AdArte
AdArte uses an approach based on syntactic transformations and machine learning techniques that fits well with a new type of available data sets that are larger but less complex than data sets used in the past. The transformations are not predefined, but calculated from the data sets, and then used as features in a supervised learning classifier. The method has been evaluated using two data sets: the SICK data set and the EXCITEMENT English data set. While both data sets are of a larger order of magnitude than data sets such as RTE-3, they are also of lower levels of complexity, each in its own way. SICK consists of pairs created by applying a predefined set of syntactic and lexical rules to its T and H pairs, which can be accurately captured by our transformations. The EXCITEMENT English data contains short pieces of text that do not require a high degree of text understanding to be annotated. The resulting AdArte system is simple to understand and implement, but also effective when compared with other existing systems.
http://dx.doi.org/10.1017/S1351324916000176
ROBERTO ZANOLI and SILVIA COLOMBO. A transformation-driven approach for recognizing textual entailment. Natural Language Engineering, available on CJO2016. doi:10.1017/S1351324916000176.
@article{NLE:10369538,
author = {ZANOLI,ROBERTO and COLOMBO,SILVIA},
title = {A transformation-driven approach for recognizing textual entailment},
journal = {Natural Language Engineering},
volume = {FirstView},
month = {6},
month = {6},
year = {2016},
issn = {1469-8110},
pages = {1--28},
numpages = {28},
doi = {10.1017/S1351324916000176},
URL = {http://journals.cambridge.org/article_S1351324916000176},
}