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Structured collections of annotated linguistic data are essential in most areas of NLP, however, we still face many obstacles in using them.
The goal of this chapter is to answer the following questions: Along the way, we will study the design of existing corpora, the typical workflow for creating a corpus, and the lifecycle of corpus.
Despite its complexity, the TIMIT corpus only contains two fundamental data types, namely lexicons and texts.
As we saw in 2., most lexical resources can be represented using a record structure, i.e. A lexical resource could be a conventional dictionary or comparative wordlist, as illustrated.
Two sentences, read by all speakers, were designed to bring out dialect variation: The remaining sentences were chosen to be phonetically rich, involving all phones (sounds) and a comprehensive range of diphones (phone bigrams).
Additionally, the design strikes a balance between multiple speakers saying the same sentence in order to permit comparison across speakers, and having a large range of sentences covered by the corpus to get maximal coverage of diphones.
The inclusion of speaker demographics brings in many more independent variables, that may help to account for variation in the data, and which facilitate later uses of the corpus for purposes that were not envisaged when the corpus was created, such as sociolinguistics.
It was designed to provide data for the acquisition of acoustic-phonetic knowledge and to support the development and evaluation of automatic speech recognition systems.Finally, TIMIT includes demographic data about the speakers, permitting fine-grained study of vocal, social, and gender characteristics.TIMIT illustrates several key features of corpus design.It could also be a phrasal lexicon, where the key field is a phrase rather than a single word.A thesaurus also consists of record-structured data, where we look up entries via non-key fields that correspond to topics.