Document Classification

This paper introduces an algorithm for automatically acquiring the conceptual struc- ture of each word from corpus. The concept of a word is defined within the proba- bilistic framework. A …...

We propose a new method of classifying documents into categories. We define for each category a finite mixture model based on soft clustering of words. We treat the problem of …...

We present new results for the DSTO project on document classification of military messages. We report more specifically on the improvements to the Part-Of-Speech (POS) tagging, probabilistic process that assigns …...

. We propose a novel type of document classification task that quantifies how much a given document (review) appreciates the target object using not binary polarity (good or bad) but …...

This paper addresses the fundamental problem of document classification, and we focus attention on classification prob- lems where the classes are mutually exclu- sive. In the course of the paper …...

Previously topic models such as PLSI (Prob- abilistic Latent Semantic Indexing) and LDA (Latent Dirichlet Allocation) were developed for modeling the contents of plain texts. Re- cently, topic models for …...

Document clustering and classification is usually done by representing the documents using a bag of words scheme. This scheme ignores many of the linguistic and semantic features contained in text …...

The (batch) EM algorithm plays an important role in unsupervised induction, but it some- times suffers from slow convergence. In this paper, we show that online variants (1) provide significant …...

In this paper an unsupervised approach to do- main adaptation is presented, which exploits external knowledge sources in order to port a classification model into a new thematic do- main. …...

The first step in graph-based semi-supervised classification is to construct a graph from in- put data. While the k-nearest neighbor graphs have been the de facto standard method of graph …...

In this paper, we revisit the problem of language identification with the focus on proper discrimination between closely related languages. Strong similarities between certain languages make it very hard to …...

Distributed representations of words have proven extremely useful in numerous natural lan- guage processing tasks. Their appeal is that they can help alleviate data sparsity problems common to supervised learning. …...

AbstractDocument zone identification aims to automatically classify sequences of text-spans (e.g. sentences) within a document into predefined zone categories. Current approaches to document zone identification mostly rely on supervised machine …...

Most sentiment analysis approaches rely on machine-learning techniques, using a bag-of-words (BoW) document represen- tation as their basis. In this paper, we examine whether a more fine-grained rep- resentation of …...

Cross-lingual adaptation aims to learn a pre- diction model in a label-scarce target lan- guage by exploiting labeled data from a label- rich source language. An effective cross- lingual adaptation …...

Often the bottleneck in document classifica- tion is finding good representations that zoom in on the most important aspects of the doc- uments. Most research uses n-gram repre- sentations, but …...

AbstractData annotation in modern practice often involves multiple, imperfect human annotators. Multiple annotations can be used to infer estimates of the ground-truth labels and to estimate individual annotator error characteristics …...

High dimension of bag-of-words vectors poses a serious challenge from sparse data, overfitting, irrelevant features to document classification. Filter feature selection is one of effective methods for dimensionality reduction by …...

This paper addresses the question of how doc- ument classifiers can exploit implicit infor- mation about document similarity to improve document classifier accuracy. We infer doc- ument similarity using simple …...

In regard to document classification, semi- supervised learning using the Naive Bayes method and EM algorithm was a great suc- cess, and we refer to this method as NBEM in …...

AbstractProbabilistic topic models are widely used to discover latent topics in document collections, while latent feature vector representations of words have been used to obtain high performance in many NLP …...

We demonstrate the advantage of special- izing semantic word embeddings for either similarity or relatedness. We compare two variants of retrofitting and a joint-learning approach, and find that all three …...

We propose a hierarchical attention network for document classification. Our model has two distinctive characteristics: (i) it has a hier- archical structure that mirrors the hierarchical structure of documents; (ii) …...

AbstractMost neural network models for document classification on social media focus on text information to the neglect of other information on these platforms. In this paper, we classify post stance …...

AbstractThis paper proposes a matching technique for learning causal associations between word features and class labels in document classification. The goal is to identify more meaningful and generalizable features than …...

AbstractRecurrent Neural Networks are showing much promise in many sub-areas of natural language processing, ranging from document classification to machine translation to automatic question answering. Despite their promise, many recurrent …...

This paper introduces “Czech Text Document Corpus v 2.0”, a collection of text documents for automatic document classification in Czech language. It is composed of the text documents provided by …...

