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      Extracting semantic relations using syntax : An R package for querying and reshaping dependency trees.

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          Abstract

          Most common methods for automatic text analysis in communication science ignore syntactic information, focusing on the occurrence and co-occurrence of individual words, and sometimes n-grams. This is remarkably effective for some purposes, but poses a limitation for fine-grained analyses into semantic relations such as who does what to whom and according to what source. One tested, effective method for moving beyond this bag-of-words assumption is to use a rule-based approach for labeling and extracting syntactic patterns in dependency trees. Although this method can be used for a variety of purposes, its application is hindered by the lack of dedicated and accessible tools. In this paper we introduce the rsyntax R package, which is designed to make working with dependency trees easier and more intuitive for R users, and provides a framework for combining multiple rules for reliably extracting useful semantic relations.

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          The Stanford CoreNLP Natural Language Processing Toolkit

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            Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts

            Politics and political conflict often occur in the written and spoken word. Scholars have long recognized this, but the massive costs of analyzing even moderately sized collections of texts have hindered their use in political science research. Here lies the promise of automated text analysis: it substantially reduces the costs of analyzing large collections of text. We provide a guide to this exciting new area of research and show how, in many instances, the methods have already obtained part of their promise. But there are pitfalls to using automated methods—they are no substitute for careful thought and close reading and require extensive and problem-specific validation. We survey a wide range of new methods, provide guidance on how to validate the output of the models, and clarify misconceptions and errors in the literature. To conclude, we argue that for automated text methods to become a standard tool for political scientists, methodologists must contribute new methods and new methods of validation.
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              Structural Topic Models for Open-Ended Survey Responses

                Author and article information

                Contributors
                Journal
                CCR
                Computational Communication Research
                Amsterdam University Press (Amsterdam )
                2665-9085
                2665-9085
                October 2021
                : 3
                : 2
                : 1-16
                Affiliations
                VU University Amsterdam
                VU University Amsterdam
                VU University Amsterdam
                Article
                CCR2021.2.003.WELB
                10.5117/CCR2021.2.003.WELB
                74fee655-8377-4845-bc78-6770bbf3ee89
                © This is an open access article distributed under the terms of the CC BY 4.0 license
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