Corpus and annotation

• Corpus linguistics is the study of language as expressed in samples (corpora) of "real world".
• Corpus annotation is a path to greater linguistic understanding.

Corpus annotation “can be defined as the practice of adding interpretative, linguistic information to an electronic corpus of spoken and/or written language data. 'Annotation' can also refer to the end-product of this process” (Leech, 1997).

Annotations

• Annotation is not an end in itself - it is a basis for further analyses.
• Annotations must be time-synchronized:
  • annotations need to be time-aligned in order to be useful for purposes such as qualitative or quantitative analyses.
• Temporal information makes it possible to describe simultaneous behaviours:
  • of different levels in an utterance (e.g. prosody and locution);
  • of different modalities (e.g. speech and gesture);
  • of different speakers or extralinguistic events.

Time-synchronized annotations

Annotation software

• Manual annotation
  • Audacity;
  • Praat;
  • Elan;
  • Anvil;
  • Winpitch;
  • ...
• Automatic annotation:
  • The current state-of-the-art in Computational Linguistics allows many annotation tasks to be semi- or fully- automated.

Automatic annotation

• Each annotation that can be done automatically must be done automatically!

• Why? Because revising is faster and easier than annotating... if the automatic system is "good enough".

Before using any automatic annotation tool/software, it is important to consider its error rate (where applicable) and to estimate how those errors will affect the purpose for the annotated corpora.

My research

• I'm a computer scientist
• My focus: automatic annotations for Linguistics (phonetic, prosody, discourse)
• My main theoretical approach: methods as language-independent as possible:
  • language independent algorithms;
  • language-dependent resources;
  • easy and fast to add a new language;
  • easy and fast to modify existing resources;
• My second theoretical approach: the possibility to adapt technologies to low-resourced languages.

Summary

• Phonemes and words segmentation
• Syllables segmentation
• Repetitions detection
• Conclusion