Automatic Indexing: An Experimental Inquiry

Maron, M. E.

doi:10.1145/321075.321084

Cited by 445 publications

(215 citation statements)

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“…The early research work on text categorization focused on how to use a limited number of key words extracted from a given document to index, or classify the document into a predefined set of subject categories. For example, Maron [11] proposed to use statistical technique to automatically index documents. In Maron's experiments, documents were short and clearly written, and the topics of the documents were limited not too heterogeneous.…”

Section: Related Workmentioning

confidence: 99%

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An evaluation study on text categorization using automatically generated labeled dataset

Zhu

Wong

2017

Neurocomputing

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Naïve Bayes, k-Nearest Neighbours, Adaboost, Support Vector Machines and Neural Networks are five among others commonly used text classifiers. Evaluation of these classifiers involves a variety of factors to be considered including benchmark used, feature selections, parameter settings of algorithms, and the measurement criteria employed. Researchers have demonstrated that some algorithms outperform others on some corpus, however, inconsistency of human labelling and high dimensionality of feature spaces are two issues to be addressed in text categorization. This paper focuses on evaluating the five commonly used text classifiers by using an automatically generated text document collection which is labelled by a group of experts to alleviate subjectivity of human category assignments, and at the same time to examine the influence of the number of features on the performance of the algorithms.

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Section: Related Workmentioning

confidence: 99%

“…An automatically generated labelled dataset, the Open Directory Project dataset -CategoryDocuments [11], is employed as the experimental dataset to evaluate the above five widely used text categorization algorithms.…”

Section: Experimental Datasetmentioning

confidence: 99%

An evaluation study on text categorization using automatically generated labeled dataset

Zhu

Wong

2017

Neurocomputing

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“…The English senses together with their aligned translations (and probability scores) are then stored in a word sense translation table, in which look-ups are performed during the testing phase. -The Naive Bayes (NB) [11] classifier is a probabilistic classifier that assumes that the features are independent. We compare with this classifier because it has similarities with our new approach, that is, it is also based on the frequencies of the features, but it does not take into account the sparse nature of the data.…”

Section: Experimental Set-upmentioning

confidence: 99%

SBFC: An Efficient Feature Frequency-Based Approach to Tackle Cross-Lingual Word Sense Disambiguation

Mourisse

Lefever

Verbiest

et al. 2012

Text, Speech and Dialogue

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Abstract. The Cross-Lingual Word Sense Disambiguation (CLWSD) problem is a challenging Natural Language Processing (NLP) task that consists of selecting the correct translation of an ambiguous word in a given context. Different approaches have been proposed to tackle this problem, but they are often complex and need tuning and parameter optimization.In this paper, we propose a new classifier, Selected Binary Feature Combination (SBFC), for the CLWSD problem. The underlying hypothesis of SBFC is that a translation is a good classification label for new instances if the features that occur frequently in the new instance also occur frequently in the training feature vectors associated with the same translation label.The advantage of SBFC over existing approaches is that it is intuitive and therefore easy to implement. The algorithm is fast, which allows processing of large text mining data sets. Moreover, no tuning is needed and experimental results show that SBFC outperforms state-of-the-art models for the CLWSD problem w.r.t. accuracy.

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“…Unlike ROTE, however, Naive Bayes can be competitive with more powerful methods (Domingos & Pazzani, 1996). The idea of a Bayesian method for text classification is almost as old as the idea of automatic text classification itself (Maron, 1961). Lewis discusses the uses of such methods for text classification and retrieval (1992) and provides a large list of pointers to related research (1997).…”

Section: Related Workmentioning

confidence: 99%

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Freitag¹

2000

Machine Learning

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Abstract.We consider the problem of learning to perform information extraction in domains where linguistic processing is problematic, such as Usenet posts, email, and finger plan files. In place of syntactic and semantic information, other sources of information can be used, such as term frequency, typography, formatting, and mark-up. We describe four learning approaches to this problem, each drawn from a different paradigm: a rote learner, a term-space learner based on Naive Bayes, an approach using grammatical induction, and a relational rule learner. Experiments on 14 information extraction problems defined over four diverse document collections demonstrate the effectiveness of these approaches. Finally, we describe a multistrategy approach which combines these learners and yields performance competitive with or better than the best of them. This technique is modular and flexible, and could find application in other machine learning problems.

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Automatic Indexing: An Experimental Inquiry

Cited by 445 publications

References 1 publication

An evaluation study on text categorization using automatically generated labeled dataset

An evaluation study on text categorization using automatically generated labeled dataset

SBFC: An Efficient Feature Frequency-Based Approach to Tackle Cross-Lingual Word Sense Disambiguation

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