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Design and development of a concept-based multi-document summarization system for research abstracts

Shiyan Ou, Christopher Soo-Guan Khoo, Dion H. Goh
2008 Journal of information science  
This paper describes a new concept-based multi-document summarization system that employs discourse parsing, information extraction and information integration.  ...  The majority of subjects (70%) preferred the concept-based summaries generated using the system to the sentence-based summaries generated using traditional sentence extraction techniques.  ...  a new sentence.  ... 
doi:10.1177/0165551507084630 fatcat:oz44rflbcfcc3ihxnmnf3bsao4

Learning Textologies: Networks of Linked Word Clusters [chapter]

Hristo Tanev
2014 Text Mining  
In this chapter we propose an alternative solution: learning a textology, that is, a graph of word clusters connected by co-occurrence relations.  ...  On the other hand, building a full-fledged ontology is not necessary for every application which requires modeling of semantic classes and relations between them.  ...  (b) Extracting new words and multiwords using the contextual features extracted in step (a). (c) Manually deleting inappropriate candidates.  ... 
doi:10.1007/978-3-319-12655-5_2 dblp:series/tanlp/Tanev14 fatcat:fbapdiqanrejzezhuszyu2wmk4

Clustering of Deep Contextualized Representations for Summarization of Biomedical Texts [article]

Milad Moradi, Matthias Samwald
2019 arXiv   pre-print
In this paper, we demonstrate that contextualized representations extracted from the pre-trained deep language model BERT, can be effectively used to measure the similarity between sentences and to quantify  ...  The source code and data are available at https://github.com/BioTextSumm/BERT-based-Summ.  ...  a concept-based level [2, 3] .  ... 
arXiv:1908.02286v2 fatcat:3pa62vuvi5hnrg5fkvvm7fsjmq

A Scientometric Analysis of Publications Related to Predictive Medicine

Aida Khakimova, Dongxiao Gu, Oleg Zolotarev, Maria Berberova, Michael Charnine
2020 Proceedings of the 30th International Conference on Computer Graphics and Machine Vision (GraphiCon 2020). Part 2  
We identified the terms relevant to PPM using own search engine based on neural network processing in PubMed database. We extracted initially about 15000 articles.  ...  An approach based on the analysis of article titles has been implemented.  ...  Fig. 4 . 4 Visualization of 123 concepts (14 clusters) Fig. 5 . 5 Contextual mapping of terms obtained by statistical analysis of the text corpus based on the predictive term.  ... 
doi:10.51130/graphicon-2020-2-3-81 fatcat:bcjujnth5rewxbmgoxbz5b5yu4

Usingtagflakefor condensing navigable tag hierarchies from tag clouds

Luigi Di Caro, K. Selçuk Candan, Maria Luisa Sapino
2008 Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD 08  
TMine extracts the most significant tag/terms from text documents and maps them onto a hierarchy in such a way that descendant terms are contextually dependent on their ancestors within the given corpus  ...  This provides tagFlake with a mechanism for enabling navigation within the tag space and for classification of the text documents based on the contextual structure captured by the created hierarchy. tagFlake  ...  K-means clustering is then applied on tag similarity matrix, with an a priori chosen number of clusters and fixed number of selected relevant tags.  ... 
doi:10.1145/1401890.1402021 dblp:conf/kdd/CaroCS08 fatcat:5ozpql7xh5dvlhpkwrzvpfss6a

A Relation-Based Contextual Approach for Efficient Multimedia Analysis

Evaggelos Spyrou, Giorgos Tolias, Phivos Mylonas
2008 2008 Third International Workshop on Semantic Media Adaptation and Personalization  
In this paper we present our research work on the identi cation of high-level concepts within multimedia documents through the introduction and utilization of contextual relations.  ...  Evaluation results are presented on a medium-size dataset, consisting of 1435 images, 25 region types and 6 high-level concepts derived from the beach domain of interest.  ...  An existing edge between a given pair of concepts is produced based on the set of contextual fuzzy relations that are meaningful for the particular pair.  ... 
doi:10.1109/smap.2008.39 dblp:conf/smap/SpyrouTM08 fatcat:zsykeo6afrf4nnxyqe5npkgwk4

Term Clustering Using a Corpus-Based Similarity Measure [chapter]

Goran Nenadić, Irena Spasić, Sophia Ananiadou
2002 Lecture Notes in Computer Science  
The method uses a hybrid similarity measure to cluster terms automatically extracted from a corpus by applying the C/NC value method.  ...  In this paper we present a method for the automatic term clustering.  ...  Conclusion We have presented the results on term clustering using a hybrid term similarity measure. The measure is based on lexical and syntactical patterns automatically extracted from a corpus.  ... 
doi:10.1007/3-540-46154-x_20 fatcat:o7yphqivivc37ekkn5knyevvse

ViTS: Video Tagging System from Massive Web Multimedia Collections

Delia Fernandez, David Varas, Joan Espadaler, Issey Masuda, Jordi Ferreira, Alejandro Woodward, David Rodriguez, Xavier Giro-i-Nieto, Juan Carlos Riveiro, Elisenda Bou
2017 2017 IEEE International Conference on Computer Vision Workshops (ICCVW)  
ViTS is an industrial product under exploitation with a vocabulary of over 2.5M concepts, capable of indexing more than 150k videos per month.  ...  ViTS analyses massive multimedia collections by Internet crawling, and maintains a knowledge base that updates in real time with no need of human supervision.  ...  Notice how learned relations in R generate clusters: e.g. news channels generated a cluster in the lower left corner, while politics in Spain generate a cluster in the top right corner.  ... 
doi:10.1109/iccvw.2017.48 dblp:conf/iccvw/FernandezVEMFWR17 fatcat:j4crsduvbjgrxe6rjztws7vsme

