2 research outputs found
JaTeCS an open-source JAva TExt Categorization System
JaTeCS is an open source Java library that supports research on automatic
text categorization and other related problems, such as ordinal regression and
quantification, which are of special interest in opinion mining applications.
It covers all the steps of an experimental activity, from reading the corpus to
the evaluation of the experimental results. As JaTeCS is focused on text as the
main input data, it provides the user with many text-dedicated tools, e.g.:
data readers for many formats, including the most commonly used text corpora
and lexical resources, natural language processing tools, multi-language
support, methods for feature selection and weighting, the implementation of
many machine learning algorithms as well as wrappers for well-known external
software (e.g., SVM_light) which enable their full control from code. JaTeCS
support its expansion by abstracting through interfaces many of the typical
tools and procedures used in text processing tasks. The library also provides a
number of "template" implementations of typical experimental setups (e.g.,
train-test, k-fold validation, grid-search optimization, randomized runs) which
enable fast realization of experiments just by connecting the templates with
data readers, learning algorithms and evaluation measures
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. DCI is a transfer learning
method for cross-domain and cross-lingual text classification for which we had
provided an implementation (here called JaDCI) built on top of JaTeCS, a Java
framework for text classification. PyDCI is a stand-alone version of DCI that
exploits scikit-learn and the SciPy stack. We here report on new experiments
that we have carried out in order to test PyDCI, and in which we use as
baselines new high-performing methods that have appeared after DCI was
originally proposed. These experiments show that, thanks to a few subtle ways
in which we have improved DCI, PyDCI outperforms both JaDCI and the
above-mentioned high-performing methods, and delivers the best known results on
the two popular benchmarks on which we had tested DCI, i.e.,
MultiDomainSentiment (a.k.a. MDS -- for cross-domain adaptation) and
Webis-CLS-10 (for cross-lingual adaptation). PyDCI, together with the code
allowing to replicate our experiments, is available at
https://github.com/AlexMoreo/pydci