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        <title>A Simple Model for Improving the Performance of the Stanford Parser for Action Detection in Textual Instructions</title>
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    <name type="personal">
        <namePart type="given">Kristina</namePart>
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    <abstract>Different approaches for behaviour understanding rely on textual instructions to generate models of human behaviour. These approaches usually use state of the art parsers to obtain the part of speech (POS) meaning and dependencies of the words in the instructions. For them it is essential that the parser is able to correctly annotate the instructions and especially the verbs as they describe the actions of the person. State of the art parsers usually make errors when annotating textual instructions, as they have short sentence structure often in imperative form. The inability of the parser to identify the verbs results in the inability of behaviour understanding systems to identify the relevant actions. To address this problem, we propose a simple rule-based model that attempts to correct any incorrectly annotated verbs. We argue that the model is able to significantly improve the parser’s performance without the need of additional training data. We evaluate our approach by extracting the actions from 61 textual instructions annotated only with the Stanford parser and once again after applying our model. The results show a significant improvement in the recognition rate when applying the rules (75% accuracy compared to 68% without the rules, p-value \textless 0.001).</abstract>
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%0 Conference Proceedings
%T A Simple Model for Improving the Performance of the Stanford Parser for Action Detection in Textual Instructions
%A Yordanova, Kristina
%S Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
%D 2017
%8 sep
%I INCOMA Ltd.
%C Varna, Bulgaria
%F yordanova-2017-simple
%X Different approaches for behaviour understanding rely on textual instructions to generate models of human behaviour. These approaches usually use state of the art parsers to obtain the part of speech (POS) meaning and dependencies of the words in the instructions. For them it is essential that the parser is able to correctly annotate the instructions and especially the verbs as they describe the actions of the person. State of the art parsers usually make errors when annotating textual instructions, as they have short sentence structure often in imperative form. The inability of the parser to identify the verbs results in the inability of behaviour understanding systems to identify the relevant actions. To address this problem, we propose a simple rule-based model that attempts to correct any incorrectly annotated verbs. We argue that the model is able to significantly improve the parser’s performance without the need of additional training data. We evaluate our approach by extracting the actions from 61 textual instructions annotated only with the Stanford parser and once again after applying our model. The results show a significant improvement in the recognition rate when applying the rules (75% accuracy compared to 68% without the rules, p-value \textless 0.001).
%R 10.26615/978-954-452-049-6_106
%U https://doi.org/10.26615/978-954-452-049-6_106
%P 831-838

Markdown (Informal)

[A Simple Model for Improving the Performance of the Stanford Parser for Action Detection in Textual Instructions](https://doi.org/10.26615/978-954-452-049-6_106) (Yordanova, RANLP 2017)

• A Simple Model for Improving the Performance of the Stanford Parser for Action Detection in Textual Instructions (Yordanova, RANLP 2017)

ACL

• Kristina Yordanova. 2017. A Simple Model for Improving the Performance of the Stanford Parser for Action Detection in Textual Instructions. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pages 831–838, Varna, Bulgaria. INCOMA Ltd..