TY - GEN
T1 - A statistics-based semantic textual entailment system
AU - Pakray, Partha
AU - Barman, Utsab
AU - Bandyopadhyay, Sivaji
AU - Gelbukh, Alexander
PY - 2011
Y1 - 2011
N2 - We present a Textual Entailment (TE) recognition system that uses semantic features based on the Universal Networking Language (UNL). The proposed TE system compares the UNL relations in both the text and the hypothesis to arrive at the two-way entailment decision. The system has been separately trained on each development corpus released as part of the Recognizing Textual Entailment (RTE) competitions RTE-1, RTE-2, RTE-3 and RTE-5 and tested on the respective RTE test sets.
AB - We present a Textual Entailment (TE) recognition system that uses semantic features based on the Universal Networking Language (UNL). The proposed TE system compares the UNL relations in both the text and the hypothesis to arrive at the two-way entailment decision. The system has been separately trained on each development corpus released as part of the Recognizing Textual Entailment (RTE) competitions RTE-1, RTE-2, RTE-3 and RTE-5 and tested on the respective RTE test sets.
KW - Recognizing Textual Entailment data sets
KW - Universal Networking Language
KW - textual entailment
UR - http://www.scopus.com/inward/record.url?scp=82555191207&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-25324-9_23
DO - 10.1007/978-3-642-25324-9_23
M3 - Contribución a la conferencia
SN - 9783642253232
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 267
EP - 276
BT - Advances in Artificial Intelligence - 10th Mexican International Conference on Artificial Intelligence, MICAI 2011, Proceedings
T2 - 10th Mexican International Conference on Artificial Intelligence, MICAI 2011
Y2 - 26 November 2011 through 4 December 2011
ER -