Zero‐anaphora resolution in Korean based on deep language representation model: BERT

Kim, Youngtae; Ra, Dong-Yul; Lim, Soojong

doi:10.4218/etrij.2019-0441

Cited by 12 publications

(13 citation statements)

References 14 publications

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“…Second, previous studies have found that when Korean phrases are classified using both word and grapheme information, classification performance is higher than conventional models that use only word information or grapheme information [19]. In this study, it also appears that a higher performance can be obtained by combining grapheme-and syllable-based techniques.…”

Section: Discussionmentioning

confidence: 53%

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Profane or Not: Improving Korean Profane Detection using Deep Learning

2022

KSII TIIS

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Abusive behaviors have become a common issue in many online social media platforms. Profanity is common form of abusive behavior in online. Social media platforms operate the filtering system using popular profanity words lists, but this method has drawbacks that it can be bypassed using an altered form and it can detect normal sentences as profanity. Especially in Korean language, the syllable is composed of graphemes and words are composed of multiple syllables, it can be decomposed into graphemes without impairing the transmission of meaning, and the form of a profane word can be seen as a different meaning in a sentence. This work focuses on the problem of filtering system mis-detecting normal phrases with profane phrases. For that, we proposed the deep learning-based framework including grapheme and syllable separation-based word embedding and appropriate CNN structure. The proposed model was evaluated on the chatting contents from the one of the famous online games in South Korea and generated 90.4% accuracy.

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Section: Discussionmentioning

confidence: 53%

“…Kim, Ra and Lim [19] proposed BERT model that solves the task of zero anaphora resolution (omission of subject in the sentence) for completely understanding the texts in Korean.…”

Section: Text Mining For Korean Languagementioning

confidence: 99%

Profane or Not: Improving Korean Profane Detection using Deep Learning

2022

KSII TIIS

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“…Pretrained models are commonly used to improve overall network performance [27][28][29][30]. However, true success depends on the nature of the computer vision task and how well the model fits the task.…”

Section: Importance Of the Imagenetpretrained Resnet-18 Networkmentioning

confidence: 99%

Assembling three one‐camera images for three‐camera intersection classification

Astrid,

Lee

2023

ETRI Journal

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Determining whether an autonomous self‐driving agent is in the middle of an intersection can be extremely difficult when relying on visual input taken from a single camera. In such a problem setting, a wider range of views is essential, which drives us to use three cameras positioned in the front, left, and right of an agent for better intersection recognition. However, collecting adequate training data with three cameras poses several practical difficulties; hence, we propose using data collected from one camera to train a three‐camera model, which would enable us to more easily compile a variety of training data to endow our model with improved generalizability. In this work, we provide three separate fusion methods (feature, early, and late) of combining the information from three cameras. Extensive pedestrian‐view intersection classification experiments show that our feature fusion model provides an area under the curve and F1‐score of 82.00 and 46.48, respectively, which considerably outperforms contemporary three‐ and one‐camera models.

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“…BERT is bidirectional, unlike Elmo. Transformer encoder-based word Embedding is used 18 . A fine-tune and classify patents with the model.…”

Section: Related Workmentioning

confidence: 99%

Deep neural architecture for natural language image synthesis for Tamil text using BASEGAN and hybrid super resolution GAN (HSRGAN)

Diviya,

Karmel

2023

Sci Rep

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Tamil is a language that has the most extended history and is a conventional language of India. It has antique origins and a distinct tradition. A study reveals that at the beginning of the twenty-first century, more than 66 million people spoke Tamil. In the present time, image synthesis from text emerged as a promising advancement in computer vision applications. The research work done so far in intelligent systems is trained in universal language but still has not achieved the desired development level in regional languages. Regional languages have a greater scope for developing applications and will enhance more research areas to be explored, ruling out the barrier. The current work using Auto Encoders failed at the point of providing vivid information along with essential descriptions of the synthesised images. The work aims to generate embedding vectors using a language model headed by image synthesis using GAN (Generative Adversarial Network) architecture. The proposed method is divided into two stages: designing a language model TBERTBASECASE model for generating embedding vectors. Synthesising images using Generative Adversarial Network called BASEGAN, the resolution has been improved through two-stage architecture named HYBRID SUPER RESOLUTION GAN. The work uses Oxford-102 and CUB-200 datasets. The framework efficiency has been measured using F1 Score, Fréchet inception distance (FID), and Inception Score (IS). Language and image synthesis architecture proposed can bridge the gap between the research ideas in regional languages.

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Zero‐anaphora resolution in Korean based on deep language representation model: BERT

Cited by 12 publications

References 14 publications

Profane or Not: Improving Korean Profane Detection using Deep Learning

Profane or Not: Improving Korean Profane Detection using Deep Learning

Assembling three one‐camera images for three‐camera intersection classification

Deep neural architecture for natural language image synthesis for Tamil text using BASEGAN and hybrid super resolution GAN (HSRGAN)

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