Esther Seyffarth


2021

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Implicit representations of event properties within contextual language models: Searching for “causativity neurons”
Esther Seyffarth | Younes Samih | Laura Kallmeyer | Hassan Sajjad
Proceedings of the 14th International Conference on Computational Semantics (IWCS)

This paper addresses the question to which extent neural contextual language models such as BERT implicitly represent complex semantic properties. More concretely, the paper shows that the neuron activations obtained from processing an English sentence provide discriminative features for predicting the (non-)causativity of the event denoted by the verb in a simple linear classifier. A layer-wise analysis reveals that the relevant properties are mostly learned in the higher layers. Moreover, further experiments show that appr. 10% of the neuron activations are enough to already predict causativity with a relatively high accuracy.

2020

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Corpus-based Identification of Verbs Participating in Verb Alternations Using Classification and Manual Annotation
Esther Seyffarth | Laura Kallmeyer
Proceedings of the 28th International Conference on Computational Linguistics

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.

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Proceedings of the 19th International Workshop on Treebanks and Linguistic Theories
Kilian Evang | Laura Kallmeyer | Rafael Ehren | Simon Petitjean | Esther Seyffarth | Djamé Seddah
Proceedings of the 19th International Workshop on Treebanks and Linguistic Theories

2019

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Identifying Participation of Individual Verbs or VerbNet Classes in the Causative Alternation
Esther Seyffarth
Proceedings of the Society for Computation in Linguistics (SCiL) 2019

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Modeling the Induced Action Alternation and the Caused-Motion Construction with Tree Adjoining Grammar (TAG) and Semantic Frames
Esther Seyffarth
Proceedings of the IWCS 2019 Workshop on Computing Semantics with Types, Frames and Related Structures

2018

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AET: Web-based Adjective Exploration Tool for German
Tatiana Bladier | Esther Seyffarth | Oliver Hellwig | Wiebke Petersen
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Verb Alternations and Their Impact on Frame Induction
Esther Seyffarth
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop

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.