Corpus-based Identification of Verbs Participating in Verb Alternations Using Classification and Manual Annotation (2020)

Abstract: English verb alternations allow participating verbs to appear in a set of syntactically different constructions whose associated semantic frames are systematically related. We use ENCOW and VerbNet data to train classifiers to predict the instrument subject alternation and the causative-inchoative alternation, relying on count-based and vector-based features as well as perplexity-based language model features, which are intended to reflect each alternation’s felicity by simulating it. Beyond the prediction task, we use the classifier results as a source for a manual annotation step in order to identify new, unseen instances of each alternation. This is possible because existing alternation datasets contain positive, but no negative instances and are not comprehensive. Over several sequences of classification-annotation steps, we iteratively extend our sets of alternating verbs. Our hybrid approach to the identification of new alternating verbs reduces the required annotation effort by only presenting annotators with the highest-scoring candidates from the previous classification. Due to the success of semi-supervised and unsupervised features, our approach can easily be transferred to further alternations.

## introduction verb alternations are a phenomenon at the syntax-semantics interface in which several syntactic patterns are available for certain verbs, assigning different semantic roles to the elements in individual slots of the constructions. english verbs that participate in the instrument subject alternation (isa, @xcite ) can take an optional instrument argument, encoded as a with-pp or using-phrase, which can also be realized in subject position (see ( 1 )). replacing with with using is also a standard test for instrumenthood, i.e., a means to exclude other cases of with-pps. not all verbs with an (optional) instrument participate in isa (see ( 2 )). this is usually explained by the specific nature of the instrument in the context of the verb @xcite . the english causative-inchoative alternation (cia) involves two main constructions: verbs can appear intransitively, as in (3-a), or transitively, as in (3-b). the syntactically intransitive construction is associated with an inchoative sense, while the transitive construction has a causative sense. verb alternations pose a challenge to tasks like semantic role labeling or frame induction, and knowledge about which verbs participate in which alternations is highly useful for such tasks: certain syntactic patterns need to be interpreted differently, depending on whether or not the verb participates in the alternation. for instance, the syntactic subject in a sentence with the syntactic structure of (3-a) would ## conclusion the contribution of this work is two-fold. first, we present feature-based classifiers for the instrument subject alternation and the causative-inchoative alternation. second, we show that an iterative, hybrid approach to identifying alternations involving a classification step and a manual annotation step is a promising way to find more alternating verbs. our classifiers outperform the baseline for both alternations (by a small margin for cia and a larger margin for isa). our features are good predictors of the alternations, even though our data makes the tasks particularly challenging because both positive and negative instances are observed in all relevant syntactic patterns in our corpora. the best performance on the alternation identification task is achieved with vector-based features for isa, and perplexity-based features for cia. the features are derived from corpora in an unsupervised (vec) or semi-supervised (pplx) manner, which means that our approach can be transferred to similar phenomena. the pplx features in particular seem to be a reliable approximation of the felicity of alternate sentences constructed from original corpus data. our gold data from verbnet only contains positive instances, but no reliable negative instances. our manual annotation step allows us to verify whether hypothesized negative examples are truly negative instances, or instead good candidates for the alternation that should be added to the positive set. the combination of our classification step and annotation step minimizes the necessary annotation effort while leading to a number of new alternation candidates.