Natural Language Understanding (NLU) is a subset of NLP that enables computers to comprehend human language. It bridges the gap between machines and humans. Since machines can only understand binary code (0s and 1s), NLU is the core technology that processes human language input extracts its meaning, and provides meaningful insights.
NLU is the foundation for many advanced AI applications, such as chatbots, voice assistants, sentiment analysis, and machine translation. It allows systems to parse sentences and understand the context, recognize entities, and resolve ambiguities inherent in human language. The ultimate goal is to build systems that interact with humans as naturally and intelligently as possible.
History of Natural Language Understanding
The history of Natural Language Understanding (NLU) is a fascinating journey through computational linguistics, artificial intelligence (AI), and cognitive science
STUDENT (1964)
STUDENT Program was designed to demonstrate natural language understanding. Bobrow's program allowed a computer to receive a problem described in natural language, such as "John has 3 apples and Mary has 4 apples. How many apples do they have together?" and solve it mathematically.
ELIZA (1965)
ELIZA was the first experiments in computational linguistics and simulated human-like conversations, marking an early point where students in AI programs began learning the possibilities of human-computer interactions.
Conceptual Dependency Theory (1969)
The theory focused on how to represent the meaning of sentences based on the relationships between actions, objects, and participants. Schank's approach was important because it shifted the focus from syntax (sentence structure) to semantics (meaning), emphasizing that understanding language required more than just parsing grammatical forms.
Augmented Transition Networks (1970)
Augmented Transition Networks (ATNs) was an early computational model used to represent natural language input. They utilized recursive finite-state automata to handle language processing. These networks allowed for more flexible and dynamic handling of linguistic structures and continued to be a key tool in NLU research for several years.
SHRDLU (1971)
Terry Winograd’s SHRDLU demonstrated that computers could understand and respond to commands given in natural language within a limited environment, such as moving blocks in a virtual world. This represented an early step toward applying formal linguistic models to computational problems.
Expert Systems (1980s)
Expert systems applied rule-based reasoning to domains such as medical diagnosis and technical support. These systems relied on large sets of rules and knowledge bases to infer conclusions from natural language input. Though they were successful in specialized domains, these systems struggled with the complexities of open-ended language understanding.
Machine Learning and IBM Watson (2000s)
The early 2000s saw the introduction of machine learning techniques for natural language processing. This shift allowed systems to learn from large datasets rather than relying solely on predefined rules.
In 2011, IBM’s Watson became famous for defeating human champions on the quiz show Jeopardy!, demonstrating the power of machine learning. However, there was considerable debate about whether Watson truly understood the questions and answers it processed, as John Searle and other experts argued that the system lacked true comprehension of the language it used.
Working of NLU
Consider a sample text : 'A new mobile will be launched in the upcoming year'
1. Text Processing
Text processing is the first step in NLU after receiving the input. This step involves several operations:
- Tokenization: Breaking the sentence into individual words.
- Stopword Removal: Removing common words like “the”, “is”, etc., that don’t add significant meaning.
- Punctuation Removal: Removing punctuation marks like commas, periods, etc.
- Stemming and Lemmatization: Reducing words to their base form (e.g., “launching” becomes “launch”).
After processing, the text is transformed into a list of relevant words:
["new", "mobile", "will", "be", "launch", "upcoming", "year"]
2. Parts of Speech Tagging
Once the text is tokenized, Parts of Speech (POS) tagging is applied. POS tagging assigns grammatical categories to words, such as verbs, adjectives, nouns, and prepositions.
For our example:
- new → Adjective (ADJ)
- mobile → Noun (NOUN)
- will → Modal verb (AUX)
- be → Auxiliary verb (AUX)
- launched → Verb (VERB)
- upcoming → Adjective (ADJ)
- year → Noun (NOUN)
3. Named Entity Recognition
Named Entity Recognition (NER) helps identify and extract key entities from the text, which can be either numerical or categorical.
In our example:
- mobile → Entity (Object/Device)
- upcoming year → Entity (Time/Date)
These entities are essential for understanding the context of the sentence.
