CS224N: Introduction to Natural Language Processing and Deep Learning

Welcome to CS224N, a course exploring the fascinating intersection of Natural Language Processing (NLP) and Deep Learning! This introductory lecture, presented by Christopher Manning, provides an overview of the field, its applications, and the course logistics.

What is Natural Language Processing?

Natural Language Processing (NLP), also known as Computational Linguistics, is a field at the intersection of computer science, linguistics, and artificial intelligence. The goal is to enable computers to understand and express themselves in human languages, similar to human beings.

NLP is a crucial part of AI, distinguished by language's unique properties. While other creatures possess vision systems, **language is distinctly human**, serving as a primary tool for thinking and communication.

The aim is to develop computers that can process and understand human languages to perform useful tasks, such as making appointments, buying things, or even understanding the state of the world. The increasing prevalence of mobile devices has significantly boosted the importance of NLP, with major tech companies like Google, Apple, Facebook and Amazon actively integrating NLP into their products and services.

The Challenge of Meaning

Understanding meaning is complex and referred to as AIcomplete. While full understanding is a lofty goal, even partial understanding can be valuable. The course will explore different approaches to achieving varying degrees of meaning comprehension.

Levels of Language

Human language can be analyzed at various levels:

Input: Speech or text.
Phonetic/Phonological Analysis: Understanding speech sounds.
Morphological Analysis: Analyzing word parts (e.g., prefixes, suffixes).
Syntactic Analysis: Understanding sentence structure.
Semantic Interpretation: Working out the meaning of sentences.
Pragmatics/Discourse Processing: Understanding context and how language is used.

This course primarily focuses on syntactic analysis and semantic interpretation, the core of natural language processing.

Applications of NLP

NLP applications are pervasive and range from simple to complex:

Simple: Spell checking, autocomplete.
Intermediate: Web search considering synonyms.
Complex: Information extraction from text, sentiment analysis, machine translation, spoken dialogue systems, and question answering.

NLP is increasingly used in commercial applications, from search engine optimization to sentiment analysis for financial markets and the burgeoning chatbot industry. Recent advancements in machine translation and speech recognition are particularly noteworthy.

What Makes Human Language Special?

Human language differs significantly from other data processed in signal processing and data mining. It's not just random data; it's a deliberate message constructed by a human being to communicate information to others.

Language is a discrete, symbolic system, using words to represent concepts. However, communication occurs through continuous substrates like audio waves (voice), visual representations (text), or sign language.

The challenge lies in bridging the gap between this symbolic system and the continuous patterns of activation in our brains. Deep learning offers a new approach, considering our brains as having continuous patterns of activation.

Language also presents a challenge due to its vast vocabularies, leading to data sparsity, which must be addressed.

Introduction to Deep Learning

Deep learning is a subfield of machine learning focused on enabling computers to learn automatically, without explicit programming. It stands apart from traditional machine learning approaches that rely on humandesigned features. Deep learning is part of representation learning, which attempts to have computers automatically learn good representations from raw signals.

Traditional machine learning often involves humans meticulously crafting features relevant to a specific problem. The machine then primarily performs numeric optimization based on these features. In contrast, deep learning aims for the computer to learn intermediate representations automatically, effectively inventing its own features.

**The core idea of deep learning is the use of multiple layers of learned representations.** This approach has proven to outperform other learning methods. The term 'deep learning' often refers to the use of neural networks, which will be the primary focus of this course.

Why is Deep Learning Exciting?

Manually designed features are often overspecified, incomplete, and timeconsuming to create. Learned features are adaptable, quick to train, and capable of achieving higher performance levels.

Deep learning provides a flexible framework suitable for representing various types of information, including linguistic, world, and visual data. Its success stems from realworld results, demonstrating superior performance compared to traditional machine learning methods. The field is rapidly advancing, with continuous improvements and new methods emerging frequently.

Why Has Deep Learning Succeeded?

While many deep learning techniques were developed decades ago, their recent success is attributed to technological advancements:

Vastly greater amounts of data available due to our online society.
Increased compute power allowing us to build systems that work. GPUs are particularly well suited for parallel vector processing.

However, algorithmic advancements, better ways of learning intermediate representations, endtoend joint system learning, and transfer learning have also played key roles. The first big breakthrough in deep learning was in speech recognition. Shortly thereafter, deep learning revolutionized computer vision with the ImageNet competition.

Course Logistics

The course is taught by Christopher Manning and Richard Socher (chief scientist at Salesforce). The TAs are also integral to the class, and are excited to help you learn.

Prerequisites

Proficiency in Python (assignments will be in Python).
Multivariate calculus and linear algebra.
Basic probability and statistics.
Fundamentals of machine learning (e.g., loss functions, gradient descent).

Course Goals

Understand and use effective modern methods for deep learning.
Gain a broad understanding of human languages and their complexities.
Build systems for important NLP problems.

Assignments and Grading

Three assignments.
Midterm exam.
Final project (either a studentproposed project or a default assignment).
Final poster session.

Assignment 1 will be in pure Python (NumPy library is acceptable) to ensure fundamental understanding. Assignments 2 and 3 will use TensorFlow, a popular deep learning library. Piazza will be used for online communication.

Why is NLP Hard?

NLP is challenging because human languages are inherently ambiguous, unlike programming languages. Humans use language as an efficient communication system, often omitting information, relying on the listener to fill in the gaps using world knowledge, common sense, and context.

Language is not a formal system, but "glorious chaos," filled with countless signals, contexts, and subtexts, each interpreted uniquely by the listener.

Examples of Ambiguity

Headlines often contain humorous ambiguities:

"Pope's baby steps on gays" (Is it about the Pope's literal baby, or the Pope taking baby steps on gay rights?)
"Boy paralyzed after tumor fights back to gain black belt" (Did the boy become paralyzed, then fight back? Or was he paralyzed after the tumor fought back?)

Deep Learning and NLP: Combining Forces

This course explores the application of deep learning techniques (neural networks, representation learning) to solve problems in natural language understanding and processing. This intersection of fields is rapidly evolving.

Deep learning models offer a unifying method for tackling various language problems, from speech and words to syntax and semantics. A small toolbox of key techniques has proven widely applicable across many tasks, often outperforming humanengineered solutions without significant customization.

Word Meaning as Vectors

A fundamental concept is representing words as vectors of numbers. These vectors place words in a highdimensional semantic space, where words with similar meanings cluster together. These vector spaces can often reveal directions in the vector space that actually tell you about components of meaning.

These vectors can be used to understand complex words and even parts of words, allowing for the construction of neural networks that compose the meaning of larger units from smaller pieces.

Beyond Word Vectors

Deep learning enables syntactic parsing to find the structure of sentences and understand their meaning. Meaning is also represented by vectors with neural networks allowing for automated learning of those representations.

Deep learning has also proven useful in Sentiment Analysis allowing for greater understanding of what parts of sentences have differing meanings. All leading to better analysis of the sentence as a whole.

Dialogue Agents and Machine Translation

Deep learning is also revolutionizing dialogue agents and chatbots. Recent advancements in speech recognition, driven by neural networks, have been remarkable, but the challenge now is to achieve equally good natural language understanding.

Machine Translation (MT), has also greatly benefited from deep learning, with endtoend trained deep learning systems (neural machine translation) producing significant improvements in translation quality. These systems often employ recurrent neural networks to read the source sentence and generate the translation.

It's All Vectors

The key takeaway is that deep learning in NLP relies heavily on representing language elements (sounds, words, sentences, conversations) as realvalued vectors. While this may seem simplistic, these vectors are incredibly flexible data structures with significant representational capacity, as demonstrated by deep learning systems.