[PDF] Top 20 Hierarchical Text Classification with Reinforced Label Assignment

... existing hierarchical text classification (HTC) methods attempt to capture label hier- archies for model training, they either make local decisions regarding each label or com- pletely ... See full document

... and text classification. Yang et al. (2016) explore the hierarchical structure of docu- ments classification, they use a GRU-based atten- tion to build representations of sentences and an- ... See full document

... state-of-the-art text encoders at en- coder layer, including FastText, TextCNN, Tex- tRNN, RCNN, VDCNN, DPCNN, DRNN, Trans- former encoder, Star-Transformer encoder and At- ...cific classification tasks, ... See full document

... Obtaining consistent labelings. As the learning algorithms compared here all decompose the hierar- chy for learning, the multilabel composed of naively combining the microlabel predictions may be incon- sistent, that is, ... See full document

... the hierarchical classification. In other words, classification mis- takes at one level of the hierarchy carry on as mis- takes at the next ...the hierarchical classification approach ... See full document

... free-form text gener- ated by clinicians during patient encounters, and a set of metadata diagnosis codes from the Inter- national Classification of Diseases (ICD), which represent the diagnoses and ... See full document

... in text style transfer, ...a classification confi- dence reward and an auxiliary classification task; for content preservation, we adopt a reconstruc- tion reward and a self-supervised reconstruction ... See full document

... Predictive text embedding (Tang, Qu, and Mei 2015) utilizes both labeled and unlabeled documents to learn text embedding specifically for a ...heterogeneous text net- work and then embedded into a ... 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

... HAN : The Hierarchical Attention Network (Yang et al., 2016) is a strong baseline for text classification. We use a slightly modified version, where a BIGRU with self-attention reads the words of ... See full document

... class label text affects the effectiveness of the pro- posed ...class label text has, the more effective the proposed method ...short label text such as O THER -D ISCUSSION , ... See full document

... In hierarchical multi-label classification (HMC), samples are classified into one or multiple class labels that are organized in a structured label hier- archy (Silla and Freitas, ...robust ... 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, ...Multi-Label Classification (XMC) ... 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 ... See full document

... In this paper, we propose to exploit inter-label dependencies. To do this, we first formulate hier- archical multi-label text classification as a struc- tured prediction problem. We propose a ... See full document

... capture label correlations, but also alleviates the strict requirements of Seq2Seq for label order via reinforcement ...reasonable label or- der on the uniform distribution, leading to excel- lent ... See full document

... From the experimental result, the time based analysis shows C- mean is most efficient algorithm for taking less time for processing the text classification of dataset. The numbers of clusters are generated ... See full document

... A number of approaches learn the transition from latent labels to the noisy labels. For example, Mnih and Hinton (2012) propose a noise adapta- tion framework for symmetric label noise. Based on this work, several ... See full document

... any classification strategy to be em- ployed, including discriminative classifiers such as logistic ...the hierarchical classification strategy discussed in §4 allows us to take advan- tage of ... 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