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This last observation is less surprising when we consider that text and record structures are the primary domains for the two subfields of computer science that focus on data management, namely text retrieval and databases.
A notable feature of linguistic data management is that usually brings both data types together, and that it can draw on results and techniques from both fields.
Despite its complexity, the TIMIT corpus only contains two fundamental data types, namely lexicons and texts.
TIMIT was developed by a consortium including Texas Instruments and MIT, from which it derives its name.
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.
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.
We can also construct special tabulations (known as paradigms) to illustrate contrasts and systematic variation, as shown in 1.3 for three verbs. At the most abstract level, a text is a representation of a real or fictional speech event, and the time-course of that event carries over into the text itself.
A text could be a small unit, such as a word or sentence, or a complete narrative or dialogue.
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.
Moreover, even at a given level there may be different labeling schemes or even disagreement amongst annotators, such that we want to represent multiple versions.Tags: Adult Dating, affair dating, sex dating