TY - GEN
T1 - Measuring non-compositionality of verb-noun collocations using lexical functions and wordnet hypernyms
AU - Kolesnikova, Olga
AU - Gelbukh, Alexander
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - In such verb-noun combinations as draw a conclusion, lend support, take a step, the verb acquires a meaning different from its typical meaning usually represented by the first sense in WordNet thus making a correct compositional analysis hard or even impossible. Such non-compositional word combinations are called collocations. The semantics and syntactical properties of collocations can be formalized using lexical functions, a concept of the Meaning-Text Theory. In this paper we realized two series of experiments, both with supervised learning methods on automatic detection of lexical functions in verb-noun collocations using WordNet hypernyms. In the first experimental series, we used hypernyms which correspond to the manually annotated WordNet senses of verbs and nouns in the dataset. In the second series, we used hypernyms corresponding to the typical (first) sense of the verbs. Comparing the results of both experiments we found that the performance of supervised learning on some lexical functions was better in the second case in spite of the fact that the first sense was not the sense of the verbs they have in collocations. This shows that for such lexical functions, the semantics of the verbs is closer to their typical senses and thus noncompositionality of such collocations is weaker. We propose to use the difference in lexical function detection based on the actual sense and the first sense as a simple measure of non-compositionality of verb-noun collocations.
AB - In such verb-noun combinations as draw a conclusion, lend support, take a step, the verb acquires a meaning different from its typical meaning usually represented by the first sense in WordNet thus making a correct compositional analysis hard or even impossible. Such non-compositional word combinations are called collocations. The semantics and syntactical properties of collocations can be formalized using lexical functions, a concept of the Meaning-Text Theory. In this paper we realized two series of experiments, both with supervised learning methods on automatic detection of lexical functions in verb-noun collocations using WordNet hypernyms. In the first experimental series, we used hypernyms which correspond to the manually annotated WordNet senses of verbs and nouns in the dataset. In the second series, we used hypernyms corresponding to the typical (first) sense of the verbs. Comparing the results of both experiments we found that the performance of supervised learning on some lexical functions was better in the second case in spite of the fact that the first sense was not the sense of the verbs they have in collocations. This shows that for such lexical functions, the semantics of the verbs is closer to their typical senses and thus noncompositionality of such collocations is weaker. We propose to use the difference in lexical function detection based on the actual sense and the first sense as a simple measure of non-compositionality of verb-noun collocations.
KW - Lexical functions
KW - Non-compositionality of collocations
KW - Supervised learning
KW - Verb-noun collocations
KW - Wordnet hypernyms
UR - http://www.scopus.com/inward/record.url?scp=84952656126&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-27101-9_1
DO - 10.1007/978-3-319-27101-9_1
M3 - Contribución a la conferencia
SN - 9783319271002
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 3
EP - 25
BT - Advances in Artificial Intelligence and Its Applications - 14th Mexican International Conference on Artificial Intelligence, MICAI 2015, Proceedings
A2 - Alcántara, Oscar Herrera
A2 - Lagunas, Obdulia Pichardo
A2 - Figueroa, Gustavo Arroyo
PB - Springer Verlag
T2 - 14th Mexican International Conference on Artificial Intelligence, MICAI 2015
Y2 - 25 October 2015 through 31 October 2015
ER -