Using Distributional Similarity to Identify Individual Verb Choice
نویسنده
چکیده
Human text is characterised by the individual lexical choices of a specific author. Significant variations exist between authors. In contrast, natural language generation systems normally produce uniform texts. In this paper we apply distributional similarity measures to help verb choice in a natural language generation system which tries to generate text similar to individual author. By using a distributional similarity (DS) measure on corpora collected from a recipe domain, we get the most likely verbs for individual authors. The accuracy of matching verb pairs produced by distributional similarity is higher than using the synonym outputs of verbs from WordNet. Furthermore, the combination of the two methods provides the best accuracy.
منابع مشابه
Using Distributional Similarity of Multi-way Translations to Predict Multiword Expression Compositionality
We predict the compositionality of multiword expressions using distributional similarity between each component word and the overall expression, based on translations into multiple languages. We evaluate the method over English noun compounds, English verb particle constructions and German noun compounds. We show that the estimation of compositionality is improved when using translations into m...
متن کاملA Factorized Model for Transitive Verbs in Compositional Distributional Semantics
We present a factorized compositional distributional semantics model for the representation of transitive verb constructions. Our model first produces (subject, verb) and (verb, object) vector representations based on the similarity of the nouns in the construction to each of the nouns in the vocabulary and the tendency of these nouns to take the subject and object roles of the verb. These vect...
متن کاملLow-Rank Tensors for Verbs in Compositional Distributional Semantics
Several compositional distributional semantic methods use tensors to model multi-way interactions between vectors. Unfortunately, the size of the tensors can make their use impractical in large-scale implementations. In this paper, we investigate whether we can match the performance of full tensors with low-rank approximations that use a fraction of the original number of parameters. We investi...
متن کاملOptimizing a Distributional Semantic Model for the Prediction of German Particle Verb Compositionality
In the work presented here we assess the degree of compositionality of German Particle Verbs with a Distributional Semantics Model which only relies on word window information and has no access to syntactic information as such. Our method only takes the lexical distributional distance between the Particle Verb to its Base Verb as a predictor for compositionality. We show that the ranking of dis...
متن کاملFrom distributional to semantic similarity
Lexical-semantic resources, including thesauri and WORDNET, have been successfully incorporated into a wide range of applications in Natural Language Processing. However they are very difficult and expensive to create and maintain, and their usefulness has been severely hampered by their limited coverage, bias and inconsistency. Automated and semi-automated methods for developing such resources...
متن کامل