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Natural Language Processing (NLP) 自然语言处理

JC4003: Natural Language Processing - Group Assessment: Understanding and Generating Explanations from the RuozhiBa Dataset

JC4003Natural Language ProcessingRuozhiBaData Annotation

In this group assessment, you will explore and experiment with traditional machine learning and deep learning models, including large language models (LLMs), to generate accurate meanings and explanations for the samples provided in the RuozhiBa dataset. The purpose of this exercise is to apply your knowledge from the course to a real-world dataset, practicing your skills in data annotation, model design, and evaluation.

Assignment 2: Content Analysis and Regression

Linear RegressionPythonComputational content analysisNLP

For this assignment, you need to test a hypothesis using multiple linear regression. Before doing that, you also need to use computational content analysis and NLP techniques to create new variables that you will use as a predictor in the regression model.

CISC3025 Natural Language Processing Project 3: Maximum entropy model

CISC3025Natural Language ProcessingPythonNLPmaximum entropy model

In this project, you will be building a maximum entropy model (MEM) for identifying person names in newswire texts (Label=PERSON or Label=O)

Natural Language Engineering: Assessed Coursework: Classification

NLPPythonClassifierNaive Bayes

Design and carry out an experiment into the impact of the length of the wordlists on the wordlist classifier. Make sure you describe design decisions in your experiment, include a graph of your results and discuss your conclusions.

EECS595: Natural Language Processing Homework 4: Probabilistic Context Free Grammar and Dependency Parsing

EECS595Natural Language Processing

This exercise is to get you familiar with dependency parsing and the Stanford CoreNLP [1] toolkit. You may also need to consult the inventory of universal dependency relations. You have two options to complete this exercise.

EECS595: Natural Language Processing Homework 4: Probabilistic Context Free Grammar and Dependency Parsing

EECS595Natural Language ProcessingProbabilistic Context Free GrammarDependency Parsing

This exercise is to get you familiar with dependency parsing and the Stanford CoreNLP [1] toolkit. You may also need to consult the inventory of universal dependency relations. You have two options to complete this exercise.

[2021] CS3002 Artificial Intelligence - Final Exam - Q7: Deep Learning for Natural Language Processing

CS3002Artificial IntelligenceExam Help

This question is part of the CS3002 Artificial Intelligence, final exam May 2021, Brunel University London

Word Representation in Biomedical Domain

PythonNatural language自然语言处理TokenizationNLTKByte-Pair Encoding

BERT introduces a new language model for pre-training named Masked Language Model (MLM). The advantage of MLM is that the word representations by MLM will be contextualised.

HDAG Interview Take Home Assignment - Data Analytics

Data AnalyticsVisualizationNLPMachine Learning

This take-home assignment is meant to evaluate your background and fit for a role within HDAG.

NLP with Representation Learning, Fall 2022 - Homework 1: N-Gram Models, Additive Smoothing, Interpolation Smoothing, Backoff and Neural Networks

NYUNew York UniversityNLP with Representation LearningN-Gram ModelsAdditive SmoothingInterpolation Smoothing

In this section, you will implement a basic n-gram model with no smoothing. The core challenge in doing this is counting the number of occurrences of different n-grams in the training data.

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