Using Visual Context and Region Semantics for High-Level Concept Detection

Phivos Mylonas, Evaggelos Spyrou, Yannis Avrithis, Stefanos Kollias
2009 IEEE transactions on multimedia  
In the former, detection is based on model vectors that represent image composition in terms of region types, obtained through clustering over a large data set.  ...  A set of algorithms is presented, which modify either the confidence values of detected concepts, or the model vectors based on which detection is performed.  ...  There it is shown that ontology-based concept learning improves the accuracy of a concept by considering the possible influence relations between all concepts based on a predefined ontology hierarchy.  ... 
doi:10.1109/tmm.2008.2009681 fatcat:73vxxnxpsrfwtpjayu7b6mi2gy

Enhancing Clinical Concept Extraction with Contextual Embedding [article]

Yuqi Si, Jingqi Wang, Hua Xu, Kirk Roberts
2019 arXiv   pre-print
Contextual embeddings pre-trained on a large clinical corpus achieves new state-of-the-art performances across all concept extraction tasks.  ...  We explore a battery of embedding methods consisting of traditional word embeddings and contextual embeddings, and compare these on four concept extraction corpora: i2b2 2010, i2b2 2012, SemEval 2014,  ...  Experimental Setting Concept Extraction Concept extraction is based on the model proposed in Lample et al., (2016) , a Bi-LSTM CRF architecture.  ... 
arXiv:1902.08691v3 fatcat:zwy4xfq5bjbelhuvl7hu6jdw54

Learning Taxonomies of Concepts and not Words using Contextualized Word Representations: A Position Paper [article]

Lukas Schmelzeisen, Steffen Staab
2019 arXiv   pre-print
We outline a novel approach for taxonomy learning that (1) defines concepts as synsets, (2) learns density-based approximations of contextualized word representations, and (3) can measure similarity and  ...  This position paper argues that contextualized word representations, which recently achieved state-of-the-art results on many competitive NLP tasks, are a promising method to address this limitation.  ...  The result of the previous step is a set of clusters (of contextualized word representation vectors) each characterizing one synset/concept.  ... 
arXiv:1902.02169v1 fatcat:xehjdyjdnfb23fxiyf7wlyw4wq

Discovering contextual tags from product review using semantic relatedness

Soon Chong Johnson Lim, Shilong Wang, Ying Liu
2014 Journal of Industrial and Production Engineering  
N/A N/A Table 4 . 4 Evaluation results for contextual annotation of example input query. … a new automated guided vehicle (agv) dispatching algorithm based on a bidding concept… … a flank wear estimation  ...  Generally, key term extraction can be viewed based on their learning approaches: supervised and unsupervised.  ... 
doi:10.1080/21681015.2014.895966 fatcat:53vma3dkorehhozap2gyiq6vyu

High-Level Concept Detection Based on Mid-Level Semantic Information and Contextual Adaptation

Phivos Mylonas, Evaggelos Spyrou, Yannis Avrithis
2007 Second International Workshop on Semantic Media Adaptation and Personalization (SMAP 2007)  
Prior work on low-level feature extraction is extended and a region thesaurus containing all mid-level features is constructed using a hierarchical clustering method.  ...  A model vector that contains the distances from each mid-level element is formed and a neural network-based detector is trained for each semantic concept.  ...  Finally, a mean-shift algorithm is used in [8] , in order to extract low-level concepts, after the image is clustered.  ... 
doi:10.1109/smap.2007.4414409 fatcat:zmpxji5ph5bgvjt33wrvquvtzu

High-Level Concept Detection Based on Mid-Level Semantic Information and Contextual Adaptation

Phivos Mylonas, Evaggelos Spyrou, Yannis Avrithis
2007 Second International Workshop on Semantic Media Adaptation and Personalization (SMAP 2007)  
Prior work on low-level feature extraction is extended and a region thesaurus containing all mid-level features is constructed using a hierarchical clustering method.  ...  A model vector that contains the distances from each mid-level element is formed and a neural network-based detector is trained for each semantic concept.  ...  Finally, a mean-shift algorithm is used in [8] , in order to extract low-level concepts, after the image is clustered.  ... 
doi:10.1109/smap.2007.38 dblp:conf/smap/MylonasSA07 fatcat:5qijyp6hp5h3rm2rsprwwtuwqy

"Climate Change" Frames Detection and Categorization Based on Generalized Concepts

Saud Alashri, Sultan Alzahrani, Jiun-Yi Tsai, Steven R. Corman, Hasan Davulcu
2016 International Journal of Semantic Computing (IJSC)  
In this paper, we develop a new type of textual features that generalize (subject,verb,object) triplets extracted from text, by clustering them into high-level concepts.  ...  Compared to unigram and bigram based models, classification using our generalized concepts yielded better discriminating features and a higher accuracy classifier with a 12% boost (i.e. from 74% to 83%  ...  Here the definition of contextual synonyms is not based on the one in the traditional dictionary.  ... 
doi:10.1142/s1793351x16400055 fatcat:wf2nrnfbsfeqdfxytjydpbhgru
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