This repository contains the code that supports the paper Analogies Between Sentences: Theoretical Aspects - Preliminary Experiments.
To get all the files and the correct folder structure, lauch the dependencies.sh
script.
- Go into the
generated_sentences/
directory$ cd generated_sentences/
- Choose the embedding size at
line 146
inmain_sentence_classification.py
- Choose the number of
epochs
,folds
andbatch_size
in the train function atline 150
- Start the main_sentence_classification.py script with the following command line:
$ python3 main_sentence_classification.py
- Go into the
pdtb_sentence/
directory$ cd pdtb_sentence/
- Please read and meet all the requirements layed out in the
pdtb_sentence/README.md
file - Choose the embedding size at
line 160
inmain_pdtb_sentence_classification.py
- Choose the number of
epochs
,folds
andbatch_size
in the train function atline 164
- Start the main_pdtb_sentence_classification.py script with the following command line:
$ python3 main_pdtb_sentence_classification.py
- Check the
sentence_forest/README_sentence_forest
file.
@InProceedings{10.1007/978-3-030-86772-0_1,
author="Afantenos, Stergos
and Kunze, Tarek
and Lim, Suryani
and Prade, Henri
and Richard, Gilles",
editor="Vejnarov{\'a}, Ji{\v{r}}ina
and Wilson, Nic",
title="Analogies Between Sentences: Theoretical Aspects - Preliminary Experiments",
booktitle="Symbolic and Quantitative Approaches to Reasoning with Uncertainty",
year="2021",
publisher="Springer International Publishing",
address="Cham",
pages="3--18",
abstract="Analogical proportions hold between 4 items a, b, c, d insofar as we can consider that ``a is to b as c is to d''. Such proportions are supposed to obey postulates, from which one can derive Boolean or numerical models that relate vector-based representations of items making a proportion. One basic postulate is the preservation of the proportion by permuting the central elements b and c. However this postulate becomes debatable in many cases when items are words or sentences. This paper proposes a weaker set of postulates based on internal reversal, from which new Boolean and numerical models are derived. The new system of postulates is used to extend a finite set of examples in a machine learning perspective. By embedding a whole sentence into a real-valued vector space, we tested the potential of these weaker postulates for classifying analogical sentences into valid and non-valid proportions. It is advocated that identifying analogical proportions between sentences may be of interest especially for checking discourse coherence, question-answering, argumentation and computational creativity. The proposed theoretical setting backed with promising preliminary experimental results also suggests the possibility of crossing a real-valued embedding with an ontology-based representation of words. This hybrid approach might provide some insights to automatically extract analogical proportions in natural language corpora.",
isbn="978-3-030-86772-0"
}