The induced action alternation and the caused-motion construction are two phenomena that allow English verbs to be interpreted as motion-causing events. This is possible when a verb is used with a direct object and a directional phrase, even when the verb does not lexically signify causativity or motion, as in "Sylvia laughed Mary off the stage". While participation in the induced action alternation is a lexical property of certain verbs, the caused-motion construction is not anchored in the lexicon. We model both phenomena with XMG-2 and use the TuLiPA parser to create compositional semantic frames for example sentences. We show how such frames represent the key differences between these two phenomena at the syntax-semantics interface, and how TAG can be used to derive distinct analyses for them.

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Frame induction is the automatic creation of frame-semantic resources similar to FrameNet or PropBank, which map lexical units of a language to frame representations of each lexical unit's semantics. For verbs, these representations usually include a specification of their argument slots and of the selectional restrictions that apply to each slot. Verbs that participate in diathesis alternations have different syntactic realizations whose semantics are closely related, but not identical. We discuss the influence that such alternations have on frame induction, compare several possible frame structures for verbs in the causative alternation, and propose a systematic analysis of alternating verbs that encodes their similarities as well as their differences.

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 causativeinchoative alternation, relying on count-based and vector-based features as well as perplexitybased 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.

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