Adding annotation to your webpage using the Annotator is easy. Full instructions are in the Getting Started section of the docs, but it is just two short steps. First, you need to download the Annotator library (or link to the hosted version), include it on your page along with jQuery. Then add the following line to initialize the annotator.
How does one go about doing this process? I was thinking of using the kappa score, but is used for mutually exclusive classification not really for spans/ner. How would I go about computing a inter-annotator agreement score for a ner task where the annotators may have different number of annotations per documents?
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How many annotators will you have (a few like 2-3 or many 5+)? And will they all review the same ones or will each see different ones (e.g., every item is reviewed by three annotators but each annotator sees different examples)?
I ask b/c these could factor in a difference in performance (e.g., very technical text with expert annotators may have a lot of overlap as shared knowledge of entities while open-ended tasks to non-experts may have very little overlap in spans).
Grouin et al., 2011 also may help as they calculate Kappa, Scott's Pi or F-Measure but on a subset of terms with at least one annotator (e.g., pooling/span-overlap), only n-grams or noun-phrases only. They discuss some of the pros/cons of each approach.
using f1 score right before I saw this message.
I followed along with Hripcsak & Rothschild, 2005
I dealt with annotations that had overlaps by combining (all annotators intervals), sorting, then merging the intervals that overlap. If there were annotations that did not overlap (1 annotator annotated something that another didnt) I set the values for the annotation at that interval to be the value set and -1. Here is an example where the label is converted to an float (or int) and if there is a label that is -1 (say at interval [57,78]) AG didnt label that, but DC did. Then perform the F1 on this table (this table represents the labels for 1 document). I then averaged it for all documents.
So at the end I would have the original PDF and the note with the annotator plugin notes, which seems kind of messy... Do you have any suggestion? Should I save all pdfs in a separate folder for organizational purpouses?
We have presented a service for ontology-based annotation of biomedical data. Our biomedical annotator has access to a large dictionary, which is composed of UMLS and NCBO ontologies. OBA is not limited to the syntactic recognition of terms, but also leverages the structure of the ontologies to expand annotations.
Model suffix is explicitly stated when the annotator is the result of a training process. Some annotators, such as Tokenizer are transformers, but do not contain the word Model since they are not trained annotators.
This annotator matches a pattern of part-of-speech tags in order to return meaningful phrases from document.Extracted part-of-speech tags are mapped onto the sentence, which can then be parsed by regular expressions.The part-of-speech tags are wrapped by angle brackets to be easily distinguishable in the text itself.This example sentence will result in the form:
ClassifierDL uses the state-of-the-art Universal Sentence Encoder as an input for text classifications.The ClassifierDL annotator uses a deep learning model (DNNs) we have built inside TensorFlow and supports up to100 classes.
Note: This annotator accepts a label column of a single item in either type of String, Int, Float, or Double. UniversalSentenceEncoder, BertSentenceEmbeddings, or SentenceEmbeddings can be used for the inputCol
Converts DOCUMENT type annotations into CHUNK type with the contents of a chunkCol.Chunk text must be contained within input DOCUMENT. May be either StringType or ArrayType[StringType](using setIsArray). Useful for annotators that require a CHUNK type input.
LanguageDetectorDL is an annotator that detects the language of documents or sentences depending on the inputCols.The models are trained on large datasets such as Wikipedia and Tatoeba.Depending on the language (how similar the characters are), the LanguageDetectorDL worksbest with text longer than 140 characters.The output is a language code in Wiki Code style.
Note: This annotator accepts a label column of a single item in either type of String, Int, Float, or Double. UniversalSentenceEncoder, BertSentenceEmbeddings, SentenceEmbeddings or other sentence based embeddings can be used for the inputCol
A feature transformer that converts the input array of strings (annotatorType TOKEN) into anarray of n-grams (annotatorType CHUNK).Null values in the input array are ignored.It returns an array of n-grams where each n-gram is represented by a space-separated string ofwords.
