Bidirectional LSTM Recurrent Neural Network for Keyphrase Extraction

Basaldella, Marco; Antolli, Elisa; Serra, Giuseppe; Tasso, Carlo

doi:10.1007/978-3-319-73165-0_18

Cited by 55 publications

(27 citation statements)

References 22 publications

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“…Finally, Basaldella, Antolli, Serra, and Tasso (2018) propose a Bi-LSTM RNN which is able to exploit previous and future context of a given word. First, the document is split into sentences that are tokenized in words.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

“…Recently, features based on pretrained word embeddings are widely used. Particularly, deep learning methods (Alzaidy et al, 2019;Basaldella et al, 2018;Wang et al, 2018;Zhang et al, 2016) use such embeddings as input. Additionally, GloVe pretrained word embeddings are used along with an Idf-weighted scheme for each phrase representation by Wang and Li (2017) (PCU-ICL).…”

Section: External Knowledgementioning

confidence: 99%

See 1 more Smart Citation

A review of keyphrase extraction

Papagiannopoulou

Tsoumakas

2019

WIREs Data Min & Knowl

133

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Keyphrase extraction is a textual information processing task concerned with the automatic extraction of representative and characteristic phrases from a document that express all the key aspects of its content. Keyphrases constitute a succinct conceptual summary of a document, which is very useful in digital information management systems for semantic indexing, faceted search, document clustering and classification. This article introduces keyphrase extraction, provides a well‐structured review of the existing work, offers interesting insights on the different evaluation approaches, highlights open issues and presents a comparative experimental study of popular unsupervised techniques on five datasets. This article is categorized under: Ensemble Methods > Web Mining Ensemble Methods > Text Mining

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Section: Deep Learning Methodsmentioning

confidence: 99%

Section: External Knowledgementioning

confidence: 99%

A review of keyphrase extraction

Papagiannopoulou

Tsoumakas

2019

WIREs Data Min & Knowl

133

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“…From the short text summarization and analysis perspective, it is no doubt in saying that a particular number of neural network varieties are experimented with, and proven over time. To name a few, recurrent neural networks with long-short term memory (RNN-LSTM) [27], bi-directional long-short term memory (Bi-LSTM) [28], and the convolutional neural networks (CNN) [29]. While the problem with classic rulebased machine learning algorithms is, though they are faster to learn [30], they are shallow, hence the learning curve never really takes off with the time flow.…”

Section: Related Workmentioning

confidence: 99%

Predicting the pandemic: sentiment evaluation and predictive analysis from large-scale tweets on Covid-19 by deep convolutional neural network

Das

Kolya²

2021

Evol. Intel.

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Engaging deep neural networks for textual sentiment analysis is an extensively practiced domain of research. Textual sentiment classification harnesses the full computational potential of deep learning models. Typically, these research works are carried either with a popular open-source data corpus, or self-extracted short phrase texts from Twitter, Reddit, or web-scrapped text data from other resources. Rarely do we see a large amount of data on a current ongoing event is being collected and cultured further. Also, an even more complex task would be to model the data from a currently ongoing event, not only for scaling the sentiment accuracy but also for making a predictive analysis for the same. In this paper, we propose a novel approach for achieving sentiment evaluation accuracy by using a deep neural network on live-streamed tweets on Coronavirus and future case growth prediction. We develop a large tweet corpus exclusively based on the Coronavirus tweets. We split the data into train and test sets, alongside we perform polarity classification and trend analysis. The refined outcome from the trend analysis helps to train the data to provide an incremental learning curvature for our neural network, and we obtain an accuracy of 90.67%. Finally, we provide a statistical-based future prediction for Coronavirus cases growth. Not only our model outperforms several previous state-of-art experiments in overall sentiment accuracy comparison for similar tasks, but it also maintains a throughout performance stability among all the test cases when tested with several popular open-source text corpora.

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“…Generally, the output of the two networks is combined by summation [35]. BLSTM networks are the extension of LSTMs, which can improve the model performance on sequence classification problems [36][37][38]. In this paper, we both used RNN, LSTM, BRNN, and BLSTM to classify the protein families.…”

Section: Deep Learning Modelsmentioning

confidence: 99%

A novel Fibonacci hash method for protein family identification by using recurrent neural networks

Alakuş¹,

Türkoğlu²

2021

Turk J Elec Eng & Comp Sci

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Identification and classification of protein families are one of the most significant problem in bioinformatics and protein studies. It is essential to specify the family of a protein since, proteins are highly used in smart drug therapies, protein functions and in some case, phylogenetic trees. Some sequencing techniques provide researchers to identify the biological similarities of protein families and functions. Yet, determining these families with sequencing applications requires huge amount of time. Thus, it is needed a computer and artificial intelligence based classification system to save the time, and avoid complexity in protein classification process. In order to designate the protein families with computer-aided systems, protein sequences need to be converted to the numerical representations. In this paper, we provide a novel protein mapping method based on Fibonacci numbers and hashing table (FIBHASH). Each amino acid code is assigned to the Fibonacci numbers based on integer representations respectively. Later, these amino acid codes are inserted a hashing table with the size of 20 to be classified with recurrent neural networks. To determine the performance of the proposed mapping method, we used accuracy, f1-score, recall, precision, and AUC evaluation criteria. In addition, the results of evaluation metrics with other protein mapping techniques including EIIP, hydrophobicity, CPNR, Atchley factors, BLOSUM62, PAM250, binary one-hot encoding, and randomly encoded representations are compared. The proposed method showed a promising result with an accuracy of 92.77%, and 0.98 AUC score.

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Bidirectional LSTM Recurrent Neural Network for Keyphrase Extraction

Cited by 55 publications

References 22 publications

A review of keyphrase extraction

A review of keyphrase extraction

Predicting the pandemic: sentiment evaluation and predictive analysis from large-scale tweets on Covid-19 by deep convolutional neural network

A novel Fibonacci hash method for protein family identification by using recurrent neural networks

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