Ai Machine Learning

Download Collaborative Annotation for Reliable Natural Language by Kar?n Fort PDF

By Kar?n Fort

This booklet offers a distinct chance for developing a constant snapshot of collaborative handbook annotation for typical Language Processing (NLP).  NLP has witnessed significant evolutions some time past 25 years: to begin with, the intense luck of desktop studying, that's now, for larger or for worse, overwhelmingly dominant within the box, and secondly, the multiplication of overview campaigns or shared initiatives. either contain manually annotated corpora, for the educational and overview of the systems.

These corpora have gradually develop into the hidden pillars of our area, delivering meals for our hungry computing device studying algorithms and reference for overview. Annotation is now where the place linguistics hides in NLP. although, guide annotation has principally been missed for it slow, and it has taken your time even for annotation guidance to be famous as essential.

Although a few efforts were made in recent years to handle a number of the matters awarded by means of guide annotation, there has nonetheless been little learn performed at the topic. This e-book goals to supply a few important insights into the subject.

Manual corpus annotation is now on the middle of NLP, and continues to be mostly unexplored. there's a want for guide annotation engineering (in the feel of a accurately formalized process), and this ebook goals to supply a primary step in the direction of a holistic method, with an international view on annotation.

Show description

Read Online or Download Collaborative Annotation for Reliable Natural Language Processing: Technical and Sociological Aspects PDF

Similar ai & machine learning books

How did we find out about the beginning of life?

Describes scientists' makes an attempt to determine how existence started, together with such themes as spontaneous iteration and evolution.

Statistical Machine Translation

This introductory textual content to statistical computing device translation (SMT) offers the entire theories and strategies had to construct a statistical computing device translator, akin to Google Language instruments and Babelfish. as a rule, statistical concepts let computerized translation platforms to be equipped quick for any language-pair utilizing in simple terms translated texts and frequent software program.

Biomedical Natural Language Processing

Biomedical average Language Processing is a entire journey during the vintage and present paintings within the box. It discusses all matters from either a rule-based and a computing device studying technique, and likewise describes each one topic from the point of view of either organic technological know-how and scientific medication. The meant viewers is readers who have already got a history in ordinary language processing, yet a transparent creation makes it obtainable to readers from the fields of bioinformatics and computational biology, in addition.

Extra info for Collaborative Annotation for Reliable Natural Language Processing: Technical and Sociological Aspects

Sample text

During the annotation phase, a regular evaluation of the conformity of the annotation with regards to the mini-reference should be done, associated with regular intraand inter-annotator agreement measurements. 3. Updating Even if it has been decided to stabilize the guidelines at the end of the break-in phase, updates are inevitable during the pre-campaign and the break-in phase. These updates have to be passed on to the annotated corpus, in order for it to remain consistent with the guidelines.

02). The context is maximum (1), as the annotators had to read the whole text to be able to identify renaming relations and they at least had to consult identified external sources. 15). 13). 15. Synthesis of the complexity of the gene names renaming campaign (new scale x2) Annotating Collaboratively 43 Note that the decomposition into EATs does not imply a simplification of the original task, as is often the case for Human Intelligence Tasks (HITs) performed by Turkers (workers) on Amazon Mechanical Turk (see, for example, [COO 10a]).

Each annotator in the campaign is assigned some files to annotate. They are provided with the up-to-date annotation guide, which is coherent with the used data model, and an appropriate annotation tool. They have been trained for the task and they have assimilated the principles explained in the guide. The break-in period should have helped them in refining their understanding of the task. Ideally, the guidelines should be directly integrated in the annotation tool and the tool should be able to check the conformity of the annotation with regard to the guidelines, but if this was possible, human annotators would no longer be needed.

Download PDF sample

Rated 4.46 of 5 – based on 19 votes