Language Analysis

"Ever wondered how computers understand human language? Natural Language Processing (NLP) unlocks this ability, but it dives deep into the intricate layers of language itself. This page explores the key levels of language analysis used in NLP. By understanding these levels, you gain insight into how NLP tackles the complexities of human language, paving the way for exciting applications like machine translation, chatbots, and sentiment analysis."- Gemini 2024

Language Building Blocks

Thinking about the building blocks of languages helps illustrate the levels of language analysis:

You first need the individual building blocks: words (lexical level)
Then you consider how they are formed (morphological level)
You arrange them according to grammar rules (syntactic level)
You understand the meaning of the individual words and the sentence itself (semantic level)
You consider the context and speaker's intent (pragmatic level)
Finally, you analyze how these sentences work together as a whole (discourse level)

Levels of Language Analysis

Level	Description
Lexical	The foundation – understanding individual words and their forms.
Morphological	Delving into word structure – how prefixes, suffixes, and roots combine.
Syntactic	Building sentences – arranging words according to grammar rules.
Semantic	Capturing meaning – understanding the sense of words and sentences.
Pragmatic	Considering context – interpreting language based on the situation and speaker's intent.
Discourse	Connecting the dots – analyzing how sentences flow together to form a coherent whole.

Level Details

Lexical

This level deals with the individual words (or tokens) in a sentence. NLP tasks include:

Tokenization - Breaking text into individual words or subwords
Stemming - reducing words to their base form
Lemmatization - reducing words to their root form

Morphological

This level focuses on the internal structure of words, analyzing how morphemes (meaningful units like prefixes, suffixes, and roots) are combined to form words. NLP techniques can identify morphemes and understand how they contribute to word meaning. NLP tasks include:

Morphological analysis - Analyzing word structure into morphemes.
Inflectional morphology - Identifying grammatical changes in words (e.g., tense, number, person).
Contributes to tagging - Word endings or affixes can provide clues about a word's part of speech (e.g., "-ly" often indicates an adverb. In some languages, morphological features like case or gender can influence POS assignment.

Syntactic

This level deals with how words are arranged to form phrases and sentences, focusing on grammar rules and sentence structure. NLP tasks include:

Parsing - Analyzing the grammatical structure of sentences.
Part-of-speech (POS) Tagging - Assigning grammatical categories to words (noun, verb, adjective, etc.). Morphological analysis also contributes too as sord endings or affixes can provide clues about a word's part of speech (e.g., "-ly" often indicates an adverb
Dependency parsing - Identifying grammatical relationships between words.

Semantic

This level focuses on the meaning of words and sentences, considering the relationship between language and the concepts it represents. NLP tasks include:

Word sense disambiguation - Determining the correct meaning of a word in context.
Sentiment analysis - Determining the emotional tone of a text.
Textual entailment - Determining if one sentence implies another.

Pragmatic

This level considers the context in which language is used, including the speaker's intention, the listener's background knowledge, and the overall situation. NLP tasks include:

Sarcasm detection: Identifying hidden meanings in text.
Metaphor interpretation: Understanding implied comparisons in language.
Speech act recognition - Identifying the intended action of a speaker

Discourse

This level focuses on how sentences are connected to form a coherent discourse, analyzing relationships between sentences and paragraphs in a larger context. NLP tasks include:

Text summarization - Creating concise summaries of longer texts.
Topic modeling - identifying main themes
Task: Question answering - Providing answers to questions based on given text.

When we think about the complexities of human languages, and the years humans put in to learning them, it's easy to understand the challenges of a machines learning a human language. Where we are immersed in spoken language from birth and have visual, auditory clues to help with understanding, a machine relies on text-based data and lacks the benefit of a rich sensory experience.

"As we delve deeper into the intricacies of language, it becomes evident that while machines have made remarkable strides, fully replicating human linguistic comprehension remains a complex challenge. The journey towards artificial intelligence that truly understands and responds to language is still in its infancy." - Gemini 2024