Search (200 results, page 9 of 10)

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Abstract

Purpose - The purpose of this research is to compare several machine learning techniques on the task of Asian language text classification, such as Chinese and Japanese where no word boundary information is available in written text. The paper advocates a simple language modeling based approach for this task. Design/methodology/approach - Naïve Bayes, maximum entropy model, support vector machines, and language modeling approaches were implemented and were applied to Chinese and Japanese text classification. To investigate the influence of word segmentation, different word segmentation approaches were investigated and applied to Chinese text. A segmentation-based approach was compared with the non-segmentation-based approach. Findings - There were two findings: the experiments show that statistical language modeling can significantly outperform standard techniques, given the same set of features; and it was found that classification with word level features normally yields improved classification performance, but that classification performance is not monotonically related to segmentation accuracy. In particular, classification performance may initially improve with increased segmentation accuracy, but eventually classification performance stops improving, and can in fact even decrease, after a certain level of segmentation accuracy. Practical implications - Apply the findings to real web text classification is ongoing work. Originality/value - The paper is very relevant to Chinese and Japanese information processing, e.g. webpage classification, web search.

Type

a

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Abstract

The following paper deals with an automatic text classification method which does not require training documents. For this method the German Subject Heading Authority File (SWD), provided by the linked data service of the German National Library is used. Recently the SWD was enriched with notations of the Dewey Decimal Classification (DDC). In consequence it became possible to utilize the subject headings as textual representations for the notations of the DDC. Basically, we we derive the classification of a text from the classification of the words in the text given by the thesaurus. The method was tested by classifying 3826 OAI-Records from 7 different repositories. Mean reciprocal rank and recall were chosen as evaluation measure. Direct comparison to a machine learning method has shown that this method is definitely competitive. Thus we can conclude that the enriched version of the SWD provides high quality information with a broad coverage for classification of German scientific articles.

Source

Proceedings of the 2nd International Workshop on Semantic Digital Archives held in conjunction with the 16th Int. Conference on Theory and Practice of Digital Libraries (TPDL) on September 27, 2012 in Paphos, Cyprus [http://ceur-ws.org/Vol-912/proceedings.pdf]. Eds.: A. Mitschik et al

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Abstract

Automatic text classification (TC) continues to be a relevant research topic and several TC algorithms have been proposed. However, the majority of TC algorithms assume that the underlying data distribution does not change over time. In this work, we are concerned with the challenges imposed by the temporal dynamics observed in textual data sets. We provide evidence of the existence of temporal effects in three textual data sets, reflected by variations observed over time in the class distribution, in the pairwise class similarities, and in the relationships between terms and classes. We then quantify, using a series of full factorial design experiments, the impact of these effects on four well-known TC algorithms. We show that these temporal effects affect each analyzed data set differently and that they restrict the performance of each considered TC algorithm to different extents. The reported quantitative analyses, which are the original contributions of this article, provide valuable new insights to better understand the behavior of TC algorithms when faced with nonstatic (temporal) data distributions and highlight important requirements for the proposal of more accurate classification models.

Type

a

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Abstract

Classifying papers according to the fields of knowledge is critical to clearly understand the dynamics of scientific (sub)fields, their leading questions, and trends. Most studies rely on journal categories defined by popular databases such as WoS or Scopus, but some experts find that those categories may not correctly map the existing subfields nor identify the subfield of a specific article. This study addresses the classification problem using data from each paper (Abstract, Title, Keywords, and the KeyWords Plus) and the help of experts to identify the existing subfields and journals exclusive of each subfield. These "exclusive journals" are critical to obtain, through a pattern detection procedure that uses machine learning techniques (from software NVivo), a list of the frequent terms that are specific to each subfield. With that list of terms and with the help of optimization procedures, we can identify to which subfield each paper most likely belongs. This study can contribute to support scientific policy-makers, funding, and research institutions-via more accurate academic performance evaluations-, to support editors in their tasks to redefine the scopes of journals, and to support popular databases in their processes of refining categories.