AbstractK-fold cross validation (CV) is a popular method for estimating the true performance of machine learning models, allowing model selection and parameter tuning. However, the very process of CV requires …...

AbstractIn this paper we describe our participation to the Hyperpartisan News Detection shared task at SemEval 2019. Motivated by the late arrival of Doris Martin, we test a previously developed …...

AbstractWe consider a document classification problem where document labels are absent but only relevant keywords of a target class and unlabeled documents are given. Although heuristic methods based on pseudo-labeling …...

AbstractUnsupervised document representation learning is an important task providing pre-trained features for NLP applications. Unlike most previous work which learn the embedding based on self-prediction of the surface of text, …...

AbstractThis paper describes our submission to the SemEval 2019 Hyperpartisan News Detection task. Our system aims for a linguistics-based document classification from a minimal set of interpretable features, while maintaining …...

AbstractWhile high quality gold standard annotated corpora are crucial for most tasks in natural language processing, many annotated corpora published in recent years, created by annotators or tools, contains noisy …...

AbstractWhile much work on deep latent variable models of text uses continuous latent variables, discrete latent variables are interesting because they are more interpretable and typically more space efficient. We …...

AbstractExisting methods for different document classification tasks in the context of social networks typically only capture the semantics of texts, while ignoring the users who exchange the text and the …...

AbstractIn this work we present the experiments which lead to the creation of our BERT and ELECTRA based German language models, GBERT and GELECTRA. By varying the input training data, …...

AbstractIn this paper, we propose a novel method for learning cross-lingual word embeddings, that incorporates sub-word information during training, and is able to learn high-quality embeddings from modest amounts of …...

AbstractRepresentation learning is a critical ingredient for natural language processing systems. Recent Transformer language models like BERT learn powerful textual representations, but these models are targeted towards token- and sentence-level …...

AbstractThis paper presents six document classification models using the latest transformer encoders and a high-performing ensemble model for a task of offensive language identification in social media. For the individual …...

AbstractThe mix-up method (Zhang et al., 2017), one of the methods for data augmentation, is known to be easy to implement and highly effective. Although the mix-up method is intended …...

AbstractSemi-supervised learning through deep generative models and multi-lingual pretraining techniques have orchestrated tremendous success across different areas of NLP. Nonetheless, their development has happened in isolation, while the combination of …...

AbstractRecently, domain shift, which affects accuracy due to differences in data between source and target domains, has become a serious issue when using machine learning methods to solve natural language …...

AbstractText classification is a significant branch of natural language processing, and has many applications including document classification and sentiment analysis. Unsurprisingly, those who do text classification are concerned with the …...

AbstractThe input vocabulary and the representations learned are crucial to the performance of neural NLP models. Using the full vocabulary results in less explainable and more memory intensive models, with …...

AbstractPretraining-based neural network models have demonstrated state-of-the-art (SOTA) performances on natural language processing (NLP) tasks. The most frequently used sentence representation for neural-based NLP methods is a sequence of subwords …...

AbstractThe great majority of languages in the world are considered under-resourced for successful application of deep learning methods. In this work, we propose a meta-learning approach to document classification in …...

AbstractOrganisations disclose their privacy practices by posting privacy policies on their websites. Even though internet users often care about their digital privacy, they usually do not read privacy policies, since …...

AbstractClassification problems having thousands or more classes naturally occur in NLP, for example language models or document classification. A softmax or one-vs-all classifier naturally handles many classes, but it is …...

AbstractTopic models have been widely used to learn text representations and gain insight into document corpora. To perform topic discovery, most existing neural models either take document bag-of-words (BoW) or …...

AbstractIn document classification for, e.g., legal and biomedical text, we often deal with hundreds of classes, including very infrequent ones, as well as temporal concept drift caused by the influence …...

AbstractHow to obtain hierarchical representations with an increasing level of abstraction becomes one of the key issues of learning with deep neural networks. A variety of RNN models have recently …...

AbstractWe present FewShotTextGCN, a novel method designed to effectively utilize the properties of word-document graphs for improved learning in low-resource settings. We introduce K-hop Neighbourhood Regularization, a regularizer for heterogeneous …...

AbstractIn the context of an epidemiological study involving multilingual social media, this paper reports on the ability of machine translation systems to preserve content relevant for a document classification task …...