@inproceedings{kazeminejad-etal-2021-automatic,
title = "Automatic Entity State Annotation using the {V}erb{N}et Semantic Parser",
author = "Kazeminejad, Ghazaleh and
Palmer, Martha and
Li, Tao and
Srikumar, Vivek",
editor = "Bonial, Claire and
Xue, Nianwen",
booktitle = "Proceedings of the Joint 15th Linguistic Annotation Workshop (LAW) and 3rd Designing Meaning Representations (DMR) Workshop",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.law-1.13",
doi = "10.18653/v1/2021.law-1.13",
pages = "123--132",
abstract = "Tracking entity states is a natural language processing task assumed to require human annotation. In order to reduce the time and expenses associated with annotation, we introduce a new method to automatically extract entity states, including location and existence state of entities, following Dalvi et al. (2018) and Tandon et al. (2020). For this purpose, we rely primarily on the semantic representations generated by the state of the art VerbNet parser (Gung, 2020), and extract the entities (event participants) and their states, based on the semantic predicates of the generated VerbNet semantic representation, which is in propositional logic format. For evaluation, we used ProPara (Dalvi et al., 2018), a reading comprehension dataset which is annotated with entity states in each sentence, and tracks those states in paragraphs of natural human-authored procedural texts. Given the presented limitations of the method, the peculiarities of the ProPara dataset annotations, and that our system, Lexis, makes no use of task-specific training data and relies solely on VerbNet, the results are promising, showcasing the value of lexical resources.",
}
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<abstract>Tracking entity states is a natural language processing task assumed to require human annotation. In order to reduce the time and expenses associated with annotation, we introduce a new method to automatically extract entity states, including location and existence state of entities, following Dalvi et al. (2018) and Tandon et al. (2020). For this purpose, we rely primarily on the semantic representations generated by the state of the art VerbNet parser (Gung, 2020), and extract the entities (event participants) and their states, based on the semantic predicates of the generated VerbNet semantic representation, which is in propositional logic format. For evaluation, we used ProPara (Dalvi et al., 2018), a reading comprehension dataset which is annotated with entity states in each sentence, and tracks those states in paragraphs of natural human-authored procedural texts. Given the presented limitations of the method, the peculiarities of the ProPara dataset annotations, and that our system, Lexis, makes no use of task-specific training data and relies solely on VerbNet, the results are promising, showcasing the value of lexical resources.</abstract>
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%0 Conference Proceedings
%T Automatic Entity State Annotation using the VerbNet Semantic Parser
%A Kazeminejad, Ghazaleh
%A Palmer, Martha
%A Li, Tao
%A Srikumar, Vivek
%Y Bonial, Claire
%Y Xue, Nianwen
%S Proceedings of the Joint 15th Linguistic Annotation Workshop (LAW) and 3rd Designing Meaning Representations (DMR) Workshop
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F kazeminejad-etal-2021-automatic
%X Tracking entity states is a natural language processing task assumed to require human annotation. In order to reduce the time and expenses associated with annotation, we introduce a new method to automatically extract entity states, including location and existence state of entities, following Dalvi et al. (2018) and Tandon et al. (2020). For this purpose, we rely primarily on the semantic representations generated by the state of the art VerbNet parser (Gung, 2020), and extract the entities (event participants) and their states, based on the semantic predicates of the generated VerbNet semantic representation, which is in propositional logic format. For evaluation, we used ProPara (Dalvi et al., 2018), a reading comprehension dataset which is annotated with entity states in each sentence, and tracks those states in paragraphs of natural human-authored procedural texts. Given the presented limitations of the method, the peculiarities of the ProPara dataset annotations, and that our system, Lexis, makes no use of task-specific training data and relies solely on VerbNet, the results are promising, showcasing the value of lexical resources.
%R 10.18653/v1/2021.law-1.13
%U https://aclanthology.org/2021.law-1.13
%U https://doi.org/10.18653/v1/2021.law-1.13
%P 123-132
Markdown (Informal)
[Automatic Entity State Annotation using the VerbNet Semantic Parser](https://aclanthology.org/2021.law-1.13) (Kazeminejad et al., LAW 2021)
ACL
- Ghazaleh Kazeminejad, Martha Palmer, Tao Li, and Vivek Srikumar. 2021. Automatic Entity State Annotation using the VerbNet Semantic Parser. In Proceedings of the Joint 15th Linguistic Annotation Workshop (LAW) and 3rd Designing Meaning Representations (DMR) Workshop, pages 123–132, Punta Cana, Dominican Republic. Association for Computational Linguistics.