By Kar?n Fort
This publication offers a special chance for developing a constant snapshot of collaborative guide annotation for normal Language Processing (NLP). NLP has witnessed significant evolutions long ago 25 years: to start with, the extreme good fortune of computing device studying, that's now, for greater or for worse, overwhelmingly dominant within the box, and secondly, the multiplication of assessment campaigns or shared projects. either contain manually annotated corpora, for the learning and assessment of the systems.
These corpora have steadily develop into the hidden pillars of our area, supplying nutrients for our hungry laptop studying algorithms and reference for assessment. Annotation is now where the place linguistics hides in NLP. in spite of the fact that, guide annotation has mostly been overlooked for a while, and it has taken it slow even for annotation guidance to be famous as essential.
Although a few efforts were made in recent times to handle many of the matters provided by means of handbook annotation, there has nonetheless been little learn performed at the topic. This ebook goals to supply a few worthy insights into the subject.
Manual corpus annotation is now on the center of NLP, and remains to be principally unexplored. there's a desire for handbook annotation engineering (in the feel of a accurately formalized process), and this e-book goals to supply a primary step in the direction of a holistic technique, with an international view on annotation.
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Extra resources for Collaborative Annotation for Reliable Natural Language Processing: Technical and Sociological Aspects
An Elementary Annotation Task (EAT) is a task that cannot be decomposed. We consider that an annotation task can be decomposed into at least two EATs if its tagset can be decomposed into independent reduced tagsets. Tagsets are independent when their tags are globally compatible (even if some combinations are not allowed), whereas the tags from a unique tagset are mutually exclusive (apart from the need to encode ambiguity). In the gene renaming campaign, for example, the annotation of the relations can be analyzed as a combination of two EATs: (i) identifying gene names in the source signal and (ii) indicating which of these gene names participate in a renaming relation.
Tagset dimension The size of the tagset is probably the most obvious complexity dimension. It relates to short-term memory limitations and is quite obvious when you annotate. However, a very large number of tags is not necessarily a synonym for maximum complexity: if they are well-structured, like in the structured named entity annotation task (31 types and sub-types), then the annotators have to make choices from a reasonable number of tags each time, at different levels. 9) they ﬁrst have to choose between seven main types (Person, Function, Location, Production, Organization, Time, Amount), which corresponds to a degree of freedom of 6.
5, as most of the time only the guidelines and a small co-text are needed to annotate. 5. Visualization Once the 6 complexity dimensions are computed, it is rather easy to put them into a spiderweb diagram to visualize the complexity proﬁle of the annotation task. This type of representation can prove useful to compare the complexity of different tasks. 14 present examples of what can be obtained applying the complexity grid, even in a fuzzy way (for the POS annotation task). In the Penn Treebank POS annotation task, the corpus was pre-segmented and pre-annotated, so the discrimination and Annotating Collaboratively 39 delimitation are null.