Temporally-aware algorithms for document classification

Salles, Thiago; Rocha, Leonardo; Pappa, Gisele L.; Mourão, Fernando; Meira, Wagner; Gonçalves, Marcos André

doi:10.1145/1835449.1835502

Cited by 24 publications

(27 citation statements)

References 16 publications

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“…The classifier has also been extended to modeling temporally aware training data, in which the importance of a document may decay with time [114]. As in the case of other statistical classifiers, the naive Bayes classifier [113] can easily incorporate domain-specific knowledge into the classification process.…”

Section: If We Sampled a Term Set T Of Any Size From The Term Distribmentioning

confidence: 99%

“…A similar observation has been made in [54], in which it has been shown that the addition of weights to the terms (based on their class-sensitivity) significantly improves the underlying classifier performance. The nearest neighbor classifier has also been extended to the temporally-aware scenario [114], in which the timeliness of a training document plays a role in the model construction process.…”

Section: Proximity-based Classifiersmentioning

confidence: 99%

“…In order to incorporate such factors, a temporal weighting function has been introduced in [114], which allows the importance of a document to gracefully decay with time.…”

Section: Proximity-based Classifiersmentioning

confidence: 99%

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A Survey of Text Classification Algorithms

2012

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Section: If We Sampled a Term Set T Of Any Size From The Term Distribmentioning

confidence: 99%

Section: Proximity-based Classifiersmentioning

confidence: 99%

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A Survey of Text Classification Algorithms

2012

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“…Mourao et al investigated the impact of temporal evolution of document collections on the document classification (Mourao et al, 2008). Salles et al presented an approach to classify documents in scenarios where the method uses information about both the past and the future, and this information may change over time (Salles et al, 2010). They address the drawbacks of which instances to select by approximating the Temporal Weighting Function (TWF) using a mixture of two Gaussians.…”

Section: Related Workmentioning

confidence: 99%

Learning Timeline Difference for Text Categorization

Fukumoto

Suzuki

2015

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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This paper addresses text categorization problem that training data may derive from a different time period from the test data. We present a learning framework which extends a boosting technique to learn accurate model for timeline adaptation. The results showed that the method was comparable to the current state-of-theart biased-SVM method, especially the method is effective when the creation time period of the test data differs greatly from the training data.

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“…For instance, Miao and Kamel (2011) have re-examined the applicable assumptions and parameter optimization method of the traditional Rocchio algorithm and proposed an enhanced version of this method that clearly outperforms the former one by using a pairwise optimized strategy. Salles et al (2010) also presents a methodology to determine the impact that may have temporal effects on TC and to minimize it. By extending the three algorithms (namely kNN, Rocchio and NB) to incorporate a Temporal Weighting Function (TWF), experiments showed that these temporally-aware classifiers achieved significant gains, outperforming (or at least matching) state-of-the-art algorithms.…”

Section: Vector Space Classificationmentioning

confidence: 99%

Text categorization methods for automatic estimation of verbal intelligence

Fernández-Martínez

Zablotskaya

Minker

2012

Expert Systems with Applications

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In this paper we investigate whether conventional text categorization methods may suffice to infer different verbal intelligence levels. This research goal relies on the hypothesis that the vocabulary that speakers make use of reflects their verbal intelligence levels. Automatic verbal intelligence estimation of users in a spoken language dialog system may be useful when defining an optimal dialog strategy by improving its adaptation capabilities. The work is based on a corpus containing descriptions (i.e. monologs) of a short film by test persons yielding different educational backgrounds and the verbal intelligence scores of the speakers. First, a one-way analysis of variance was performed to compare the monologs with the film transcription and to demonstrate that there are differences in the vocabulary used by the test persons yielding different verbal intelligence levels. Then, for the classification task, the monologs were represented as feature vectors using the classical TF-IDF weighting scheme. The Naive Bayes, /c-nearest neighbors and Rocchio classifiers were tested. In this paper we describe and compare these classification approaches, define the optimal classification parameters and discuss the classification results obtained.

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Temporally-aware algorithms for document classification

Cited by 24 publications

References 16 publications

A Survey of Text Classification Algorithms

A Survey of Text Classification Algorithms

Learning Timeline Difference for Text Categorization

Text categorization methods for automatic estimation of verbal intelligence

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