How to build a Chatbot in R with SVM model --- a basic introduction скачать в хорошем качестве

How to build a Chatbot in R with SVM model --- a basic introduction

Chatbot for answering summer intern recruitment questions Methdology 1. Convert training questions into document term matrix (sparse matrix with 1s and 0s) 2. Match the matrix of each training question with its corresponding answer to form a training matrix 3. Train SVM model with the training matrix 4. Propose a testing quesiton 5. Convert the testing question into document term matrix (sparse matrix with 1s and 0s) 6. Merge the testing DTM with training DTM, with testing DTM 1s for all terms and training DTM 0s for all terms 7. Predict the answer with the trained SVM model read data data = read.csv('Q&A data.csv') 1. Convert training questions into document term matrix (sparse matrix with 1s and 0s) #clean the text library(SnowballC) library(tm) corpus = VCorpus(VectorSource(data$Question)) corpus = tm_map(corpus, content_transformer(tolower)) corpus = tm_map(corpus, removeNumbers) corpus = tm_map(corpus, removePunctuation) corpus = tm_map(corpus, removeWords, stopwords()) corpus = tm_map(corpus, stemDocument) corpus = tm_map(corpus, stripWhitespace) convert to DTM dtm = DocumentTermMatrix(corpus) convert to dataframe dataset = as.data.frame(as.matrix(dtm)) 2. Match the matrix of each training question with its corresponding answer to form a training matrix data_train= cbind(data['Response'], dataset) 3. Train SVM model with the training matrix library("e1071") svmfit = svm(Response ~., data_train, kernel = "linear", cost = 100, scale = FALSE) 4. Propose a testing quesiton and build the prediction function pred = function(x){ 5. Convert the testing question into document term matrix (sparse matrix with 1s and 0s) #clean the text corpus = VCorpus(VectorSource(x)) corpus = tm_map(corpus, content_transformer(tolower)) corpus = tm_map(corpus, removeNumbers) corpus = tm_map(corpus, removePunctuation) corpus = tm_map(corpus, removeWords, stopwords()) corpus = tm_map(corpus, stemDocument) corpus = tm_map(corpus, stripWhitespace) convert to DTM dtm = DocumentTermMatrix(corpus) convert to dataframe data_test = as.data.frame(as.matrix(dtm)) 6. Merge the testing DTM with training DTM, with testing DTM 1s for all terms and training DTM 0s for all terms add_data = dataset[1,] add_data[add_data == 1] = 0 data_test=cbind(data_test,add_data) 7. Predict the answer with the trained SVM model p = predict(svmfit, data_test) answer = as.character(p) Predict paste("Answer:", answer) } Predict pred("How are you today?")