21 Nov 16: Updated POS Tagging notes with Python 3 / Universal Tagset / Available as a Jupyter Notebook.16 Nov 16: Added reading links in Deep Learning Intro.Topics in Natural Language Processing (202-2-5381) Fall 2017 ( NOTE: If you complete the project in the workspace, then you can submit directly using the "submit" button in the workspace.Michael Elhadad - Natural Language Processing You must then export the notebook by running the last cell in the notebook, or by using the menu above and navigating to File -> Download as -> HTML (.html) Your submissions should include both the html and ipynb files.Īdd the "hmm tagger.ipynb" and "hmm tagger.html" files to a zip archive and submit it with the button below. Before exporting the notebook to html, all of the code cells need to have been run so that reviewers can see the final implementation and output. Once you have completed all of the code implementations, you need to finalize your work by exporting the iPython Notebook as an HTML document. All criteria found in the rubric must meet specifications for you to pass. Review this rubric thoroughly, and self-evaluate your project before submission. Your project will be reviewed by a Udacity reviewer against the project rubric here. See below for project submission instructions. Once you load the Jupyter browser, select the project notebook (HMM tagger.ipynb) and follow the instructions inside to complete the project. If the terminal prints a URL, simply copy the URL and paste it into a browser window to load the Jupyter browser. Open a terminal and clone the project repository:ĭepending on your system settings, Jupyter will either open a browser window, or the terminal will print a URL with a security token. You must manually install the GraphViz executable for your OS before the steps below or the drawing function will not work. (Optional) The provided code includes a function for drawing the network graph that depends on GraphViz. NOTES: These steps are not required if you are using the project Workspace. NOTE: If you are prompted to select a kernel when you launch a notebook, choose the Python 3 kernel.Īlternatively, you can download a copy of the project from GitHub and then run a Jupyter server locally with Anaconda. Simply open the lesson, complete the sections indicated in the Jupyter notebook, and then click the "submit project" button. The Workspace has already been configured with all the required project files for you to complete the project. The first method is to use the Workspace embedded in the classroom in the next lesson. You can choose one of two ways to complete the project. Please be sure to read the instructions carefully! Getting Started Instructions will be provided for each section, and the specifics of the implementation are marked in the code block with a 'TODO' statement. Sections that begin with 'IMPLEMENTATION' in the header indicate that you must provide code in the block that follows. You only need to add some new functionality in the areas indicated to complete the project you will not need to modify the included code beyond what is requested. The notebook already contains some code to get you started. Hidden Markov models have also been used for speech recognition and speech generation, machine translation, gene recognition for bioinformatics, and human gesture recognition for computer vision, and more. Hidden Markov models have been able to achieve >96% tag accuracy with larger tagsets on realistic text corpora. In this notebook, you'll use the Pomegranate library to build a hidden Markov model for part of speech tagging with a universal tagset.
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