cwi.md Class project: Complex Word Identification Andreas Vlachos and Fernando E Alva Manchego The goal of the lab sessions in weeks 3, 5, 6, 8 and 10 is to develop a system for the Complex Word Identification shared task https://sites.google.com/view/cwisharedtask2018/. The demonstrators and myself will be available during the lab sessions to help you if/when you […]
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1 Language Modelling One of the most basic tasks for handling textual context is language modelling, which is an unsupervised machine learning problem. In this assignment, you will explore the usage of language modelling for predicting which emoji a tweet will likely end with. This could later form the basis of an opinion mining model.
## Overview The practical exercises are based around a program for predicting dictionary head words, given a definition. A neural network is trained to compose words in a definition so that the resulting definition vector is close to the vector for the corresponding head word. For example, one of the training instances could be: <fawn,
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Last Modified: March 5, 2018 CS 295: Statistical NLP: Winter 2018 Homework 4: Neural Machine Translation Sameer Singh (and Robert L. Logan) http://sameersingh.org/courses/statnlp/wi18/ One of the most widespread and public-facing applications of natural language processing is machine trans- lation. It has gained a lot of attention in recent years, both infamously for its lack of
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Natural Language Processing – Mini-Project 1 Introduction Aarthi Reddy March 6, 2018 DEADLINE: April 4th, 2018; 8am In this assignment you will be asked to perform some tasks that are commonly used in Natural Language Processing. As in the previous project, you will be writing a shiny app to display your visualizations/work. Again as in
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lab7_ner.md Lab 7: Named entity recognition with the structured perceptron Andreas Vlachos The goal of this lab session is learn a named entity recognizer (NER) using the structured perceptron. The named entity recognizer will need to predict for each word one of the following labels: O: not a named entity PER: part of a person’s
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Named Entity Recognition Due date: 2018-05-25 This assignment is worth 20% of your final assessment. In this assignment, you will build and evaluate a named entity recognition (NER) system via sequence tagging, or you will perform a systematic comparison of existing NER approaches. 3.0.1 Option I: Implementation • Implement the Viterbi algorithm for predicting the
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Computer Science 401 St. George Campus 8 January 2018 University of Toronto Homework Assignment #1 Due: Monday, 12 February 2018 at 19h00 (7pm) Red team, blue team: Identifying political persuasion on Reddit TAs: Willie Chang (williehwc@cs) and Ramin Zaviehgard (ramin.hamedi@mail.utoronto) Introduction This assignment will give you experience with a social media corpus (i.e., a collection
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COMP6714 Project 2 Specification ver 1.0.0 Last updated: 17 Oct 2017 Submission Deadline: 13 Nov, 2017 (23:59:59 PM ) In this Project, you will be implementing your own Word Embeddings for adjectives. More specifically, we want the obtained embeddings to preserve as much synonym relationship as possible. This will be measured against a set of ground
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# COMP6714-Project 1 ### Submission Deadline for the Project is 23:59:59 PM, on **25 Sep, 2017** This ipython notebook illustrates the requirements for **COMP6714 Project-1**. Instructions: 1. You need to implement your code in **`submission.py`** file, provided along with this notebook. 2. For all questions, your codes in the file **`submission.py`** need to output your
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