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Short text categorization exploiting contextual enrichment and external knowledge
2014
Proceedings of the first international workshop on Social media retrieval and analysis - SoMeRA '14
The problem
• Short texts are growing
• (at least) 2 reasons
• Twitter 140 limit
• Mobile devices, input limitations
• Categorization of short texts, or #ShortTxtCateg
5
#ShortTxtCateg: ...
• #eval
• @home
13
Our approach
• Exploiting Wikipedia
• Search engine
• Article/category labels
• Category relationships
• Enrichment
• Exploiting search engines
• Time aware
• We ...
doi:10.1145/2632188.2632205
dblp:conf/sigir/MizzaroPSV14
fatcat:wpcszowqorbezmbqeur4dnxuey
Exploiting News to Categorize Tweets: Quantifying the Impact of Different News Collections
2016
European Conference on Information Retrieval
Short texts, due to their nature which makes them full of abbreviations and new coined acronyms, are not easy to classify. Text enrichment is emerging in the literature as a potentially useful tool. ...
This paper is a part of a longer term research that aims at understanding the effectiveness of tweet enrichment by means of news, instead of the whole web as a knowledge source. ...
Wikipedia category tree as external knowledge base. ...
dblp:conf/ecir/PavanMBS16
fatcat:flygl2pnlvbldbkumfolheat7u
Short Text Feature Enrichment Using Link Analysis on Topic-Keyword Graph
[chapter]
2014
Communications in Computer and Information Science
In this paper, we propose a novel feature enrichment method for short text classification based on the link analysis on topic-keyword graph. ...
At last, the short text is expanded by appending these related keywords for classification. Experimental results on two open datasets validate the effectiveness of the proposed method. ...
[5] proposed a graph-based text similarity measurement and exploited background knowledge from Wikipedia to find semantic affinity between documents. Phan et al. ...
doi:10.1007/978-3-662-45924-9_8
fatcat:hpbkigwc7vbvhgmutihkjhr7nq
Clustering of semantically enriched short texts
2018
Journal of Intelligent Information Systems
We present two approaches, one based on neural-based distributional models, and the other based on external knowledge resources. The approaches are tested on SnSRC and other knowledge-poor algorithms. ...
In addition, we test the possibilities of improving the quality of clustering ultra-short texts by means of enriching them semantically. ...
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. ...
doi:10.1007/s10844-018-0541-4
fatcat:eipabygtdrdr3ji7wqth6vic4a
Wiki3C
2013
Proceedings of the sixth ACM international conference on Web search and data mining - WSDM '13
In this paper, we exploit Wikipedia for a new task of text mining: Context-aware Concept Categorization. In the task, we focus on categorizing concepts according to their context. ...
We exploit article link feature and category structure in Wikipedia, followed by introducing Wiki3C, an unsupervised and domain independent concept categorization approach based on context. ...
Gabrilovich and Markovitch [2] enrich document representation through Wikipedia to improve the performance of text categorization. ...
doi:10.1145/2433396.2433441
dblp:conf/wsdm/JiangHCCYLW13
fatcat:ma2y3c5ebzbzlfeqb5lhgjkqoq
Leveraging Knowledge-Based Features With Multilevel Attention Mechanisms for Short Arabic Text Classification
2022
IEEE Access
When it comes to the Arabic language, the exploitation of external knowledge to support the classification of Arabic short text has not been widely explored. ...
A common solution is to enrich the short text with additional semantic features extracted from external knowledge, such as Wikipedia, to help the classifier better decide on the correct class. ...
However, when it comes to the Arabic language, little work has been done to exploit external knowledge bases to support the classification of Arabic short text. ...
doi:10.1109/access.2022.3175306
fatcat:jwn327xofzdyjpdcmrgo6mjyhy
A New Approach to Information Extraction in User-Centric E-Recruitment Systems
2019
Applied Sciences
Furthermore, job context information is expanded using a job description domain ontology based on the contextual and knowledge information. ...
The extracted information entities are enriched with knowledge using Linked Open Data. ...
existing data by adding more knowledge from external sources. ...
doi:10.3390/app9142852
fatcat:sy62dzuvcra7zfi44xm64ytwom
Toward a Deep Neural Approach for Knowledge-Based IR
[article]
2016
arXiv
pre-print
This latter issue is tackled by recent works dealing with deep representation learn ing of texts. ...
In this context, knowledge bases (KBs) have already been acknowledged as valuable means since they allow the representation of explicit relations between entities. ...
