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Distributional Semantic Representation for Text Classification and Information Retrieval

Barathi Ganesh H. B., M. Anand Kumar, Soman K. P
2016 Forum for Information Retrieval Evaluation  
The objective of this experiment is to validate the performance of the distributional semantic representation of text in the classification (Question Classification) task and the Information Retrieval  ...  Followed by the distributional representation, first level classification of the questions is performed and relevant tweets with respect to the given queries are retrieved.  ...  DISTRIBUTIONAL REPRESENTATION This section describes about the distributional representation of the text, which is used further for the question classification and retrieval tasks.  ... 
dblp:conf/fire/HBMP16 fatcat:7uljiekpt5e4rivrdsriys7dcq

Distributional Semantic Representation in Health Care Text Classification

Barathi Ganesh H. B., M. Anand Kumar, Soman K. P
2016 Forum for Information Retrieval Evaluation  
In this proposed approach distributional representation of text along with its statistical and distance measures are carried over to perform the given tasks as a text classification problem.  ...  In our experiment, Non -Negative Matrix Factorization utilized to get the distributed representation of the document as well as queries, distance and correlation measures taken as the features and Random  ...  DISTRIBUTIONAL REPRESENTATION This section describes about the distributional representation of the text, which is used further for the classification task.  ... 
dblp:conf/fire/HBMP16a fatcat:bnm5tycthzd3fkywqlzi4jvscm

Joint Image-Text Representation by Gaussian Visual-Semantic Embedding

Zhou Ren, Hailin Jin, Zhe Lin, Chen Fang, Alan Yuille
2016 Proceedings of the 2016 ACM on Multimedia Conference - MM '16  
In this work, we present a novel Gaussian Visual-Semantic Embedding (GVSE) model, which leverages the visual information to model text concepts as Gaussian distributions in semantic space.  ...  How to jointly represent images and texts is important for tasks involving both modalities. Visual-semantic embedding models have been recently proposed and shown to be effective.  ...  We also acknowledge the support of NVIDIA Corporation with the donation of GPUs used for this research.  ... 
doi:10.1145/2964284.2967212 dblp:conf/mm/RenJLFY16 fatcat:i3pc7yh64raerdyndksx4vvaee

Retrieval of Scientific and Technological Resources for Experts and Scholars [article]

Suyu Ouyang and Yingxia Shao and Ang Li
2022 arXiv   pre-print
This paper sorts out the related research work in this field from four aspects: text relation extraction, text knowledge representation learning, text vector retrieval and visualization system.  ...  Therefore, it is very necessary to build an expert and scholar information database and provide relevant expert and scholar retrieval services.  ...  Text semantic vector representation Text semantic vector representation is to use fixedlength vectors to represent sentences of indeterminate length to provide services for downstream tasks such as semantic  ... 
arXiv:2204.06142v1 fatcat:qspkjuqrhbc6he3pdoyrheukua

SF-CNN: Deep Text Classification and Retrieval for Text Documents

R. Sarasu, K. K. Thyagharajan, N. R. Shanker
2023 Intelligent Automation and Soft Computing  
for retrieving correct text documents.  ...  The proposed SF-CNN is based on deep semantic-based bag-of-word representation for document retrieval.  ...  Text classification and information retrieval are applied for retrieving the research article. Traditional method of text classification algorithms are logistic regression, support vector machines.  ... 
doi:10.32604/iasc.2023.027429 fatcat:r2czwj5p6jdntkr3lgkp23erma

Implicit Semantic Relations Identification through Distributed Representations for Effective Text Retrieval
ENGLISH

Rajendra Prasath
2011 DESIDOC Journal of Library & Information Technology  
This paper discusses the use of a distributed representation, namely random indexing for an effective retrieval of relevant text documents.  ...  This type of distributed representation would be scalable with modern computing facilities and flexible to develop knowledge-based applications, which may require the process of identifying implicit semantic  ...  Distributed representations extract semantically similar words and capture their contexts as well.  ... 
doi:10.14429/djlit.31.4.1104 fatcat:j53tsw7davdfjerfxlrlxpo4ve

On the regularization of image semantics by modal expansion

J. C. Pereira, N. Vasconcelos
2012 2012 IEEE Conference on Computer Vision and Pattern Recognition  
Recent research efforts in semantic representations and context modeling are based on the principle of task expansion: that vision problems such as object recognition, scene classification, or retrieval  ...  Pairs of images and text are mapped to a semantic space, and the text features used to regularize their image counterparts.  ...  Query image Top 4 images retrieved (framing box indicates incorrectly retrieved images) Figure 4 . Query image of a butterfly (left) with top four retrieval results (right).  ... 
doi:10.1109/cvpr.2012.6248041 dblp:conf/cvpr/PereiraV12 fatcat:pelvzgtmxne73ehsfruhchiqgy

Cognitive Aspects-Based Short Text Representation with Named Entity, Concept and Knowledge

Wenfeng Hou, Qing Liu, Longbing Cao
2020 Applied Sciences  
The final multi-level semantic representations are formed by concatenating all of these individual-level representations, which are used for text classification.  ...  Since word, entity, concept and knowledge entity in the same short text have different cognitive informativeness for short text classification, attention networks are formed to capture these category-related  ...  The ECKA Method The framework of our proposed ECKA representation is illustrated in Figures 2 and 3 further shows its semantic information retrieval module.  ... 
doi:10.3390/app10144893 fatcat:uotbuk4tajcghmmwrlnkbkrfze

