Building Tamil Treebanks
Fuente:
arXiv
Guardado en:
| Autor principal: | |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866929509873221632 |
|---|---|
| author | Sarveswaran, Kengatharaiyer |
| author_facet | Sarveswaran, Kengatharaiyer |
| contents | Treebanks are important linguistic resources, which are structured and annotated corpora with rich linguistic annotations. These resources are used in Natural Language Processing (NLP) applications, supporting linguistic analyses, and are essential for training and evaluating various computational models. This paper discusses the creation of Tamil treebanks using three distinct approaches: manual annotation, computational grammars, and machine learning techniques. Manual annotation, though time-consuming and requiring linguistic expertise, ensures high-quality and rich syntactic and semantic information. Computational deep grammars, such as Lexical Functional Grammar (LFG), offer deep linguistic analyses but necessitate significant knowledge of the formalism. Machine learning approaches, utilising off-the-shelf frameworks and tools like Stanza, UDpipe, and UUParser, facilitate the automated annotation of large datasets but depend on the availability of quality annotated data, cross-linguistic training resources, and computational power. The paper discusses the challenges encountered in building Tamil treebanks, including issues with Internet data, the need for comprehensive linguistic analysis, and the difficulty of finding skilled annotators. Despite these challenges, the development of Tamil treebanks is essential for advancing linguistic research and improving NLP tools for Tamil. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_14657 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Building Tamil Treebanks Sarveswaran, Kengatharaiyer Computation and Language Treebanks are important linguistic resources, which are structured and annotated corpora with rich linguistic annotations. These resources are used in Natural Language Processing (NLP) applications, supporting linguistic analyses, and are essential for training and evaluating various computational models. This paper discusses the creation of Tamil treebanks using three distinct approaches: manual annotation, computational grammars, and machine learning techniques. Manual annotation, though time-consuming and requiring linguistic expertise, ensures high-quality and rich syntactic and semantic information. Computational deep grammars, such as Lexical Functional Grammar (LFG), offer deep linguistic analyses but necessitate significant knowledge of the formalism. Machine learning approaches, utilising off-the-shelf frameworks and tools like Stanza, UDpipe, and UUParser, facilitate the automated annotation of large datasets but depend on the availability of quality annotated data, cross-linguistic training resources, and computational power. The paper discusses the challenges encountered in building Tamil treebanks, including issues with Internet data, the need for comprehensive linguistic analysis, and the difficulty of finding skilled annotators. Despite these challenges, the development of Tamil treebanks is essential for advancing linguistic research and improving NLP tools for Tamil. |
| title | Building Tamil Treebanks |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2409.14657 |