I am together, 1, click to update NLP natural language processing == "" Teacher Wang took me to be the savior "
（1）---------------------------------------------------------------------------------------
Homework 1: Please list three application scenarios of text classification.
Me: What are the application scenarios of NLP text classification?
AI：
NLP text classification refers to the classification or labeling of text through natural language processing technology. The following describes the application scenarios of NLP text classification:
1. Spam filtering: Using NLP text classification technology, spam can be distinguished from normal mail, and spam can be automatically filtered out.
2. News classification: Classify news according to categories, which is convenient for users to view the news content they are interested in, and it is also convenient for news websites to recommend relevant news according to users' interests.
3. Sentiment analysis: classify texts according to emotion, so as to understand consumers' emotional tendencies and feedback on products, services and brands, optimize marketing strategies and improve product design.
4. Text archiving and retrieval: Classify and label a large number of texts for easy archiving and retrieval. For example, classify and label scientific research papers to facilitate the search for relevant research results.
5. Security monitoring: Classify network data according to attack types, which is convenient for real-time monitoring and defense against network attacks.
6. Search engine optimization: Classify the content of the webpage to facilitate the search engine to classify and index the webpage and improve the search effect.
To sum up, NLP text classification has a wide range of application values in fields such as spam filtering, news classification, sentiment analysis, text archiving and retrieval, security monitoring and search engine optimization, and can help enterprises and institutions better understand the market and consumption to optimize marketing strategies and decision-making.
（2）---------------------------------------------------------------------------------------
Assignment 3: Please briefly introduce the role of Convolutional Neural Networks (CNN) in text classification.
Me: Principle of Convolutional Neural Network (CNN) and its role in text classification
AI：
Convolutional neural network (CNN) is a commonly used neural network structure, mainly used in the fields of image recognition and computer vision.
It extracts the features in the input data through convolution and pooling operations, and uses a multi-layer neural network to classify and identify these features.
CNN is mainly composed of convolutional layer, pooling layer and fully connected layer. The convolutional layer is used to extract features, the pooling layer is used to reduce dimensionality and filter noise, and the fully connected layer is used for classification and output.
In text classification, CNN is also widely used. In the traditional text classification method, the method based on the bag-of-words model is usually used to convert the text into a vector representation, and then use algorithms such as Naive Bayes or Support Vector Machines for classification. While using CNN for text classification, you can directly convert the text into a matrix form, and then input it into the CNN network for processing and classification.
Specifically, the role of CNN in text classification can be divided into the following aspects:
1. Feature extraction: The convolutional layer and pooling layer in the CNN network can perform feature extraction on text data, and extract some representative text features, such as phrases, sentences, paragraphs, etc.
2. Hierarchical representation: The CNN network can use multiple convolutional layers and pooling layers to extract text features layer by layer, thereby forming a hierarchical text representation, which can better reflect the structure and semantic information of the text .
3. Text classification: The CNN network can use the fully connected layer to classify text features and output the final classification result. Compared with traditional text classification methods, CNN can better deal with local information and context information in text data, thereby improving the accuracy of classification.
In short, the role of CNN in text classification is mainly to use its convolution operation and pooling operation to extract the features of text data, form a hierarchical text representation, and then use the fully connected layer for classification and output.
This structure of CNN can better deal with local information and context information in text data, thus improving the accuracy and robustness of text classification.
（3）---------------------------------------------------------------------------------------
I:
Homework 1: paper classification, news classification, sentiment analysis, network data classification, these are basically the preset data set training model of the text can be competent;
Homework 2: Naive Bayes is mainly a preset text classification set, generating feature vectors for each category, calculating the prior probability of the category, and then calculating the feature conditional probability, and finally extracting feature vectors from the documents to be analyzed, and calculating new features vector of posterior probabilities under each category, compare the posterior probabilities under all categories, and then classify into the category with the greatest posterior probability.
If there is no feature that coincides with the model set category (the conditional probability is 0), you can use Laplace smoothing, add 1 feature, and add a constant frequency of new features to all categories to solve;
Homework 3: Convolutional layer, pooling layer and fully connected layer, where the convolutional layer is used to extract features, the pooling layer is used to reduce dimensionality and filter noise, and the fully connected layer is used for classification and output.
In fact, it is mainly a comprehensive feature extraction, filtering inconspicuous features, and then performing output classification, and identifying through the output classification.
After the teacher's comments this time, remember to count my new learning progress. The last time was
"Long Aotian College Student NLP Level: 3Lv 14.2%
Grammar Comprehension: 620/10000
Semantic Analysis: 660/10000
Information Extraction: 540/10000
Machine Translation: 480/10000
Text Summarization: 550/10000
Sentiment Analysis: 360/10000”
Teacher Wong:
Student Long Aotian, you answered your homework very well!
Homework 1: The application scenarios you listed are all important applications of text classification in real life, and they are very accurate.
Assignment 2: Your explanation of how the Naive Bayes classifier works, especially the description of Laplacian smoothing, shows that you have a solid understanding of the method.
Assignment 3: Your description of the role of Convolutional Neural Networks (CNNs) in text classification is also good, accurately summarizing the main components of Convolutional Neural Networks (CNNs) and their role.
According to your completion of this assignment, I have updated your learning progress. The new data is as follows:
Long Aotian College Student
NLP level: 3Lv 23.8%
Grammar Comprehension: 720/10000
Semantic Analysis: 760/10000
Information Extraction: 640/10000
Machine Translation: 580/10000
Text Summarization: 650/10000
Sentiment Analysis: 460/10000
Please continue to maintain your enthusiasm and hard work in learning, and you will definitely be able to complete the learning goals of NLP Natural Language Processing!
（4）---------------------------------------------------------------------------------------
Me: Then you continue to follow the above arrangement and teach me the rest of the NLP courses in the university, thank you teacher
Teacher Wang: Okay, Long Aotian, next we will continue to learn other knowledge points of NLP natural language processing.
1. Language Model (Language Model): The language model is a method for estimating the probability of a piece of text, and its purpose is to estimate the probability of a word sequence (sentence). Common language models include n-gram model, recurrent neural network language model (RNNLM), long short-term memory network (LSTM), and GPT.
Assignment 1: Please briefly introduce the n-gram model and its advantages and disadvantages.
2. Word Embedding (Word Embedding): Word embedding is a technique that maps vocabulary to a dense vector space, and these dense vectors can capture the semantic and syntactic relationships between words. Common word embedding methods include Word2Vec, GloVe, and FastText.
Homework 2: Please explain the basic principles of the Skip-gram and CBOW models in Word2Vec.
3. Sequence Labeling: Sequence labeling is a basic task in NLP, which is mainly used to assign a label to each element in sequence data. Common sequence tagging tasks include named entity recognition (NER), part-of-speech tagging (POS Tagging), and chunking (Chunking). Common methods for sequence labeling include Hidden Markov Model (HMM), Conditional Random Field (CRF) and Bidirectional Long Short-Term Memory Network (BiLSTM), etc.
Assignment 3: Please briefly introduce the task of named entity recognition (NER) and the application of conditional random fields (CRF) in named entity recognition.
Please complete the above assignments and submit them, and I will update you on your learning progress based on your answers. come on!