COBRA: Contrastive Bi-Modal Representation Algorithm [article]

Vishaal Udandarao, Abhishek Maiti, Deepak Srivatsav, Suryatej Reddy Vyalla, Yifang Yin, Rajiv Ratn Shah
2020 arXiv   pre-print
There are a wide range of applications that involve multi-modal data, such as cross-modal retrieval, visual question-answering, and image captioning.  ...  In this paper, we present a novel framework COBRA that aims to train two modalities (image and text) in a joint fashion inspired by the Contrastive Predictive Coding (CPC) and Noise Contrastive Estimation  ...  These applications often utilize this multi-modal data for tasks such as information retrieval [11, 44] , classification [48, 58] , and question-answering [27, 35] .  ... 
arXiv:2005.03687v2 fatcat:nfz7mrdv6jfyhil6vroqpegkie

Self-Supervised Visual Representations for Cross-Modal Retrieval [article]

Yash Patel, Lluis Gomez, Marçal Rusiñol, Dimosthenis Karatzas, C.V. Jawahar
2019 arXiv   pre-print
Our experiments demonstrate that the proposed method is not only capable of learning discriminative visual representations for solving vision tasks like image classification and object detection, but that  ...  the learned representations are better for cross-modal retrieval when compared to supervised pre-training of the network on the ImageNet dataset.  ...  Let Φ(x A i ) and Φ(x C i ) be the text topic probability distributions given by LDA 4.2 for the document text and the image captions accordingly.  ... 
arXiv:1902.00378v1 fatcat:ksha6zs7u5a53cclm2xjrozu7i

Cross-modal domain adaptation for text-based regularization of image semantics in image retrieval systems

Jose Costa Pereira, Nuno Vasconcelos
2014 Computer Vision and Image Understanding  
Training images and text are first mapped to a common semantic space. A regularization operator is then learned for each concept in the semantic vocabulary.  ...  In query-by-semantic-example image retrieval, images are ranked by similarity of semantic descriptors.  ...  Acknowledgments This work was funded by FCT graduate Fellowship SFRH/BD/ 40963/2007 from the Portuguese Ministry of Sciences and Education, and NSF Grant CCF-0830535.  ... 
doi:10.1016/j.cviu.2014.03.003 fatcat:npeftbsppbhd7lfqgpw637ucjm

Cross‐modal semantic correlation learning by Bi‐CNN network

Chaoyi Wang, Liang Li, Chenggang Yan, Zhan Wang, Yaoqi Sun, Jiyong Zhang
2021 IET Image Processing  
The deep CNN is used to extract the representation of images, and the shallow CNN uses a multi-dimensional kernel to extract multi-level semantic representation of text.  ...  Cross modal retrieval can retrieve images through a text query and vice versa. In recent years, cross modal retrieval has attracted extensive attention.  ...  In this paper, we fully explicate category information to generate semantic representations of images and text in cross-media retrieval.  ... 
doi:10.1049/ipr2.12176 fatcat:xbw2qc5tffdu7g3it7g6n4ljje

Self-Supervised Learning of Visual Features through Embedding Images into Text Topic Spaces

Lluis Gomez, Yash Patel, Marcal Rusinol, Dimosthenis Karatzas, C. V. Jawahar
2017 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
For this we leverage the hidden semantic structures discovered in the text corpus with a well-known topic modeling technique.  ...  Our experiments demonstrate state of the art performance in image classification, object detection, and multimodal retrieval compared to recent self-supervised or natural-supervised approaches.  ...  This work has been partially supported by the Spanish research project TIN2014-52072-P and the CERCA Programme/Generalitat de Catalunya.  ... 
doi:10.1109/cvpr.2017.218 dblp:conf/cvpr/Gomez-BigordaPR17 fatcat:paymsbngcbfblfp5ehxgnk3gpm

Self-supervised learning of visual features through embedding images into text topic spaces [article]

Lluis Gomez, Yash Patel, Marçal Rusiñol, Dimosthenis Karatzas, C.V. Jawahar
2017 arXiv   pre-print
For this we leverage the hidden semantic structures discovered in the text corpus with a well-known topic modeling technique.  ...  Our experiments demonstrate state of the art performance in image classification, object detection, and multi-modal retrieval compared to recent self-supervised or natural-supervised approaches.  ...  This work has been partially supported by the Spanish research project TIN2014-52072-P and the CERCA Programme/Generalitat de Catalunya.  ... 
arXiv:1705.08631v1 fatcat:c7pu7heiobcexhftqemuuye6pi

Deep Adversarial Learning Triplet Similarity Preserving Cross-Modal Retrieval Algorithm

Guokun Li, Zhen Wang, Shibo Xu, Chuang Feng, Xiaohan Yang, Nannan Wu, Fuzhen Sun
2022 Mathematics  
In each modal space, to ensure that the generated features have the same semantic information as the sample labels, we establish a linear classifier and require that the generated features' classification  ...  For the image to text task, our proposed method improved the mAP values by 1% and 0.7% on the Pascal sentence and Wikipedia datasets, respectively.  ...  Acknowledgments: The authors express their gratitude to the institutions that supported this research: Shandong University of Technology (SDUT) and Jilin University (JLU).  ... 
doi:10.3390/math10152585 fatcat:rfbm3qwkwvb4zh7n3bjw4w7xza
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