Type

a

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Abstract

In a scientific and technological watch (STW) task, an expert user needs to survey the evolution of research topics in his area of specialisation in order to detect interesting changes. The majority of methods proposing evaluation metrics (bibliometrics and scientometrics studies) for STW rely solely an statistical data analysis methods (Co-citation analysis, co-word analysis). Such methods usually work an structured databases where the units of analysis (words, keywords) are already attributed to documents by human indexers. The advent of huge amounts of unstructured textual data has rendered necessary the integration of natural language processing (NLP) techniques to first extract meaningful units from texts. We propose a method for STW which is NLP-oriented. The method not only analyses texts linguistically in order to extract terms from them, but also uses linguistic relations (syntactic variations) as the basis for clustering. Terms and variation relations are formalised as weighted di-graphs which the clustering algorithm, CPCL (Classification by Preferential Clustered Link) will seek to reduce in order to produces classes. These classes ideally represent the research topics present in the corpus. The results of the classification are subjected to validation by an expert in STW.

Type

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Abstract

The authors describe their research which improves software reuse by using an automated approach to semantically search for and retrieve reusable software components in large software component repositories and on the World Wide Web (WWW). Using automation and smart (semantic) techniques, their approach speeds up the search and retrieval of reusable software components, while retaining good accuracy, and therefore improves the affordability of software reuse. A program understanding of software components and natural language understanding of user queries was employed. Then the software component descriptions were compared by matching the resulting semantic representations of the user queries to the semantic representations of the software components to search for software components that best match the user queries. A proof of concept system was developed to test the authors' approach. The results of this proof of concept system were compared to human experts, and statistical analysis was performed on the collected experimental data. The results from these experiments demonstrate that this automated semantic-based approach for software reusable component classification and retrieval is successful when compared to the labor-intensive results from the experts, thus showing that this approach can significantly benefit software reuse classification and retrieval.

Type

a

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Abstract

Purpose - The purpose of this study is twofold: to investigate whether it is meaningful to use the Engineering Index (Ei) classification scheme for browsing, and then, if proven useful, to investigate the performance of an automated classification algorithm based on the Ei classification scheme. Design/methodology/approach - A user study was conducted in which users solved four controlled searching tasks. The users browsed the Ei classification scheme in order to examine the suitability of the classification systems for browsing. The classification algorithm was evaluated by the users who judged the correctness of the automatically assigned classes. Findings - The study showed that the Ei classification scheme is suited for browsing. Automatically assigned classes were on average partly correct, with some classes working better than others. Success of browsing showed to be correlated and dependent on classification correctness. Research limitations/implications - Further research should address problems of disparate evaluations of one and the same web page. Additional reasons behind browsing failures in the Ei classification scheme also need further investigation. Practical implications - Improvements for browsing were identified: describing class captions and/or listing their subclasses from start; allowing for searching for words from class captions with synonym search (easily provided for Ei since the classes are mapped to thesauri terms); when searching for class captions, returning the hierarchical tree expanded around the class in which caption the search term is found. The need for improvements of classification schemes was also indicated. Originality/value - A user-based evaluation of automated subject classification in the context of browsing has not been conducted before; hence the study also presents new findings concerning methodology.

Type

a

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Abstract

Recent studies of authorship attribution have used machine-learning methods including regularized multinomial logistic regression, neural nets, support vector machines, and the nearest shrunken centroid classifier to identify likely authors of disputed texts. These methods are all limited by an inability to perform open-set classification and account for text and corpus size. We propose a customized Bayesian logit-normal-beta-binomial classification model for supervised authorship attribution. The model is based on the beta-binomial distribution with an explicit inverse relationship between extra-binomial variation and text size. The model internally estimates the relationship of extra-binomial variation to text size, and uses Markov Chain Monte Carlo (MCMC) to produce distributions of posterior authorship probabilities instead of point estimates. We illustrate the method by training the machine-learning methods as well as the open-set Bayesian classifier on undisputed papers of The Federalist, and testing the method on documents historically attributed to Alexander Hamilton, John Jay, and James Madison. The Bayesian classifier was the best classifier of these texts.

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Abstract

Autonomy's Classification solutions remove the necessity for organizations to rely on human intervention or manual processing of information, such as manual tagging, typically required to make most other e-business applications work. Autonomy's ability to consistently and accurately classify data automatically is a unique infrastructure solution that overcomes the predicaments surrounding the exponential growth of unstructured data.

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