Foundations of NLP
- (The University of Chicago - Alvin Wei-Cheng Wong)
- Overview
Natural language processing (NLP) uses linguistics and mathematics to bridge human language and computer language. Natural language generally comes in two forms: text or speech. Through NLP algorithms, these natural forms of communication are broken down into data that machines can understand.
There are many complexities in working with natural language, especially for humans who are not used to adapting speech to algorithms. While we can create programs with the rules of speech and written text, humans don't always follow these rules. Linguistics is the study of official and unofficial rules of language.
The problem with using formal linguistics to create NLP models is that the rules of any language are complex. The rules of language themselves often pose problems when converted into formal mathematical rules. While the rules of language do a good job of defining how an ideal person in an ideal world would speak, human language is also full of shortcuts, inconsistencies, and errors.
- Computational Linguistics
Due to the limitations of formal linguistics, computational linguistics has become a growing field. Using large datasets, linguists can discover more about how human language works and use these findings to guide NLP.
This version of NLP, statistical NLP, has gradually become dominant in the field of natural language processing. Statistical NLP uses statistics derived from large amounts of data to bridge the gap between how language should be used and how it is actually used.
- Building Blocks and Process of NLP
Natural Language Processing (NLP) is a field of AI that enables computers to effectively understand, analyze, and interact with human language.
Foundations of NLP is the core concepts and building blocks of Natural Language Processing (NLP), which primarily involve techniques like tokenization, syntax analysis, semantic analysis, and discourse analysis, allowing computers to understand and process human language by breaking down text into meaningful components and interpreting their meaning within context.
The process of NLP can be divided into five different stages: lexical analysis, syntactic analysis, semantic analysis, discourse integration, and pragmatic analysis. Each stage plays a vital role in the overall understanding and processing of natural language.
- Text Becomes Insight with Vectorization
In Natural Language Processing (NLP), we convert text into vectors to enable machine learning (ML) models to understand and process textual data by representing words and sentences as numerical values, allowing them to perform calculations and identify patterns within the text, which is essential for tasks like sentiment analysis, text classification, and machine translation, as computers can only work with numbers, not raw text directly.
This process captures the semantic meaning of words and their relationships within a context, leading to more accurate and robust NLP applications.
Key features about text vectorization:
- Machine Learning Compatibility: ML models can only process numerical data, so converting text into vectors allows them to learn patterns and make predictions based on textual information.
- Semantic Understanding: Vectorization techniques like word embeddings capture the semantic relationships between words, meaning words with similar meanings will have similar vector representations.
- Dimensionality Reduction: By converting text into fixed-size vectors, we can manage large amounts of data and perform computations efficiently.
- Contextual Awareness: Advanced vectorization methods can incorporate contextual information, allowing models to better understand the nuances of language depending on the surrounding words.
Examples of text vectorization techniques:
- Bag-of-Words (BoW): Counts the frequency of each word in a document, ignoring word order.
- TF-IDF (Term Frequency-Inverse Document Frequency): Weights words based on their frequency within a document and their rarity across the corpus
- Word Embeddings (like Word2Vec): Represents words as vectors in a high-dimensional space, where similar words have similar vectors
- Text Preprocessing in NLP
One of the foundational steps in NLP is text preprocessing, which involves cleaning and preparing raw text data for further analysis or model training. Proper text preprocessing can significantly impact the performance and accuracy of NLP models.
In NLP, text preprocessing refers to the initial step of cleaning and transforming raw text data into a structured format by performing operations like tokenization, removing stop words, stemming, or lemmatization, which allows the NLP model to analyze the text more effectively; essentially, it prepares the text for further processing by removing unnecessary elements and standardizing the format.
- Tokenization: Breaking down text into individual words or units called "tokens".
- Lowercasing: Converting all text to lowercase.
- Stop word removal: Removing common words like "the," "a," and "is" that don't contribute significant meaning.
- Stemming: Reducing words to their root form (e.g., "walking" becomes "walk").
- Lemmatization: Finding the base form of a word based on its dictionary definition (considered more accurate than stemming).
Why is text preprocessing important?
- Improves accuracy: By removing irrelevant information, the NLP model can focus on the most important aspects of the text.
- Reduces computational complexity: Removing unnecessary data points can make processing faster.
- Standardizes data: Ensures that words are treated consistently regardless of their case or grammatical form.
