Results 21 to 30 of about 544,807 (273)
Classification of Russian Texts by Genres Based on Modern Embeddings and Rhythm
Моделирование и анализ информационных систем, 2022 The article investigates modern vector text models for solving the problem of genre classification of Russian-language texts. Models include ELMo embeddings, BERT language model with pre-training and a complex of numerical rhythm features based on lexico-
Ksenia Vladimirovna Lagutina
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IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 2021 Someone's opinion on a product or service that is poured through a review is something that is quite important for the owner or potential customer. However, the large number of reviews makes it difficult for them to analyze the information contained in ...
Putri Rizki Amalia, Edi Winarko
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Connection Science, 2022 Aspect-level sentiment classification, a significant task of fine-grained sentiment analysis, aims to identify the sentimental information expressed in each aspect of a given sentence The existing methods combine global features and local structures to ...
Subo Wei +4 more
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ParsBERT: Transformer-based Model for Persian Language Understanding
, 2020 The surge of pre-trained language models has begun a new era in the field of Natural Language Processing (NLP) by allowing us to build powerful language models.
Farahani, Marzieh +3 more
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Neural Language Models for Nineteenth-Century English
Journal of Open Humanities Data, 2021 We present four types of neural language models trained on a large historical dataset of books in English, published between 1760 and 1900, and comprised of ≈5.1 billion tokens.
Kasra Hosseini +3 more
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Table Search Using a Deep Contextualized Language Model
, 2020 Pretrained contextualized language models such as BERT have achieved impressive results on various natural language processing benchmarks. Benefiting from multiple pretraining tasks and large scale training corpora, pretrained models can capture complex ...
Auer Sören +5 more
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CEDR: Contextualized Embeddings for Document Ranking [PDF]
, 2019 Although considerable attention has been given to neural ranking architectures recently, far less attention has been paid to the term representations that are used as input to these models.
Cohan, Arman +3 more
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I-BERT: Integer-only BERT Quantization
, 2021 Transformer based models, like BERT and RoBERTa, have achieved state-of-the-art results in many Natural Language Processing tasks. However, their memory footprint, inference latency, and power consumption are prohibitive efficient inference at the edge, and even at the data center.
Kim, Sehoon +4 more
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How to Fine-Tune BERT for Text Classification?
, 2020 Language model pre-training has proven to be useful in learning universal language representations. As a state-of-the-art language model pre-training model, BERT (Bidirectional Encoder Representations from Transformers) has achieved amazing results in ...
Huang, Xuanjing +3 more
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Look at the First Sentence: Position Bias in Question Answering
, 2020 Many extractive question answering models are trained to predict start and end positions of answers. The choice of predicting answers as positions is mainly due to its simplicity and effectiveness. In this study, we hypothesize that when the distribution
Kang, Jaewoo +4 more
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