can also achieve optimal linear time in generating personalized search results. INDEX TERMS Document clustering, machine learning, natural language processing, personalized topic search, user log.

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“…Closed-domain QA systems aim to address a specific area of knowledge, providing more accurate answers and being easier to fine tune the system. Some examples of Closed-domain QA are Question Answer System on Education Acts [8], Python Question Answer System (PythonQA) [9], and K-Extractor [2]. Opendomain QA systems attempt to work with any domain of knowledge, having a broader knowledge base than the closed-domain.…”

Section: Related Workmentioning

confidence: 99%

“…The authors propose ten question types. In the work proposed by [8], a QA system to handle education acts is presented. The knowledge base is created from the data publicly available from the UK parliament using NLP techniques.…”

Section: Related Workmentioning

confidence: 99%