This Named Entity recognition annotator allows for a generic model to be trained by utilizing a CRF machine learningalgorithm. The training data should be a labeled Spark Dataset, e.g. CoNLL 2003 IOB withAnnotation type columns. The data should have columns of type DOCUMENT, TOKEN, POS, WORD_EMBEDDINGS and anadditional label column of annotator type NAMED_ENTITY.Excluding the label, this can be done with for example
This Named Entity recognition annotator allows for a generic model to be trained by utilizing a CRF machine learningalgorithm. The data should have columns of type DOCUMENT, TOKEN, POS, WORD_EMBEDDINGS.These can be extracted with for example
The training data should be a labeled Spark Dataset, in the format of CoNLL2003 IOB with Annotation type columns. The data should have columns of type DOCUMENT, TOKEN, WORD_EMBEDDINGS and anadditional label column of annotator type NAMED_ENTITY.Excluding the label, this can be done with for example
This transformer reconstructs a DOCUMENT type annotation from tokens, usually after these have been normalized,lemmatized, normalized, spell checked, etc, in order to use this document annotation in further annotators.Requires DOCUMENT and TOKEN type annotations as input.
This annotator is based on the paper Chinese Word Segmentation as Character Tagging [1]. Wordsegmentation is treated as a tagging problem. Each character is be tagged as on of fourdifferent labels: LL (left boundary), RR (right boundary), MM (middle) and LR (word byitself). The label depends on the position of the word in the sentence. LL tagged words willcombine with the word on the right. Likewise, RR tagged words combine with words on the left.MM tagged words are treated as the middle of the word and combine with either side. LR taggedwords are words by themselves.
This annotator is based on the paperChinese Word Segmentation as Character Tagging. Wordsegmentation is treated as a tagging problem. Each character is be tagged as on of fourdifferent labels: LL (left boundary), RR (right boundary), MM (middle) and LR (word byitself). The label depends on the position of the word in the sentence. LL tagged words willcombine with the word on the right. Likewise, RR tagged words combine with words on the left.MM tagged words are treated as the middle of the word and combine with either side. LR taggedwords are words by themselves.
Extracting keywords from texts has become a challenge for individuals and organizations as the information grows incomplexity and size. The need to automate this task so that text can be processed in a timely and adequate manner hasled to the emergence of automatic keyword extraction tools. Yake is a novel feature-based system for multi-lingualkeyword extraction, which supports texts of different sizes, domain or languages. Unlike other approaches, Yake doesnot rely on dictionaries nor thesauri, neither is trained against any corpora. Instead, it follows an unsupervisedapproach which builds upon features extracted from the text, making it thus applicable to documents written indifferent languages without the need for further knowledge. This can be beneficial for a large number of tasks and aplethora of situations where access to training corpora is either limited or restricted.The algorithm makes use of the position of a sentence and token. Therefore, to use the annotator, the text should befirst sent through a Sentence Boundary Detector and then a tokenizer.
While every annotator can technically be run as a top-level component, in some cases it makes sense for one annotator to run another as a sub-annotator. For instance the coref annotator runs the coref.mention annotator (which identifies coref mentions) as a sub-annotator by default. So instead of supplying an annotator list of tokenize,parse,coref.mention,coref the list can just be tokenize,parse,coref. Another example is the ner annotator running the entitymentions annotator to detect full entities. Below is a table summarizing the annotator/sub-annotator relationships that currently exist in the pipeline. By default annotators will generally run their sub-annotators.
To allow dragging the vertices of the Curve, the Curve annotator uses the PointDraw tool in the toolbar: The vertices will appear when the tool is selected or a vertex is selected in the table. Unlike most other annotators the Curve annotator only allows editing the vertices and does not allow adding new ones.
Unlike the Points and Boxes annotators, the Path and Polygon annotators allow annotating not just each individual entity but also the vertices that make up the paths and polygons. For more information about using the editing tools associated with this annotator refer to the HoloViews PolyDraw and PolyEdit stream reference guides, but briefly: be457b7860
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