I am currently looking into structuring data and work flows for my ML end to end pipeline. I therefore have multiple problems, and ideally I am looking for one platform that can do all: Visualize and organize multiple datasets. ideally something like the Kaggle datset webinterface Do dataset exploration to quickly visualize errors in data, biases in annotations etc. Annotate images and potentially point clouds commenting functionality for all features Keep track of who annotated what on what date dataset …
I am currently playing around with tensorflows object detection to learn the basics. Now I've set myself the goal to detect letters in computer written text. For example the header of a newspaper article. I know that object detection might not be the way to go for letter detection but I wanted to know how well a object detection model performes when input data is perfectly similar( computer generated fonts). My Question: I encountered the problem that manually annotating each …
Lets say we have two trained models Ma and Mb which were trained with different datasets in a Named Entity Recognition task. Those datasets A and B contain different document and also variables or text to recognize. For example: Model A has been trained on dataset A with variables A_NAME, A_SURNAME, A_TITLE Model B has been trained on dataset B with variables B_ORG, B_COUNTRY, B_ADDRESS We now want to have a model Mc which detects all those variables altogether, but …
I have several annotators who annotated strings of text for me, in order to train an NER model. The annotation is done in json format, and it consists of a string followed by the start and end index of named entities, along with their respective entity type. What is the best way to calculate the IAA score in this case? Is there a tool, or Python library available?
I am working on annotating a dataset for the purpose of named entity recognition. In principle, I have seen that for multi-phrase (not single word) elements, annotations work like this (see this example below): Romania (B-CNT) United States of America (B-CNT C-CNT C-CNT C-CNT) where B-CNT stands for "beginning-country" and C-CNT represents "continuing-country". The problem that I face is that I have a case in which (not related to countries) where I need to annotate like B-W GAP_WORD C-W C-W. …
I have been searching around for a software tool, that I can use for annotating images. More specifically I want to do annotation to be used for semantic segmentation, meaning I want to create masks. I want to be able to create training data for applying a segmentation CNN (like for instance U-net). However I have been digging around the internet, and I have tried out some options. But I have not really found anything that seems to do the …
I am writing an ETL pipeline for geospatial data of the form place_name,address,longitude,latitude,id_linking_to_other_dataset As the last step in the pipeline, I would like to apply manual transformations submitted by reviewers. Some of these transformations might be (borrowing from Google maps suggest edits docs): Change a place's name, location, or the id linking it to another dataset Mark a place private or non-existent Mark a place as moved or duplicated I don't have a ton of records (about 5000) but would …
I have my custom dataset images of size (1080 x 1920) and I am trying to use yolov3 for object detection. I noticed that yolov3 model accepts an input image size of 416 x 416. So I am in confusion if I should resize the image and apply zero-padding to save the aspect ratio and start my annotation after that OR should I annotate my custom images in original size? And will data argumentation affect the annotation while training? Thanks
How would you most likely create a large production ready image training dataset from scratch including annotations for a image classification task? We will take a large amount of images (~1 million) with industrial cameras and save them in a S3 bucket. Do you think a data lake infrastructure is necessary? In your opinion, what are the most suitable methods for annotating the images in the shortest possible time (bounding boxes not needed). Solutions that I have been able to …
I read the book "Human-in-the-Loop Machine Learning" by Robert (Munro) Monarch about Active Learning. I don't understand the following approach to get a diverse set of items for humans to label: Take each item in the unlabeled data and count the average number of word matches it has with items already in the training data Rank the items by their average match Sample the item with the lowest average number of matches Add that item to the ‘labeled’ data and …
I have a large texts in each document and I want to know if there are any open source text annotation tools available online for multiple label annotation. Each sentence takes two labels. If there are any please let me know.
I'm looking for tools that would help me and my team annotate training sets. I work in an environment with large sets of data, some of which are un- or semi-structured. In many cases there are registration that help in finding a grounded truth. In many cases however a curated set is needed, even if it just were for evaluation. A complicating factor is that some of the data can not leave the premise. We are looking to annotate an …
Currently looking for a good tool to annotate sentences regarding aspects and their respective sentiment polarities. I'm using SemEval Task 4 as a reference. The following is an example in the training dataset: <sentence id="2005"> <text>it is of high quality, has a killer GUI, is extremely stable, is highly expandable, is bundled with lots of very good applications, is easy to use, and is absolutely gorgeous.</text> <aspectTerms> <aspectTerm term="quality" polarity="positive" from="14" to="21"/> <aspectTerm term="GUI" polarity="positive" from="36" to="39"/> <aspectTerm term="applications" polarity="positive" …
I need to find a decent online annotation tool to transcribe audio. There are some requirements for a potential tool: I should be able to deliver audio files to a few labelers. I should be able to track which files went to which labeler. It should be safe in terms of data storage. Any suggestions?
My specific question is how NLP data from multiple human annotators should be aggregated - though general advice related to the question title is appreciated. One critical step that I've seen in research is to assess inter-annotator agreement by Cohen's kappa or some other suitable metric; I've also found research reporting values for various datasets (e.g. here), which is helpful for baselining. How many annotators should work on each data point depends on time, personnel, and data size requirements/constraints, among …
I came across curlie.org (previously known as the dmoz taxonomy) and I'm interested to see how I could best start tagging a given text, with concepts from that taxonomy: Are there any tools out there that do semantic annotation based on a taxonomy (I couldn't find any) How would one go about making such a semantic annotation process I know this question might be a large to answer in a short reply, but any pointers are greatly appreciated. Thanks in …
I am looking for a financial corpus or any form of publicly available financial texts which is replete with technical terms and acronyms. Any suggestion is appreciated.
I am trying to understand Mask RCNN. For that I have to input image with mask in png format while building the model. I try to follow the article present in this blog. The blogger used Pixel Annotation Tool. I tried to follow her steps. I downloaded all the requirements for this tool. Like QT, Open CV, CMAKE and VS 2015 + When I try to update the build script as mentioned here for windows. I am unable to find …
We have 7 million news articles corpus, which we want to classify into crimes or non-crimes and further identify criminals by using NERs/annotating criminals, crime manually. For creating a model that identifies criminals, what is the number of annotated articles that we must train/build our model on? Is there any industry best practice on this count? Is there any better way to come to this number of training(annotated) dataset, than random guessing? Are there any best practices resources that anyone …