At the heart of NLP lies a fundamental element: the corpus. A corpus, in NLP, is not just a collection of text documents or utterances; it’s at the core of large language models (LLMs) training. Each corpus type serves a unique purpose in terms of training language models that serve different purposes. Whether it’s a collection of written texts, transcriptions of spoken words, or an amalgamation of various media forms, each corpus type holds the key to leveraging different aspects of language to generate value.
In this blog, we’re going to explore the significance of these different corpora types in NLP. From the traditional text corpora consisting of written content to the speech corpora, from the linguistically diverse parallel corpora to the structurally intricate treebanks, and the integrative multimodal corpora – each plays a pivotal role in how we teach machines to understand and generate human language.
Text corpora encompass a vast array of written materials including books, scholarly articles, web content, emails, and social media posts. This extensive collection is crucial in providing varied and comprehensive linguistic data. Here are some example use cases of text corpora:
The following examples are some commonly used text corpus:
Speech corpora are collections of audio recordings of spoken language, which may also include their transcriptions. They offer a rich resource for understanding various accents, dialects, and nuances of spoken language. Here are a few example use cases of speech corpora:
Parallel corpora contain texts in multiple languages, meticulously aligned at the sentence or document level for cross-lingual comparisons. Here are a few example use cases of parallel corpora:
Treebanks are annotated databases where the syntactic parse trees of sentences are meticulously detailed, elucidating the complex grammatical structures of language. Here are a few example use cases for treebanks:
An example of a text corpus based on Treebanks is Penn Treebank. Contains tagged, parsed, and raw Wall Street Journal data.
Multimodal corpora integrate text with other forms of data, such as images, videos, or audio, creating a rich, multi-layered dataset. Here are a few example use cases of multimodal corpora:
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