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An Approach for Process Model Extraction By Multi-Grained Text Classification
[article]
2020
arXiv
pre-print
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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]
2020
Lecture Notes in Computer Science
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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
2021
International Journal of Advanced Network, Monitoring, and Controls
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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]
2020
arXiv
pre-print
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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]
2021
arXiv
pre-print
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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
2021
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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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]
2022
arXiv
pre-print
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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]
2022
arXiv
pre-print
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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
2018
IEICE transactions on information and systems
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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
2022
Applied Sciences
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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
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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]
2017
arXiv
pre-print
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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]
2021
arXiv
pre-print
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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]
2021
arXiv
pre-print
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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
2020
IEEE Access
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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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