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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/probabilistic-models-in-nlp
课程评论：没有评论
课程名称：概率模型的自然语言处理 课程概要：在自然语言处理专业化的第二部分中，您将学习以下内容： a) 使用最小编辑距离和动态规划创建一个简单的自动纠错算法， b) 应用维特比算法进行词性标注（POS），这对计算语言学至关重要， c) 使用N-gram语言模型编写更好的自动完成功能算法， d) 编写自己的Word2Vec模型，使用神经网络通过连续词袋模型计算词嵌入。 在本专业化课程结束时，您将能够设计执行问题回答和情感分析的NLP应用程序，创建翻译工具和文本摘要，甚至构建聊天机器人！ 该专业化课程由两位NLP、机器学习和深度学习的专家设计和教授。Younes Bensouda Mourri是斯坦福大学的人工智能讲师，曾参与构建深度学习专业化课程。Łukasz Kaiser是谷歌大脑的员工研究科学家，同时是Tensorflow、Tensor2Tensor和Trax库的共同作者，以及Transformer论文的合著者。 课程大纲： 第一部分：自动纠错 描述：了解自动纠错、最小编辑距离和动态规划，然后构建自己的拼写检查器来纠正拼写错误！ 第二部分：词性标注和隐马尔可夫模型 描述：了解马尔可夫链和隐马尔可夫模型，然后使用它们为华尔街日报的文本语料库创建词性标签！ 第三部分：自动补全和语言模型 描述：学习N-gram语言模型如何通过计算序列概率来工作，然后使用来自Twitter的文本语料库构建自己的自动补全语言模型！ 第四部分：使用神经网络进行词嵌入 描述：了解词嵌入如何传递单词的语义意义，使其在NLP任务中更强大，然后构建自己的连续词袋模型，以从莎士比亚的文本中创建词嵌入。
Part: 1
Title:Autocorrect
Description:Learn about autocorrect, minimum edit distance, and dynamic programming, then build your own spellchecker to correct misspelled words!
Part: 2
Title:Part of Speech Tagging and Hidden Markov Models
Description:Learn about Markov chains and Hidden Markov models, then use them to create part-of-speech tags for a Wall Street Journal text corpus!
Part: 3
Title:Autocomplete and Language Models
Description:Learn about how N-gram language models work by calculating sequence probabilities, then build your own autocomplete language model using a text corpus from Twitter!
Part: 4
Title:Word embeddings with neural networks
Description:Learn about how word embeddings carry the semantic meaning of words, which makes them much more powerful for NLP tasks, then build your own Continuous bag-of-words model to create word embeddings from Shakespeare text.
In Course 2 of the Natural Language Processing Specialization, you will: a) Create a simple auto-correct algorithm using minimum edit distance and dynamic programming, b) Apply the Viterbi Algorithm for part-of-speech (POS) tagging, which is vital for computational linguistics, c) Write a better auto-complete algorithm using an N-gram language model, and d) Write your own Word2Vec model that uses a neural network to compute word embeddings using a continuous bag-of-words model. By the end of this Specialization, you will have designed NLP applications that perform question-answering and sentiment analysis, created tools to translate languages and summarize text, and even built a chatbot! This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning. Younes Bensouda Mourri is an Instructor of AI at Stanford University who also helped build the Deep Learning Specialization. Łukasz Kaiser is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper.
