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An Approach for Process Model Extraction By Multi-Grained Text Classification [article]

Chen Qian, Lijie Wen, Akhil Kumar, Leilei Lin, Li Lin, Zan Zong, Shuang Li, Jianmin Wang
2020 arXiv   pre-print
In this paper, we formalize the PME task into the multi-grained text classification problem, and propose a hierarchical neural network to effectively model and extract multi-grained information without  ...  Process model extraction (PME) is a recently emerged interdiscipline between natural language processing (NLP) and business process management (BPM), which aims to extract process models from textual descriptions  ...  To this end, we constructed two multi-grained PME corpora for the task of extracting process models from texts: • Cooking Recipes (COR).  ... 
arXiv:1906.02127v3 fatcat:x4dd7nrczbgjfm75245q3qo5yy

An Approach for Process Model Extraction by Multi-grained Text Classification [chapter]

Chen Qian, Lijie Wen, Akhil Kumar, Leilei Lin, Li Lin, Zan Zong, Shu'ang Li, Jianmin Wang
2020 Lecture Notes in Computer Science  
In this paper, we formalize the PME task into the multi-grained text classification problem, and propose a hierarchical neural network to effectively model and extract multi-grained information without  ...  Process model extraction (PME) is a recently emerged interdiscipline between natural language processing (NLP) and business process management (BPM), which aims to extract process models from textual descriptions  ...  To this end, we constructed two multi-grained PME corpora for the task of extracting process models from texts: • Cooking Recipes (COR).  ... 
doi:10.1007/978-3-030-49435-3_17 fatcat:f2xxjgml25hhdbckqdpuy65z4e

Using Text and Visual Cues for Fine-Grained Classification

Zaryab Shaker, Xiao Feng, Muhammad Adeel Ahmed Tahir
2021 International Journal of Advanced Network, Monitoring, and Controls  
Then we combine the attended word embedding and visual feature vector which are then optimized by CNN for Fine-grained image classification.  ...  Text is an important invention of humanity, which plays a key role in human life, so far from dark ages.  ...  The first novel approach of a real end-to-end model for text detection and recognition in a scene was proposed in 2010 by Neumann et al [8] .  ... 
doi:10.21307/ijanmc-2021-026 fatcat:o7ostmko7bbljjfdmek4f5s5nm

A Multi-modal Approach to Fine-grained Opinion Mining on Video Reviews [article]

Edison Marrese-Taylor, Cristian Rodriguez-Opazo, Jorge A. Balazs, Stephen Gould, Yutaka Matsuo
2020 arXiv   pre-print
In light of this issue, we propose a multi-modal approach for mining fine-grained opinions from video reviews that is able to determine the aspects of the item under review that are being discussed and  ...  We evaluate our approach on two datasets and show that leveraging the video and audio modalities consistently provides increased performance over text-only baselines, providing evidence these extra modalities  ...  Acknowledgments We are grateful for the support provided by the NVIDIA Corporation, donating two of the GPUs used for this research.  ... 
arXiv:2005.13362v2 fatcat:jisgega2uzacnd3qfxanr5ilfe

Coarse and Fine-Grained Hostility Detection in Hindi Posts using Fine Tuned Multilingual Embeddings [article]

Arkadipta De, Venkatesh E, Kaushal Kumar Maurya, Maunendra Sankar Desarkar
2021 arXiv   pre-print
We view this hostility detection as a multi-label multi-class classification problem. We propose an effective neural network-based technique for hostility detection in Hindi posts.  ...  Our best performing neural classifier model includes One-vs-the-Rest approach where we obtained 92.60%, 81.14%,69.59%, 75.29% and 73.01% F1 scores for hostile, fake, hate, offensive, and defamation labels  ...  Coarse-Grained Classification These sections include details of the models which were used for a coarse-grained classification task. ¢ Fine-Tuned mBERT (FmBERT) and XLM-R (FXLMR) Models: For the coarse-grained  ... 
arXiv:2101.04998v1 fatcat:z4bdmqg7mzdlbggjh7am472o2q

Fine-Grained Entity Recognition

Xiao Ling, Daniel Weld
2021 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
This paper defines a fine-grained set of 112 tags, formulates the tagging problem as multi-class, multi-label classification, describes an unsupervised method for collecting training data, and presents  ...  Experiments show that the system accurately predicts the tags for entities. Moreover, it provides useful information for a relation extraction system, increasing the F1 score by 93%.  ...  Acknowledgements The authors thank Congle Zhang for preparing the Relation Extraction part of the experiment and all members of the Turing Center for helpful discussions.  ... 
doi:10.1609/aaai.v26i1.8122 fatcat:lmlpohuhfnes3nbijr3alc2lb4

M5Product: Self-harmonized Contrastive Learning for E-commercial Multi-modal Pretraining [article]

Xiao Dong, Xunlin Zhan, Yangxin Wu, Yunchao Wei, Michael C. Kampffmeyer, Xiaoyong Wei, Minlong Lu, Yaowei Wang, Xiaodan Liang
2022 arXiv   pre-print
We further propose Self-harmonized ContrAstive LEarning (SCALE), a novel pretraining framework that integrates the different modalities into a unified model through an adaptive feature fusion mechanism  ...  We evaluate the current multi-modal pre-training state-of-the-art approaches and benchmark their ability to learn from unlabeled data when faced with the large number of modalities in the M5Product dataset  ...  For the Image based and BERT [10] models, which only utilize the image and text features, respectively, the extracted features are fed directly into the classification model.  ... 
arXiv:2109.04275v5 fatcat:h5ngubiktfgr3plnx4l4bjz35i

