Papers Multi-Label Classification

Semantic HMC for Big Data Analysis

Analyzing Big Data can help corporations to im-prove their efficiency. In this work we present a new vision to derive Value from Big Data using a Semantic Hierarchical Multi-label Classification called Semantic HMC based…

Predicting Useful Neighborhoods for Lazy Local Learning

Lazy local learning methods train a classifier on the fly" at test time, using only a subset of the training instances that are most relevant to the novel test example. The goal is to tailor the classifier to the propert…

Errata: Distant Supervision for Relation Extraction with Matrix Completion

The essence of distantly supervised relation extraction is that it is an incomplete multi-label classification problem with sparse and noisy features. To tackle the sparsity and noise challenges, we propose solving the c…

A Mixtures-of-Experts Framework for Multi-Label Classification

We develop a novel probabilistic approach for multi-label classification that is based on the mixtures-of-experts architecture combined with recently introduced conditional tree-structured Bayesian networks. Our approach…

Estimating Maximally Probable Constrained Relations by Mathematical Programming

Estimating a constrained relation is a fundamental problem in machine learning. Special cases are classification (the problem of estimating a map from a set of to-be-classified elements to a set of labels), clustering (t…

SAP-RI: A Constrained and Supervised Approach for Aspect-Based Sentiment Analysis

Notes on hierarchical ensemble methods for DAG-structured taxonomies

Several real problems ranging from text classification to computational biology are characterized by hierarchical multi-label classification tasks. Most of the methods presented in literature focused on tree-structured t…

Machine learning approach for text and document mining

Text Categorization (TC), also known as Text Classification, is the task of automatically classifying a set of text documents into different categories from a predefined set. If a document belongs to exactly one of the c…

Distant Supervision for Relation Extraction with Matrix Completion

Comparing Multi-label Classification with Reinforcement Learning for Summarisation of Time-series Data

kLogNLP: Graph Kernel--based Relational Learning of Natural Language

Grammatical Error Detection and Correction using a Single Maximum Entropy Model

Multi-Target Regression via Random Linear Target Combinations

Multi-target regression is concerned with the simultaneous prediction of multiple continuous target variables based on the same set of input variables. It arises in several interesting industrial and environmental applic…

Random forests with random projections of the output space for high dimensional multi-label classification

We adapt the idea of random projections applied to the output space, so as to enhance tree-based ensemble methods in the context of multi-label classification. We show how learning time complexity can be reduced without …

Fast Recursive Multi-class Classification of Pairs of Text Entities for Biomedical Event Extraction

Ensemble Detection of Single & Multiple Events at Sentence-Level

Event classification at sentence level is an important Information Extraction task with applications in several NLP, IR, and personalization systems. Multi-label binary relevance (BR) are the state-of-art methods. In thi…

Multi-label ensemble based on variable pairwise constraint projection

Multi-label classification has attracted an increasing amount of attention in recent years. To this end, many algorithms have been developed to classify multi-label data in an effective manner. However, they usually do n…

Large-scale Multi-label Text Classification - Revisiting Neural Networks

Neural networks have recently been proposed for multi-label classification because they are able to capture and model label dependencies in the output layer. In this work, we investigate limitations of BP-MLL, a neural n…

Overview of the 2013 ALTA Shared Task

On the Bayes-optimality of F-measure maximizers

The F-measure, which has originally been introduced in information retrieval, is nowadays routinely used as a performance metric for problems such as binary classification, multi-label classification, and structured outp…