1 / 7

Computational Discourse Analysis in Natural Language Processing

Explore the manual annotation process for computational discourse and dialog analysis. Learn essential steps, coding manual development, and measuring agreement. Enhance your NLP tasks effectively.

fsmith
Télécharger la présentation

Computational Discourse Analysis in Natural Language Processing

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. CS3730/ISP3120Corpus Annotation

  2. Manual Annotation • AKA coding, labeling

  3. From Webber’s Chapter • The aims of computational work in [discourse and dialog]: • Modeling particular phenomena in discourse and dialog in terms of underlying computational processes • Providing useful natural language services, whose success depends in part on handling aspects of discourse and dialog • What computation contributes is a coherent framework for modeling these phenomena in terms of … search through a space of possible candidate interpretations (in language analysis) or candidate realizations (in language generation)

  4. Desiderata • Interesting and rich enough • Not so rich that automation is too far ahead of the current state of the art • Too complex logical structure • Knowledge bottleneck (without viable source) • Too fine-grained or subtle • Annotation instructions (aka coding manual) feasible • Time required for training is reasonable • Annotators can reliably perform the annotations in a reasonable amount of time

  5. Minimal Process for NLP • Develop initial coding manual • At least two people perform sample annotations, and discuss their disagreements and experiences • Revise coding manual • Repeat 2-3 until agreement on training data is sufficient • Independently annotate a fresh test set • Evaluate agreement

  6. Additional Steps • Develop initial coding manual • At least two people perform sample annotations, and discuss their disagreements and experiences. Analysis of patterns of agreement and disagreement using probability models (Wiebe et al. ACL-99; Bruce & Wiebe NLE-99; from work in applied statistics) • Revise coding manual • Repeat 2-3 until agreement on training data is sufficient • Independently annotate a fresh test set • Evaluate agreement • Train more annotators, assess average time for training and annotation • Evaluate other types of reliability (psychology, content analysis, applied statistics literatures)

  7. Measures of Agreement • Percentage Agreement: OK, but not sufficient • If the distribution of classes is highly skewed, then the baseline algorithm of always assigning the most frequent class would have high agreement • Kappa: measures agreement over and above agreement expected by chance • Details available in section 3 of this paper by our group

More Related