Exploring events and distributed representations of text in multi-document summarization

Marujo, Luís; Ribeiro, Ricardo; Gershman, Anatole; Carbonell, Jaime G.; Matos, David Martins de; Neto, João Paulo

doi:10.1016/j.knosys.2015.11.005

Cited by 37 publications

(20 citation statements)

References 20 publications

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“…Deep learning technology is applied in common NLP (natural language processing) tasks, such as semantic parsing [ 43 ], information retrieval [ 44 , 45 ], semantic role labeling [ 46 , 47 ], sentimental analysis [ 48 ], question answering [ 49 – 52 ], machine translation [ 53 – 56 ], text classification [ 57 ], summarization [ 58 , 59 ], and text generation [ 60 ], as well as information extraction, including named entity recognition [ 61 , 62 ], relation extraction [ 63 – 67 ], and event detection [ 68 – 70 ]. Convolution neural network and recurrent neural network are two popular models employed by this work [ 71 ].…”

Section: Reviewmentioning

confidence: 99%

Text feature extraction based on deep learning: a review

Hong

Sun

et al. 2017

J Wireless Com Network

246

120

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Selection of text feature item is a basic and important matter for text mining and information retrieval. Traditional methods of feature extraction require handcrafted features. To hand-design, an effective feature is a lengthy process, but aiming at new applications, deep learning enables to acquire new effective feature representation from training data. As a new feature extraction method, deep learning has made achievements in text mining. The major difference between deep learning and conventional methods is that deep learning automatically learns features from big data, instead of adopting handcrafted features, which mainly depends on priori knowledge of designers and is highly impossible to take the advantage of big data. Deep learning can automatically learn feature representation from big data, including millions of parameters. This thesis outlines the common methods used in text feature extraction first, and then expands frequently used deep learning methods in text feature extraction and its applications, and forecasts the application of deep learning in feature extraction.

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Section: Reviewmentioning

confidence: 99%

Text feature extraction based on deep learning: a review

Hong

Sun

et al. 2017

J Wireless Com Network

246

120

View full text Add to dashboard Cite

show abstract

“…In recent years, the research has also been focusing on distributed text representations, which uses embedded models to identify important text features by eliminating less crucial and redundant pieces of information [92]. This allows to model representation optimising the trade-off between training performance and domain portability [60].…”

Section: Website Content Modellingmentioning

confidence: 99%

Acquisition and Modelling of Short-TermUser Behaviour on theWeb: A Survey

Kassak

Kompan

Bieliková

2018

JWE

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User behaviour in data intensive applications such as the Web-based applications represents a complex set of actions influenced by plenty of factors. Thanks to this complexity, it is extremely hard for human to be able to understand all its aspects. Despite of this, by observing user actions from multiple views, we are able to extract and to model typical behaviour and its deviations on the Web. The website itself, together with transaction server logs, includes information about the site structure, content and about the actual user actions (clicks) within the site. User actions logically reflect the behaviour, while other sources indicate his/her context. Combination of these data sources allows to model the typical user behaviour and his/her preferences. The longterm behaviour describes relatively stable user preferences based on extensive user history. As the Web has become more and more dynamic, modelling user behaviour from the long-term perspective does not satisfy requirements of current Web based applications. On the other side, the short-term behaviour describes current user activity and his/her actual intent. However, this source of information is often noisy. To address these shortcomings the state-ofthe-art combines both perspectives, which allows to meaningful and timely modelling of user behaviour. In this paper, we provide a comprehensive survey

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“…These powerful, efficient models have shown very promising results in capturing both semantic and syntactic relationships between words in large-scale text corpora, and obtained state-of-the-art results on many NLP tasks. Recently, the concept of embedding has been expanded to many applications, including sentences and paragraphs representation [11], summarization [21], questions answering [43], recommender systems [34] and so on.…”

Section: Embeddingmentioning

confidence: 99%

Learning to embed music and metadata for context-aware music recommendation

et al. 2017

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Contextual factors greatly influence users' musical preferences, so they are beneficial remarkably to music recommendation and retrieval tasks. However, it still needs to be studied how to obtain and utilize the contextual information. In this paper, we propose a context-aware music recommendation approach, which can recommend music pieces appropriate for users' contextual preferences for music. In analogy to matrix factorization methods for collaborative filtering, the proposed approach does not require music pieces to be represented by features ahead, but it can learn the representations from users' historical listening records. Specifically, the proposed approach first learns music pieces' embeddings (feature vectors in low-dimension continuous space) from music listening records and corresponding metadata. Then it infers and models users' global and contextual preferences for music from their listening records with the learned embeddings. Finally, it recommends appropriate music pieces according to the target user's preferences to satisfy her/his real-time requirements. Experimental evaluations on a real-world dataset show that the proposed approach outperforms baseline methods in terms of precision, recall, F1 score, and hitrate. Especially, our approach has better performance on sparse datasets.

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Exploring events and distributed representations of text in multi-document summarization

Cited by 37 publications

References 20 publications

Text feature extraction based on deep learning: a review

Text feature extraction based on deep learning: a review

Acquisition and Modelling of Short-TermUser Behaviour on theWeb: A Survey

Learning to embed music and metadata for context-aware music recommendation

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