%0 Book Section %1 dash_syntactic_2021 %A Dash, Niladri Sekhar %B Language Corpora Annotation and Processing %C Singapore %D 2021 %E Dash, Niladri Sekhar %I Springer %K texttechnologie %P 221--249 %R 10.1007/978-981-16-2960-0_10 %T Syntactic Annotation %U https://doi.org/10.1007/978-981-16-2960-0_10 %X We discuss in this chapter some of the basic challenges that are involved in analyzing sentences and designing a scheme for syntactic annotation. Here we define the basic concept of syntactic annotation with comments on its nature, method, and function in a language. Next, we focus on some goals and purposes behind developing a syntactic annotation tool for a language. There are some guidelines and instructions for developing a syntactic annotation tool for some advanced languages. We do not try to address these issues and strategies again in this chapter. Rather, we focus on the theoretical and practical importance of syntactic annotation in the process of extracting syntactic information from a sentence. During syntactic annotation, we supply a sentence of a natural language to a machine as an input and instruct the machine to identify phrases and mark their grammatical-cum-syntactic roles in the sentence. It implies that a machine has to learn how phrases are formed and organized so that it understands how a sentence is to be analyzed and interpreted from the perspective of syntactic function and semantic information of words and phrases. It also needs to learn how syntactic-cum-semantic roles of various syntactic units are functionally controlled based on their lexical associations and morphological functions in retrieving information embedded within a sentence. We address all these issues in this chapter and present some ideas and processes that are normally used in syntactic annotation. In course of formulating the basic ideas, we refer to the rules of context-free grammars and show how the outputs generated from syntactically annotated corpus can be used in a better description of a grammar of a language, teaching grammatical forms of a language with better information and analysis, understanding how human brain applies syntactic rules to form sentences, how syntactic rules can be designed to train a computer, and how applications relating to language can be developed with proper syntactic information of a language. %@ 9789811629600

@incollection{dash_syntactic_2021, abstract = {We discuss in this chapter some of the basic challenges that are involved in analyzing sentences and designing a scheme for syntactic annotation. Here we define the basic concept of syntactic annotation with comments on its nature, method, and function in a language. Next, we focus on some goals and purposes behind developing a syntactic annotation tool for a language. There are some guidelines and instructions for developing a syntactic annotation tool for some advanced languages. We do not try to address these issues and strategies again in this chapter. Rather, we focus on the theoretical and practical importance of syntactic annotation in the process of extracting syntactic information from a sentence. During syntactic annotation, we supply a sentence of a natural language to a machine as an input and instruct the machine to identify phrases and mark their grammatical-cum-syntactic roles in the sentence. It implies that a machine has to learn how phrases are formed and organized so that it understands how a sentence is to be analyzed and interpreted from the perspective of syntactic function and semantic information of words and phrases. It also needs to learn how syntactic-cum-semantic roles of various syntactic units are functionally controlled based on their lexical associations and morphological functions in retrieving information embedded within a sentence. We address all these issues in this chapter and present some ideas and processes that are normally used in syntactic annotation. In course of formulating the basic ideas, we refer to the rules of context-free grammars and show how the outputs generated from syntactically annotated corpus can be used in a better description of a grammar of a language, teaching grammatical forms of a language with better information and analysis, understanding how human brain applies syntactic rules to form sentences, how syntactic rules can be designed to train a computer, and how applications relating to language can be developed with proper syntactic information of a language.}, added-at = {2021-10-02T13:41:53.000+0200}, address = {Singapore}, author = {Dash, Niladri Sekhar}, biburl = {https://www.bibsonomy.org/bibtex/215e6bba3dc5c962563d08a15d165f176/lepsky}, booktitle = {Language {Corpora} {Annotation} and {Processing}}, doi = {10.1007/978-981-16-2960-0_10}, editor = {Dash, Niladri Sekhar}, interhash = {c563626f058b93f6ced53c8c6d10274c}, intrahash = {15e6bba3dc5c962563d08a15d165f176}, isbn = {9789811629600}, keywords = {texttechnologie}, language = {en}, pages = {221--249}, publisher = {Springer}, timestamp = {2021-10-02T13:41:53.000+0200}, title = {Syntactic {Annotation}}, url = {https://doi.org/10.1007/978-981-16-2960-0_10}, urldate = {2021-09-16}, year = 2021 }