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Found 21 papers, 14 papers with code
Quantification using Permutation-Invariant Networks based on Histograms
Quantification, also known as class prevalence estimation, is the supervised learning task in which a model is trained to predict the prevalence of each class in a given bag of examples.
Forging the Forger: An Attempt to Improve Authorship Verification via Data Augmentation
In this paper, we investigate the potential benefits of augmenting the classifier training set with (negative) synthetic examples.
Kernel Density Estimation for Multiclass Quantification
Several disciplines, like the social sciences, epidemiology, sentiment analysis, or market research, are interested in knowing the distribution of the classes in a population rather than the individual labels of the members thereof.
Explainable Authorship Identification in Cultural Heritage Applications: Analysis of a New Perspective
While a substantial amount of work has recently been devoted to enhance the performance of computational Authorship Identification (AId) systems, little to no attention has been paid to endowing AId systems with the ability to explain the reasons behind their predictions.
Regularization-Based Methods for Ordinal Quantification
Quantification, i. e., the task of training predictors of the class prevalence values in sets of unlabeled data items, has received increased attention in recent years.
Binary Quantification and Dataset Shift: An Experimental Investigation
One finding that results from this investigation is that many existing quantification methods that had been found robust to prior probability shift are not necessarily robust to other types of dataset shift.
Same or Different? Diff-Vectors for Authorship Analysis
We investigate the effects on authorship identification tasks of a fundamental shift in how to conceive the vectorial representations of documents that are given as input to a supervised learner.
Multi-Label Quantification
While many quantification methods have been proposed in the past for binary problems and, to a lesser extent, single-label multiclass problems, the multi-label setting (i. e., the scenario in which the classes of interest are not mutually exclusive) remains by and large unexplored.
LeQua@CLEF2022: Learning to Quantify
LeQua 2022 is a new lab for the evaluation of methods for "learning to quantify" in textual datasets, i. e., for training predictors of the relative frequencies of the classes of interest in sets of unlabelled textual documents.
Syllabic Quantity Patterns as Rhythmic Features for Latin Authorship Attribution
It is well known that, within the Latin production of written text, peculiar metric schemes were followed not only in poetic compositions, but also in many prose works.
Measuring Fairness Under Unawareness of Sensitive Attributes: A Quantification-Based Approach
More in detail, we show that fairness under unawareness can be cast as a quantification problem and solved with proven methods from the quantification literature.
Generalized Funnelling: Ensemble Learning and Heterogeneous Document Embeddings for Cross-Lingual Text Classification
In this ensemble method, 1st-tier classifiers, each working on a different and language-dependent feature space, return a vector of calibrated posterior probabilities (with one dimension for each class) for each document, and the final classification decision is taken by a metaclassifier that uses this vector as its input.
QuaPy: A Python-Based Framework for Quantification
prevalence values) of the classes of interest in a sample of unlabelled data.
Re-Assessing the "Classify and Count" Quantification Method
This task originated with the observation that "Classify and Count" (CC), the trivial method of obtaining class prevalence estimates, is often a biased estimator, and thus delivers suboptimal quantification accuracy; following this observation, several methods for learning to quantify have been proposed that have been shown to outperform CC.
Tweet Sentiment Quantification: An Experimental Re-Evaluation
It is well-known that solving quantification by means of ``classify and count'' (i. e., by classifying all unlabelled items by means of a standard classifier and counting the items that have been assigned to a given class) is less than optimal in terms of accuracy, and that more accurate quantification methods exist.
MedLatinEpi and MedLatinLit: Two Datasets for the Computational Authorship Analysis of Medieval Latin Texts
We present and make available MedLatinEpi and MedLatinLit, two datasets of medieval Latin texts to be used in research on computational authorship analysis.
Word-Class Embeddings for Multiclass Text Classification
Pre-trained word embeddings encode general word semantics and lexical regularities of natural language, and have proven useful across many NLP tasks, including word sense disambiguation, machine translation, and sentiment analysis, to name a few.
Cross-Lingual Sentiment Quantification
Cross-lingual sentiment quantification (and cross-lingual \emph{text} quantification in general) has never been discussed before in the literature; we establish baseline results for the binary case by combining state-of-the-art quantification methods with methods capable of generating cross-lingual vectorial representations of the source and target documents involved.
Cross-Lingual Sentiment Classification
General Classification
+2
Building Automated Survey Coders via Interactive Machine Learning
We will show that, for the same amount of training effort, interactive learning delivers much better coding accuracy than standard "non-interactive" learning.
Funnelling: A New Ensemble Method for Heterogeneous Transfer Learning and its Application to Cross-Lingual Text Classification
Funnelling consists of generating a two-tier classification system where all documents, irrespectively of language, are classified by the same (2nd-tier) classifier.
Revisiting Distributional Correspondence Indexing: A Python Reimplementation and New Experiments
This paper introduces PyDCI, a new implementation of Distributional Correspondence Indexing (DCI) written in Python.
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Sentiment Analysis
on Multi-Domain Sentiment Dataset
