# Vector Semantics Note (SLP Ch06)

Words that occur in similar contexts tend to have similar meanings. This link between similarity in how words are distributed and similarity in what they mean is called the distributional hypothesis.

• words which are synonyms tended to occur in the same environment
• with the amount of meaning difference between two words “corresponding roughly to the amount of difference in their environments”

vector semantics instantiates this linguistic hypothesis by learning representations of the meaning of words directly from their distributions in texts.

# Logistic Regression Note (SLP Ch05)

In NLP, logistic regression is the baseline supervised machine learning algorithm for classification.

• discriminative classifier: like logistic regression
• only trying to learn to distinguish the classes.
• directly compute $P(c|d)$
• generative classifier: like naive Bayes
• have the goal of understanding what each class looks like.
• makes use of likelihood term $P(d|c)P(c)$

A machine learning system for classification has four components:

• A feature representation of the input
• A classification function that computes $\hat y$, the estimated class, via $p(y|x)$. Like sigmoid and softmax.
• An objective function for learning, usually involving minimizing error on training examples. Like cross-entropy loss function.
• An algorithm for optimizing the objective function. Like stochastic gradient descent.

# Naive Bayes and Sentiment Classification Note (SLP Ch04)

Text categorization, the task of assigning a label or category to an entire text or document.

• sentiment analysis
• spam detection
• subject category or topic label

Probabilistic classifier additionally will tell us the probability of the observation being in the class.

Generative classifiers like naive Bayes build a model of how a class could generate some input data.

Discriminative classifiers like logistic regression instead learn what features from the input are most useful to discriminate between the different possible classes.

# Regular Expressions, Text Normalization, and Edit Distance Note (SLP Ch02)

Normalizing text means converting it to a more convenient, standard form.

• tokenization: separating out or tokenizing words from running text
• lemmatization: words have the same root but different surface. Stemming refers to a simpler version of lemmatization in which just strip suffixes from the end of the word.
• sentence segmentation

# Information Extraction Note (SLP Ch17)

Recently, I wanted to build an information extraction system, so I searched for Google. However there were little Chinese articles, the quality was not so good as well. Fortunately, I found several English ones seemed well, and then the summary is here. The whole structure is based on my favorite NLP book Speech and Language Processing (use SLP instead below), also with some other materials in the reference.

Information extraction (IE), turns the unstructured information extraction information embedded in texts into structured data, for example for populating a relational database to enable further processing. Here is a figure of: Simple Pipeline Architecture for an Information Extraction System.

By the way, this book provides actionable steps, focusing on specific actions.

# 第十八章：自然语言处理中的理性主义与经验主义

• 理性主义：以生成语言学为基础的方法
• 经验主义：以大规模语料库的分析为基础的方法