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Recognition of Short Handwritten Texts
2004Building on our 10-year-experience with script recognition systems, a new reader generation was designed. Previously, only single handwritten words were compared against a dictionary. Now a short text is modeled and processed as a whole. The system does not proceed in a linear fashion anymore, but uses feedback between image processing, character ...
Michael Boldt, Christopher Asp
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From Text Classification to Keyphrase Extraction for Short Text
2019 IEEE International Conference on Big Data (Big Data), 2019Existing keyphrase extraction approaches often suffer from issues such as the sparsity and brevity of short text (e.g., headlines, queries, and tweets). In this paper, we propose a novel keyphrase extraction method for short text by utilizing recurrent neural networks. The main idea behind our approach is to classify short text into a relevant class or
Song-Eun Lee +4 more
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Leveraging Conceptualization for Short-Text Embedding
IEEE Transactions on Knowledge and Data Engineering, 2018Most short-text embedding models typically represent each short-text only using the literal meanings of the words, which makes these models indiscriminative for the ubiquitous polysemy. In order to enhance the semantic representation capability of the short-texts, we (i) propose a novel short-text conceptualization algorithm to assign the associated ...
Yashen Wang, Heyan Huang, Chong Feng
exaly +2 more sources
2017
Recent increases in the use and availability of short messages have created opportunities to harvest vast amounts of information through machine-based classification. However, traditional classification methods have failed to yield accuracies comparable to classification accuracies on longer texts.
McCartney, Austin +2 more
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Recent increases in the use and availability of short messages have created opportunities to harvest vast amounts of information through machine-based classification. However, traditional classification methods have failed to yield accuracies comparable to classification accuracies on longer texts.
McCartney, Austin +2 more
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Short text keyphrase extraction with hypergraphs
Progress in Artificial Intelligence, 2014Graph-based ranking for keyphrase extraction has become an important approach for measuring saliency scores in text due to its ability to capture the context. By modeling words as vertices and the co-occurrence relation between words as edges, the importance of words is measured from the whole graph.
Abdelghani Bellaachia +1 more
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2013
Many applications handle short texts, and enableing machines to understand short texts is a big challenge. For example, in Ads selection, it is is difficult to evaluate the semantic similarity between a search query and an ad. Clearly, edit distance based string similarity does not work.
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Many applications handle short texts, and enableing machines to understand short texts is a big challenge. For example, in Ads selection, it is is difficult to evaluate the semantic similarity between a search query and an ad. Clearly, edit distance based string similarity does not work.
openaire +1 more source
Author Gender Identification for Short Texts
Computación y Sistemas, 2021At present, the possibility of communicating or expressing oneself through an electronic medium is very wide: most users of computers and mobile devices use email, social networks, chats and other tools. One of the problems that has arisen with this form of communication is excess, such as plagiarism, false identity, intimidating notes, and others. The
Francisco Antonio Castillo Velásquez +4 more
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A biterm topic model for short texts
Proceedings of the 22nd international conference on World Wide Web, 2013Uncovering the topics within short texts, such as tweets and instant messages, has become an important task for many content analysis applications. However, directly applying conventional topic models (e.g. LDA and PLSA) on such short texts may not work well.
Xiaohui Yan +3 more
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Introducing Semantics in Short Text Classification
2018To overcome short text classification issues due to shortness and sparseness, the enrichment process is classically proposed: topics (word clusters) are extracted from external knowledge sources using Latent Dirichlet Allocation. All the words, associated to topics which encompass short text words, are added to the initial short text content.
Ameni Bouaziz +3 more
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An Improved PLDA Model for Short Text
2017The boost of short texts is increasing demands for short text classification. Feature extraction based on topic models, especially those utilizing label information, such as Partially Labeled Dirichlet Allocation (PLDA), has been widely employed in normal text classification.
Chaotao Chen, Jiangtao Ren
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