TY - GEN
T1 - Entailment graphs for text analytics in the excitement project
AU - Magnini, Bernardo
AU - Dagan, Ido
AU - Neumann, Günter
AU - Pado, Sebastian
PY - 2014
Y1 - 2014
N2 - In the last years, a relevant research line in Natural Language Processing has focused on detecting semantic relations among portions of text, including entailment, similarity, temporal relations, and, with a less degree, causality. The attention on such semantic relations has raised the demand to move towards more informative meaning representations, which express properties of concepts and relations among them. This demand triggered research on "statement entailment graphs", where nodes are natural language statements (propositions), comprising of predicates with their arguments and modifiers, while edges represent entailment relations between nodes. We report initial research that defines the properties of entailment graphs and their potential applications. Particularly, we show how entailment graphs are profitably used in the context of the European project EXCITEMENT, where they are applied for the analysis of customer interactions across multiple channels, including speech, email, chat and social media, and multiple languages (English, German, Italian).
AB - In the last years, a relevant research line in Natural Language Processing has focused on detecting semantic relations among portions of text, including entailment, similarity, temporal relations, and, with a less degree, causality. The attention on such semantic relations has raised the demand to move towards more informative meaning representations, which express properties of concepts and relations among them. This demand triggered research on "statement entailment graphs", where nodes are natural language statements (propositions), comprising of predicates with their arguments and modifiers, while edges represent entailment relations between nodes. We report initial research that defines the properties of entailment graphs and their potential applications. Particularly, we show how entailment graphs are profitably used in the context of the European project EXCITEMENT, where they are applied for the analysis of customer interactions across multiple channels, including speech, email, chat and social media, and multiple languages (English, German, Italian).
KW - Semantic inferences
KW - text analytics
KW - textual entailment
UR - http://www.scopus.com/inward/record.url?scp=84906971769&partnerID=8YFLogxK
U2 - https://doi.org/10.1007/978-3-319-10816-2_2
DO - https://doi.org/10.1007/978-3-319-10816-2_2
M3 - منشور من مؤتمر
SN - 9783319108155
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 11
EP - 18
BT - Text, Speech, and Dialogue - 17th International Conference, TSD 2014, Proceedings
PB - Springer Verlag
T2 - 17th International Conference on Text, Speech, and Dialogue, TSD 2014
Y2 - 8 September 2014 through 12 September 2014
ER -