4. Identifying Dependencies
Dependency parsing is used to identify how words are related to each other in the sentence. It helps to establish which words depend on others to form meaningful phrases.
For example, in our sentence:
- “mobile” is dependent on “launch”
- “upcoming year” is the time frame for the action
NLU systems use this information to understand the relationships between different parts of the sentence.
5. Solving Word Ambiguities
Many words in the English language have multiple meanings based on context. NLU systems resolve these ambiguities by analyzing the context of the sentence to select the correct meaning.
In our example:
- Mobile can refer to:
- A smartphone
- A moving object
By analyzing the context, the NLU system determines that “mobile” in this case refers to a smartphone.
6. Analyzing the Intent
NLU systems, especially those used in chatbots, are designed to identify the intent behind user input. The system tries to understand the purpose or the emotion conveyed in the text. In this case, the intent is to inform the user about an upcoming smartphone launch.
The machine processes the text to recognize the intention behind the sentence and extracts the meaningful content from it.
7. Output Generation
After processing and understanding the deeper meaning of the text, the machine generates an appropriate response. Based on the context and intent, the system might respond with something like:
"Yes, a new smartphone will be launched in the upcoming year. Can you specify the brand so I can provide further information?"
This output helps maintain a fluid conversation and provide relevant responses.
8. Understanding the Context
Context is crucial for meaningful interaction in NLU. A chatbot, for example, needs to incorporate previous interactions to ensure continuity in the conversation. This allows the bot to provide appropriate responses based on the prior context.
By understanding the user’s history and preferences, the NLU system is able to engage in more natural and contextually aware conversations.
Models and Techniques Used in NLU
1. Transformers use an attention mechanism to analyze word relationships, regardless of their distance in the text. Notable models include:
- BERT: Uses bidirectional context for better understanding.
- T5: Treats all tasks as text-to-text problems.
- GPT: Focuses on generating coherent text for conversational AI.
2. Recurrent Neural Networks (RNNs) process text sequentially, retaining context from previous words. Key variants include:
- LSTM: Handles long-range dependencies in sequences.
- GRU: A simpler version of LSTM with fewer parameters.
3. Word Embeddings represent words as vectors in a high-dimensional space, capturing semantic relationships. Popular models include:
- Word2Vec: Learns word meanings based on context.
- GloVe: Uses word co-occurrence to generate embeddings.
4. Rule-Based Systems rely on predefined rules to extract information based on logical conditions. They are useful for structured tasks like information retrieval.
5. Conditional Random Fields (CRFs) are probabilistic models used for sequence labeling tasks like named entity recognition (NER) and part-of-speech tagging, where context is crucial.
NLU vs. NLP vs. NLG
| Aspect | Natural Language Understanding (NLU) | Natural Language Processing (NLP) | Natural Language Generation (NLG) |
|---|
| Definition | Understanding the intent of the text | NLP encompasses both NLU and NLG | Responsible for generating human-like language |
|---|
| Focus | Interpretation and extraction of meaning | Processing, interpreting, and generating text | Generation of text or content from structured data |
|---|
| Use Case | Sentiment analysis, intent detection, named entity recognition | Stemming, tokenization, part-of-speech tagging, and other linguistic tasks | Used for replies, writing summaries, text generation, etc. |
|---|
| Technologies | NLU, spaCy, Hugging Face Transformers | NLTK, spaCy, CoreNLP, Hugging Face Transformers, OpenAI models | GPT, OpenAI API, T5, BERT, etc. |
|---|
Applications of NLU
- Chatbots and Virtual Assistants: NLU enables conversational AI systems like Siri, Alexa, and Google Assistant to understand user requests and provide relevant responses.
- Machine Translation: NLU helps systems like Google Translate to not just translate words, but also understand context for accurate translations.
- Search Engines: NLU allows search engines to interpret queries and deliver more relevant results by understanding user intent.
- Content Moderation: Social media platforms use NLU to analyze and flag harmful or inappropriate content in posts and comments.
- Healthcare: NLU assists in analyzing clinical text, such as doctor’s notes and patient records, to aid in diagnosis and treatment suggestions.