This model extends the objective function of the skip-gram model [10] with two regularization functions based on relational and categorical knowledge from the external resource, respectively. ...
arXiv:1606.07211v1
fatcat:jdypcyno3zcwphnoclk44dsfxi
Exploiting Background Knowledge for Argumentative Relation Classification
2019
International Conference on Language, Data, and Knowledge
We propose an argumentative relation classification system that employs linguistic as well as knowledge-based features, and investigate the effects of injecting background knowledge into a neural baseline ...
This paper explores the difficulties and the potential effectiveness of knowledge-enhanced argument analysis, with the aim of advancing the state-of-the-art in argument analysis towards a deeper, knowledge-based ...
This corpus consists of 112 short argumentative texts [19] . The corpus was created in German and has been translated to English. We use only the English version. ...
doi:10.4230/oasics.ldk.2019.8
dblp:conf/ldk/KobbeOBHSF19
fatcat:etbraev7lfavnjc7wflkselgsi
Towards Enriching DBpedia from Vertical Enumerative Structures Using a Distant Learning Approach
[chapter]
2018
Lecture Notes in Computer Science
The remain semistructured textual structures, such as vertical enumerative structures (those using typographic and dispositional layout) have been however under-exploited. ...
Our relation extraction approach achieves an overall precision of 62%, and 99% of the extracted relations can enrich DBpedia, with respect to a reference corpus. ...
They are however under-exploited by knowledge approaches aiming at enriching semantic resources. ...
doi:10.1007/978-3-030-03667-6_12
fatcat:e7uv32y2sngvjj2wn4z6wcudve
Term Categorization Using Latent Semantic Analysis for Intelligent Query Processing
2019
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
The objective is to retrieve only relevant documents by categorizing the short texts. In the proposed method, terms are categorized by means of Latent Semantic Analysis (LSA). ...
Understanding and categorizing these texts for effective query processing is considered as one of the vital defy in the field of Natural Language Processing. ...
The major challenging task is to understand and categorize these short texts. ...
doi:10.35940/ijitee.a1065.1191s19
fatcat:2lnqh6nwtfhzbn6z4gbfvxwu24
Incorporating External Knowledge into Machine Reading for Generative Question Answering
2019
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
This allows the model to exploit external knowledge that is not explicitly stated in the given text, but that is relevant for generating an answer. ...
In this paper, we propose a new neural model, Knowledge-Enriched Answer Generator (KEAG), which is able to compose a natural answer by exploiting and aggregating evidence from all four information sources ...
the contextual passage and external knowledge. ...
doi:10.18653/v1/d19-1255
dblp:conf/emnlp/BiWYWXL19
fatcat:styjmolrzjbyhccarp36naqwvm
Incorporating External Knowledge into Machine Reading for Generative Question Answering
[article]
2019
arXiv
pre-print
This allows the model to exploit external knowledge that is not explicitly stated in the given text, but that is relevant for generating an answer. ...
In this paper, we propose a new neural model, Knowledge-Enriched Answer Generator (KEAG), which is able to compose a natural answer by exploiting and aggregating evidence from all four information sources ...
the contextual passage and external knowledge. ...
arXiv:1909.02745v1
fatcat:kae4hidiejfm7fbstcv45mz4um
Query Extension with Improved User Profiles for User tailored Search taking Advantage over Folksonomy Data
2018
International Journal for Research in Applied Science and Engineering Technology
user profiles with the assistance of associate external corpus for customized question growth. ...
Query expansion has been widely adopted in web search as the simplest way of endeavour the anomaly of queries. customized search utilizing folksonomy knowledge has incontestible associate extreme vocabulary ...
organize their on-line bookmarks with freely chosen short text descriptors. ...
doi:10.22214/ijraset.2018.6110
fatcat:nxvd2uqm7vgdtggmjqmuoxl37e
Context-aware Image Tweet Modelling and Recommendation
2016
Proceedings of the 2016 ACM on Multimedia Conference - MM '16
We start with tweet's intrinsic contexts, namely, 1) text within the image itself and 2) its accompanying text; and then we turn to the extrinsic contexts: 3) the external web page linked to by the tweet's ...
To bridge this gap, we move from the images' pixels to their context and propose a context-aware image tweet modelling (CITING) framework to mine and fuse contextual text to model such social media images ...
We also would like to thank Yongfeng Zhang and Hanwang Zhang for their help and discussions. ...
doi:10.1145/2964284.2964291
dblp:conf/mm/ChenHK16
fatcat:nepnjdu5vbffbhrhkisac5p5fe
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