[PDF] Top 20 Label Specific Document Representation for Multi Label Text Classification

... one document may contain mul- tiple labels, and each label can be taken as one aspect or component of the document, thus, the overall semantics of the whole document can be formed by multiple ... See full document

... book’s label is assigned to it as ...most specific genre of a book does not have to be a ...most specific category of a book could be Chil- dren’s Books, although Children’s Books has fur- ther ... See full document

... Single-label classification is to learn from a set of instances, each instance associated with a unique class label from a set of disjoint class labels ...B. Multi-label ... See full document

... Graph-based text categorization. In the re- lated literature, most of the graph-based method for TC, rely on graph mining algorithms that are applied to extract frequent subgraphs, which are then used to produce ... See full document

... HTrans (Hierarchical Transfer Learning) is based on a recursive strategy of training parent and child category classifiers. Say, P1 is a top-level category with C1 as one of its children. Also, lets consider C12 as a ... See full document

... of multi- label, influence of classifier construct have not explored enough which have a high impacts in the prediction of class labels and even in literature this problem is weakly being studied until ... See full document

... the multi-label classification process, where the CLR approach was used to as the problem transformation approach [3] ...evaluate classification results that have been obtained ...vector ... See full document

... each label in the taxonomy or ontology where all documents not belonging to the class are consid- ered negative examples, ...) classification (Hong and Cho, ... See full document

... Text classification has been widely exploited to improve the performance in information retrieval, information organising, text categorisation, and knowl- edge ...Traditionally, text ... See full document

... the classification experi- ments, 1000 labeled documents from source lan- guage are selected to train a multi-class classifier using averaged perceptron (Freund and Schapire, 1999; Collins, 2002) and 5000 ... See full document

... Large-scale multi-label text classification ( LMTC ) is the task of assigning to each document all the relevant labels from a large set, typically contain- ing thousands of labels ... See full document

... A special design for the CNN is the implemen- tation of dilated convolution. Dilation has be- come popular in semantic segmentation in com- puter vision in recent years (Yu and Koltun, 2015; Wang et al., 2018), and it ... See full document

... Similar concerns have led to the introduction of R-Precision and nDCG@K in Information Re- trieval (Manning et al., 2009), which we believe are also more appropriate for XMTC . Note, how- ever, that R-Precision requires ... See full document

... of text classification system is to determine the correct class of a new text document based on some training ...building text classifier is an interesting area for ...of text ... See full document

... Thus text classification has become an active research topic now a ...classifies document under a predefined ...approach, classification of text was carried out manually using domain ... See full document

... Supervised classification problems in machine learning involve identifying the ‘class’ of a particular item (such as an image, a document, an audio or video clip, ...Extreme Multi-Label ... See full document

... The specific task was to categorize the 23 cardiovascular diseases ...the text categorization is to “allocate” the documents to one or multiple categories of the total 23 Cardio vascular ...A ... See full document

... or document classification (Collobert et ...simple multi-layer perceptron with three lay- ...for multi-label learning of an MLP was improved in (Zhang and Zhou, ... See full document

... labor-intensive. Multi-Task Learn- ing (MTL) solves this problem by jointly train- ing multiple related tasks and leveraging poten- tial correlations among them to increase corpora size implicitly, extract common ... See full document

... for multi-label document classification, which em- ploys an attention mechanism to adaptively pool the convolution output for each label, learning to identify highly-predictive ... See full document