![]() ![]() You can test it with the file coref_example.jsonl Unzip PreCo dataset, run tagEditor and select menu File->Load PreCO/Coref->(select file). You can load data from PreCo dataset to TagEditor directly. Entity color can be changed except for singleton. Entities which are not singletons are added to the table automatically. If the text is long and you don't want to scroll it just click on an entity in the table to get spans linked. You can also use the table on the right side. To unlink a span from the entity, select it and then click on it again. To deselect just click on empty space in the main window. While it is in selected state click on another entity and they will be linked together and highligted by same color and get same coref number (a num in the right corner of frame). Everytime you select an entity it is highlighted by green color frame. It will be a singleton(single entity) with no connection to other entities and framed with dash line. Select in the editor window a word or a span of words. End_index is the index of the last token of the mention in the sentence plus one. Begin_idx is the index of the first token of the mention in the sentence. Sentence_idx is the index of the sentence of the mention. Each mention cluster is a list of mentions. "mention_clusters" - is a list of mention clusters. Each token is a string, which can be a word or a punctuation mark. To use NeuralCoref for annotating select "Enable NeuralCoref" after 'Start tagging'. Click on the word again to remove the tag.Ĭoreference annotation is according to PreCo 'Data Format'.Ĭompatible with NeuralCoref 4.0. Click on another word(token) to assign a head tag. Select a tag in TAG SET pannel then click on a word in the editor window to assign the tag. In this window you can edit POS tags (fine-grained) and also view coarse-grained pos tags and morphs. if you assigned paragraphs - select Manually assigned paragraphs Press button Create data, select items and save as *.spacy, *.txt or *.json format or print it on the screen. TAG SET panel allows editing labels, adding new labels and their description, saving and uploading labels. ![]() This way you can compare two(or more) different models or just to annotate text using several models in tandem. Option -Annotate- allows to switch models and annotate on top of your already annotated text in different modes. If the option NER search all is on and you selected a new span - selected label will be assigned to all spans found in the text accordingly. It is allowed to create nested or overlapping tags if you use char/token offset. ![]() Create output data with char/token offset or BILUO / IOB scheme. To delete assigned label just click on it in the editor window. If you don't have a pretrained model for a given language, select language from the list for proper tokenization:įirst click on a label in the Tag Set pannel then select words in the main window that you want to assign label to. Load project to continue where you left.Īlso you can save and load your datasets in formats. You can save it in text, json or spacy format. Press button Create DATA to create training data in "simple training style" or JSON. All Sentence starts are highlighted with rose color and whitespaces - with yellow color. To delete all newline characters and extra whitespaces in the text, select the tab Words and press Remove Whitespaces. Or use button Assign paragraphs in the tab Words to assign paragraphs after new line symbols '\n' in text. To assign new paragraph use context menu or click on the sentence number on the left side. Uncheck it and the sentence will merge with the previous sentence. To merge sentences right-click on the first word of sentence. Context menu allows to edit, delete, insert words or sentences, also merge or split sentences. To edit Doc or Tokens - use Right-click on any word. Select a head tag to assign dependency if you are working in the Dependencies window. Select a tag in TAG SET pannel then select a word to assign the tag. Or you can start with loading your datasets in formats. Choose type of annotation and labels like in the screenshot below and press Ok. ![]() Insert your text or open a text file and press Start tagging (or choose one of the options in Menu/Tools). spacy for training with spaCy library or pytorch. With TagEditor you can annotate dependencies, parts of speech, Named entities, text categories and Coreference resolution, create your customized annotated data or create a training dataset in formats. TagEditor is a desktop application (requires Windows 10, 64-bit) that allows you to quickly annotate text with the help of spaCy library. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |