12 papers · ranked by Valyu relevance
Armand Joulin, Édouard Grave, Piotr Bojanowski, Tomáš Mikolov
This paper explores a simple and efficient baseline for text classification. Our experiments show that our fast text classifier fastText is often on par with deep learning classifiers in terms of accuracy, and many orders of magnitude faster for training and evaluation. We can train fastText on more than one billion…
Asan Agibetov, Kathrin Blagec, Hong Xu, Matthias Samwald
Background Biomedical literature is expanding rapidly, and tools that help locate information of interest are needed. To this end, a multitude of different approaches for classifying sentences in biomedical publications according to their coarse semantic and rhetoric categories (e.g., Background, Methods, Results…
Yongsheng Yang, Xiaoying Wang
In the digital age of today, the internet has become an indispensable platform for people's lives, work, and information exchange. However, the problem of violent text proliferation in the network environment has arisen, which has brought about many negative effects. In view of this situation, it is particularly…
Vladimir Zolotov, David S. Kung
The paper [1] shows that simple linear classifier can compete with complex deep learning algorithms in text classification applications. Combining bag of words (BoF) and linear classification techniques, fastText [1] attains same or only slightly lower accuracy than deep learning algorithms [2-9] that are orders of…
Shiguang Guo, Qing Wang, Fangyu Li
The ‘intention’ classification of a user question is an important element of a task-engine driven chatbot. The essence of a user question’s intention understanding is the text classification. The transfer learning, such as BERT (Bidirectional Encoder Representations from Transformers) and ERNIE (Enhanced Representation…
Yudong Zhu, Di Zhou, Jinghui Xiao, Xin Jiang + 2 more
'Qun Liu'] Natural language data exhibit tree-like hierarchical structures such as the hypernymhyponym relations in WordNet. FastText, as the state-of-the-art text classifier based on shallow neural network in Euclidean space, may not model such hierarchies precisely with limited representation capacity. Considering…
A.G. Shanbhag, Suramya Jadhav, Amogh Thakurdesai, Ridhima Sinare + 1 more
Natural Language Processing (NLP) for lowresource languages, which lack large annotated datasets, faces significant challenges due to limited high-quality data and linguistic resources. The selection of embeddings plays a critical role in achieving strong performance in NLP tasks. While contextual BERT embeddings…
Abdulaziz Altamimi, Jawad Rasheed
The proliferation of fake news is one of the major problems that causes personal and societal harm. In today’s fast-paced digital age, misinformation spreads rapidly, often leaving individuals without the time to verify the authenticity of the information. This can cause irreparable damage to personal reputations and…
Anandan Chinnalagu, Ashok Kumar Durairaj, Jude Duraisamy
Customer satisfaction and their positive sentiments are some of the various goals for successful companies. However, analyzing customer reviews to predict accurate sentiments have been proven to be challenging and time-consuming due to high volumes of collected data from various sources. Several researchers approach…
Ayu Pertiwi, Azhari Azhari, Sri Mulyana, Bilal Alatas
Background Topic modeling approaches, such as latent Dirichlet allocation (LDA) and its successor, the dynamic topic model (DTM), are widely used to identify specific topics by extracting words with similar frequencies from documents. However, these topics often require manual interpretation, which poses challenges in…
Zhe Chen, Wenhai Wang, Enze Xie, Zhibo Yang + 2 more
—We propose an accurate and efficient scene text detection framework, termed FAST (i.e., faster arbitrarily-shaped text detector). Different from recent advanced text detectors that used complicated post-processing and hand-crafted network architectures, resulting in low inference speed, FAST has two new designs. (1)…
Florian Kadner, Yannik Keller, Constantin A. Rothkopf
Digital text has become one of the primary ways of exchanging knowledge, but text needs to be rendered to a screen to be read. We present AdaptiFont, a human-in-the-loop system that is aimed at interactively increasing readability of text displayed on a monitor. To this end, we first learn a generative font space with…