BLUE at Memotion 2.0 2022: You have my Image, my Text and my Transformer [article]

Ana-Maria Bucur, Adrian Cosma, Ioan-Bogdan Iordache
2022 arXiv   pre-print
In both approaches, we leverage state-of-the-art pretrained models for text (BERT, Sentence Transformer) and image processing (EfficientNetV4, CLIP).  ...  We showcase two approaches for meme classification (i.e. sentiment, humour, offensive, sarcasm and motivation levels) using a text-only method using BERT, and a Multi-Modal-Multi-Task transformer network  ...  We described two solutions for meme classification: i) text-only approach through fine-tuning a BERT model and ii) a Multi-Modal-Multi-Task transformer network that operates on both images and text.  ... 
arXiv:2202.07543v3 fatcat:hsi4cdc7zfhptle45ttbn64tg4

A Joint Neural Model for Fine-Grained Named Entity Classification of Wikipedia Articles

Masatoshi SUZUKI, Koji MATSUDA, Satoshi SEKINE, Naoaki OKAZAKI, Kentaro INUI
2018 IEICE transactions on information and systems  
Information of NE types are useful when extracting knowledge of NEs from natural language text. It is common to apply an approach based on supervised machine learning to named entity classification.  ...  However, in a setting of classifying into fine-grained types, one big challenge is how to alleviate the data sparseness problem since one may obtain far fewer instances for each fine-grained types.  ...  Furthermore, this model can also naturally realize multi-label classification. Second, we extend the feature set by exploiting the hyper-text structure of Wikipedia.  ... 
doi:10.1587/transinf.2017swp0005 fatcat:cwtwzjg775akpo36pvkx62a6ae

Current Approaches and Applications in Natural Language Processing

Arturo Montejo-Ráez, Salud María Jiménez-Zafra
2022 Applied Sciences  
One last contribution to text classification is the creation of a new multi-modal Wikimedia Commons dataset based on concrete/abstract words [9] , along with a novel multi-modal pre-training approach  ...  Another contribution to fine-grained NER is [12] . This work proposes a system for using character-level embeddings over LSTM networks multi-stacked for feature fusion.  ...  Funding: This work was supported by Project LIVING-LANG (RTI2018-094653-B-C21) funded by MCIN/AEI/10.13039/501100011033 and by ERDF A way of making Europe, Fondo Social Europeo and Administration of the  ... 
doi:10.3390/app12104859 fatcat:yhoyyoqcazflrbx7veksnkrrdq

An Artificial Intelligence based Analysis in Legal domain

2019 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
Legal document translation, text classification, summarization, data forecasting and data obtainment are part of the goals got from research charity.  ...  The machine learning and deep learning algorithmsbased analysis systems apply these methods mainly for document classification.  ...  [6] proposed multi task learning approach to be used by the Multimodal algorithm. The algorithm was developed as a single deep learning model, for multi tasks in legal domain.  ... 
doi:10.35940/ijitee.b1113.1292s219 fatcat:nk4nnpe4urgsbmowwfeysfgtke

Integrating Scene Text and Visual Appearance for Fine-Grained Image Classification [article]

Xiang Bai, Mingkun Yang, Pengyuan Lyu, Yongchao Xu, Jiebo Luo
2017 arXiv   pre-print
We have performed experiments on two datasets: Con-Text dataset and Drink Bottle dataset, that are proposed for fine-grained classification of business places and drink bottles, respectively.  ...  Then, we combine the word embedding of the recognized words and the deep visual features into a single representation, which is optimized by a convolutional neural network for fine-grained image classification  ...  Such approaches enable us to automatically extract the text information, which can be considered as an additional information for image classification.  ... 
arXiv:1704.04613v2 fatcat:njyzdagkondknmvytbgshm6mhu

Walk in Wild: An Ensemble Approach for Hostility Detection in Hindi Posts [article]

Chander Shekhar, Bhavya Bagla, Kaushal Kumar Maurya, Maunendra Sankar Desarkar
2021 arXiv   pre-print
We formulated this problem as binary classification (hostile and non-hostile class) and multi-label multi-class classification problem (for more fine-grained hostile classes).  ...  In this paper, we develop a simple ensemble based model on pre-trained mBERT and popular classification algorithms like Artificial Neural Network (ANN) and XGBoost for hostility detection in Hindi posts  ...  It consists of three stages: Pre-processing, embeddings extraction, and classification model.  ... 
arXiv:2101.06004v1 fatcat:xjkddqehezfppnh5let2izzx54

Classification of Consumer Belief Statements From Social Media [article]

Gerhard Hagerer and Wenbin Le and Hannah Danner and Georg Groh
2021 arXiv   pre-print
on text classification tasks.  ...  By doing so, this work can serve as an example of how complex expert annotations are potentially beneficial and can be utilized in the most optimal way for opinion mining in highly specific domains.  ...  Discussion We presented a systematic analysis of 3 class abstraction approaches on the organic datasets for text classification tasks.  ... 
arXiv:2106.15498v1 fatcat:2zctkylvsbehfdxcvzpyki2cme

Adaptive Feature Extractor of Global Representation and Local Semantics for Text Classification

Chaofan Wang, Shenggen Ju, Yuezhong Liu, Run Chen, Xiaoming Huang
2020 IEEE Access  
It proves that taking an adaptive approach to automatically determine the contribution degree of each model to classification indeed works in text classification tasks.  ...  The attention mechanism is efficiency in extracting more accurate local semantics in text. 2) For fine-grained emotion classification tasks, we also establish a new kind of loss function which leads to  ... 
doi:10.1109/access.2020.3036455 fatcat:biv74iw6s5bahdo4lhqhp2kneu
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