💬Natural-Language-Processing

Backpropagation is perhaps the most important algorithm of the 21st century. It is used everywhere in machine learning and is also connected to computing marginal distributions. This is why all machine learning scientists and data scientists should understand this algorithm very well. An important observation is that this algorithm is linear: the time complexity is the same as the forward pass. Derivatives are unexpectedly cheap to calculate. This took a lot of time to discover....

Sentiment analysis is one of the oldest tasks in natural language processing. In this note we will introduce some examples and terminology, some key problems in the field and a simple model that we can understand by just knowing Backpropagation Log Linear Models and the Softmax Function. We say: Polarity: the orientation of the sentiment. Subjectivity: if it expresses personal feelings. See demo Some applications: Businesses use sentiment analysis to understand if users are happy or not with their product....

This note is still a TODO. Transliteration is learning learning a function to map strings in one character set to strings in another character set. The basic example is in multilingual applications, where it is needed to have the same string written in different languages. The goal is to develop a probabilistic model that can map strings from input vocabulary $\Sigma$ to an output vocabulary $\Omega$. We will extend the concepts presented in Automi e Regexp for Finite state automata to a weighted version....

Language Constituents A constituent is a word or a group of words that function as a single unit within a hierarchical structure This is because there is a lot of evidence pointing towards an hierarchical organization of human language. Example of constituents Let’s have some examples: John speaks [Spanish] fluently John speaks [Spanish and French] fluently Mary programs the homework [in the ETH computer laboratory] Mary programs the homework [in the laboratory]...

Log Linear Models can be considered the most basic model used in natural languages. The main idea is to try to model the correlations of our data, or how the posterior $p(y \mid x)$ varies, where $x$ is our single data point features and $y$ are the labels of interest. This is a form of generalization because contextualized events (x, y) with similar descriptions tend to have similar probabilities. These kinds of models are so common that it has been discovered in many fields (and thus assuming different names): some of the most famous are Gibbs distributions, undirected graphical models, Markov Random Fields or Conditional Random Fields, exponential models, and (regularized) maximum entropy models....