Overview and motivation

A single text document often has multiple semantic aspects. A single news article related to politics may have aspects related to trade, technology and defense. Therefore, often a document needs to be tagged to multiple labels/categories, instead of a single category. Most of the classification algorithms deal with datasets which have a set of input features and only one output class. However, in reality the problem might be different from a typical binary or multiclass classification, as often a document or an image can be associated with multiple categories rather than a single category.

An introduction of enormous amount of documents belonging to multiple categories in the legal domain, makes it an attractive area for employing automated solutions. In this project we explore a public multi labelled legal text dataset that has been manually annotated over a decade. It contains laws related to the European Union, including treaties, legislation, case-law and legislative proposals, in 24 different languages. This is popularly known as the EUR-Lex dataset containing about twenty five thousand documents, around seven thousand labels and in several European languages. A skewed distribution of multiple labels per document, along with existence of the same data in multiple languages, makes this dataset an interesting proposition. Few publications have used an older version of the dataset which had around four thousand labels. The ones that have used this have reported relatively poor F1 values in the range of 40% (referred to as EUROVOC)[1] (which may be fair, given the high number of labels). There is no publications for the new dataset (having around 7000 labels), which motivates us to explore the problem of multilabel classification on this dataset.

Multilable v/s Multiclass classification
In multi-label classification, each instance in the training set is associated with a set of labels, instead of a single label, and the task is to predict the label-sets of unseen instances, instead of a single label.
There is a difference between multi-class classification and multi-label classification. In multi-class problem the classes or labels are mutually-exclusive, i.e. it makes the assumption that each instance can be assigned to only one label. E.g - an animal can be either a dog or a cat but not both. But in multi-label problem multiple labels may be assigned to an instance. E.g - a movie can belong to a comedy genre as well a detective genre.

## The script installs the necessary packages if not already installed, and then loads them
packages <- c(
  "textstem",
  "stringi",
  "dplyr",
  "data.table",
  "tm",
  "textclean",
  "XML",
  "RWeka",
  "tidyr",
  "tidytext",
  "parallel",
  "mldr",
  "utiml",
  "tidytext",
  "ggplot2",
  "RColorBrewer",
  "udpipe",
  "kknn",
  "xgboost",
  "randomForest",
  "matrixStats",
  "rmarkdown",
  "wordcloud",
  "ggraph",
  "igraph",
  "tidyverse",
  "gridExtra",
  "kableExtra",
  "knitr"
)

verify.packages <- function(pkg) {
  new.pkg <- pkg[!(pkg %in% installed.packages()[, "Package"])]
  if (length(new.pkg))
    install.packages(new.pkg, dependencies = TRUE)
  sapply(pkg, library, character.only = TRUE)
}

verify.packages(packages)