Named Entity Recognition for social media data is challenging because of its inherent
noisiness. In addition to improper grammatical structures, it contains spelling inconsistencies …

Large scale data and predictive analytics are the most challenging tasks in the field of
academic data mining. Academic libraries are a great source of information and knowledge …

In the few works that have used NLP to study literary quality, sentiment and emotion analysis
have often been considered valuable sources of information. At the same time, the idea that …

While many Machine Learning methods were developed or transposed on Riemannian
manifolds to tackle data with known non Euclidean geometry, Optimal Transport (OT) …

Computational textual aesthetics aims at studying observable differences between aesthetic
categories of text. We use Approximate Entropy to measure the (un) predictability in two …

We introduce EmotionArcs, a dataset comprising emotional arcs from over 9,000 English
novels, assembled to understand the dynamics of emotions represented in text and how …

Books have the power to make us feel happiness, sadness, pain, surprise, or sorrow. An
author's dexterity in the use of these emotions captivates readers and makes it difficult for …

Literary novels are said to distinguish themselves from other novels through conventions
associated with literariness. We investigate the task of predicting the literariness of novels as …

Optimal transport (OT) has emerged as a powerful framework to compare probability
measures, a fundamental task in many statistical and machine learning problems …

Computational studies of literature use proxies like sales numbers, human judgments, or
canonicity to estimate literary quality. However, many quantitative use one such measure as …