computational-linguistics

What I do

• Apply linguistic theory to computation
• Build language models with linguistic structure
• Implement syntactic and semantic analysis
• Create cross-linguistic NLP systems
• Handle multilingual text processing

When to use me

Use me when:

• Building linguistically-aware NLP
• Multilingual applications
• Grammar and syntax processing
• Semantic representation

Key Concepts

Linguistic Analysis Levels

┌─────────────────────────────────────────────┐
│              Discourse                      │
│         (context, coherence)                │
├─────────────────────────────────────────────┤
│               Semantics                     │
│     (meaning, representation)               │
├─────────────────────────────────────────────┤
│             Pragmatics                      │
│    (intent, context, speech acts)           │
├─────────────────────────────────────────────┤
│              Syntax                         │
│      (grammar, structure rules)            │
├─────────────────────────────────────────────┤
│               Morphology                    │
│       (word formation, inflections)        │
├─────────────────────────────────────────────┤
│             Phonology/Orthography           │
│           (sounds, written form)           │
└─────────────────────────────────────────────┘

Syntax Parsing

import spacy

nlp = spacy.load("en_core_web_sm")
doc = nlp("The cat sat on the mat.")

# Part-of-speech tags
for token in doc:
    print(token.text, token.pos_, token.tag_)

# Dependency parsing
for token in doc:
    print(token.text, token.dep_, token.head.text)

# Named entities
for ent in doc.ents:
    print(ent.text, ent.label_)

Semantic Representation

• Formal Semantics: Lambda calculus, FOL
• Frame Semantics: FrameNet
• Semantic Primitives: Primitive theory
• Distributional Semantics: Word embeddings

Linguistic Resources

• WordNet: Lexical database
• UD: Universal Dependencies
• Penn Treebank: Syntactic annotations
• BabelNet: Multilingual lexical knowledge