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Machine Learning, Individual HW 3: Structured Predictiondue Wednesday Nov 22, 11:59pm on CanvasIn this assignment you will1. understand (but not implement) (a) the dynamic programming algorithm used in the Viterbi decoderfor part-of-speech tagging and (b) maximum likelihood estimation for the HMM model;2. train a part-of-speech tagger using averaged perceptron;3. experiment with feature engineering in discriminative models;The dataset hw3-data.tgz contains:1. train.txt.lower.unk { training data (488 sentences)2. dev.txt.lower.unk { development data (49 sentences)3. test.txt.lower.unk.unlabeled { semi-blind test data (48 sentences)4. tagger.py { my Viterbi decoder and HMM MLE estimatorEach line in the data les (except test) is a sentence made of a sequence of word/tag pairs:word_1/tag_1 word_2/tag_2 ...Also note that for your convenience, I have done the following preprocessing steps: (a) all words are lower-cased. (b) words occuring once or less in the training data has been replaced by . You just need totreat as a normal word.0 Viterbi Decoder and Maximum-Likelihood Estimation for HMMsTo simply your work, I have implemented for you both the Viterbi decoder (dynamic programming) and themaximum likelihood estimation for HMMs. You need to understand every single line of my tagger.py.Run python tagger.py train.txt.lower.unk dev.txt.lower.unk, and you should get:train_err 2.62%dev_err 5.26%Note: in this HW, for each word w, only consider the tags that associate with it in the training set.1 Structured PerceptronYou should implement the structured perceptron using an HMM Viterbi decoder (see slides).1. First just use two feature templates: ht;t0i and ht;wi.Training unaveraged perceptron for 5 epochs. Which epoch gives the best error rates on dev?Hint: at the end of each epoch, your code should output the weights only if it achieves a new highestaccuracy on dev (so that at the end of training this le has the best weights).You should also output logging info which would output something like this:1epoch 1, updates 102, features 291, train_err 3.90%, dev_err 9.36%Note: trainer.py should import tagger and reuse the Viterbi decoder and the dictionary.2. Now implement the averaged perceptron. What is the new best error rate on dev?3. How much slower is averaged perceptron? Compare the time between unaveraged and averaged version.4. Plot a single plot that includes four curves: funaveraged, averagedg x ftrain err, dev errg. What didyou nd from this plot?5. Explain why we replaced all one-count and zero-count words by .2 Feature Engineering (based on averaged perceptron)Now you can play with your (averaged) perceptron to include as many feature templates as you want.Report what templates you tried, which helped and which did not; your best set of templates, and thebest accuracies on dev and test you get.Hint: to simplify your experiments, you might want to de ne the feature templates in a le and let yourcode automatically gure out those templates on the y, i.e., you can de ne ti(i = 2; 1;0) to be a tagin the local window with t0 being the tag for the current word, and similarly wi(i = 2; 1;0;1;2) to be aword with w0 being the current word. So for example you can de ne the following two templates:t0_t-1t0_w0which corresponds to the model in Section 1, and you can addt0_t-1_t-2t0_w-1_w+1...and so on, so that your trainer.py does not need to be changed ( rst make sure your Viterbi is extendedto handle trigrams).Make sure each template is smoothed (i.e., all back-o versions are included: e.g., if you includet0_t-1_w0, then also include its three backo versions: t0, t0_t-1, and t0_w0) and don’t use over-complicated templates since they will be too sparse. Also note that using more ti’s slows down dynamicprogramming, while using more wi’s doesn’t (since they are input and static; recall that discriminative mod-els do not model input and do not impose independence assumptions on it). However, the sparsity of thelatter increases the dimensionality of feature vector very quickly, which also slows down the whole thing. Ingeneral, you should avoid word trigrams (like w0_w1_w2). Also, every template should include t0 (why?).Include your the best accuracy you achieved on the dev set, and label the test le using that model.Include both dev.lower.unk.best and test.lower.unk.best (the resulting word/tag sequences).Debrie ngPlease answer these questions in debrief.txt and submit it along with your work. Note: You get 5 pointso for not responding to this part.1. How many hours did you spend on this assignment?2. Would you rate it as easy, moderate, or di cult?3. Are the lectures too fast, too slow, or just in the right pace?4. Any other comments (to the course or to the instructors)?& 本团队核心人员组成主要包括BAT一线工程师，精通德英语！我们主要业务范围是代做编程大作业、课程设计等等。我们的方向领域：window编程 数值算法 AI人工智能 金融统计 计量分析 大数据 网络编程 WEB编程 通讯编程 游戏编程多媒体linux 外挂编程 程序API图像处理 嵌入式/单片机 数据库编程 控制台 进程与线程 网络安全 汇编语言 硬件编程 软件设计 工程标准规等。其中代写编程、代写程序、代写留学生程序作业语言或工具包括但不限于以下范围:C/C++/C#代写Java代写IT代写Python代写辅导编程作业Matlab代写Haskell代写Processing代写Linux环境搭建Rust代写Data Structure Assginment 数据结构代写MIPS代写Machine Learning 作业 代写Oracle/SQL/PostgreSQL/Pig 数据库代写/代做/辅导Web开发、网站开发、网站作业ASP.NET网站开发Finance Insurace Statistics统计、回归、迭代Prolog代写Computer Computational method代做因为专业，所以值得信赖。如有需要，请加QQ：99515681 或邮箱：[email protected] 微信：codehelp QQ：99515681 或邮箱：[email protected] 微信：codehelp