2nd/10_Wiki/Topics/AI_and_ML/Computational-Linguistics.md

---
id: wiki-2026-0508-computational-linguistics
title: Computational Linguistics
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [computational linguistics, NLP roots, syntax, semantics, pragmatics, formal grammar, Chomsky]
duplicate_of: none
source_trust_level: A
confidence_score: 0.88
verification_status: applied
tags: [linguistics, nlp, syntax, semantics, parsing, llm, chomsky, formal-grammar]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: Python
  framework: spaCy / NLTK / Stanza / Transformers
---

# Computational Linguistics

## 매 한 줄
> **"매 language 의 mathematical model"**. NLP 의 academic 의 root. 매 syntax + semantics + pragmatics + 매 morphology + phonology. 매 modern: 매 LLM 가 dominant 가, 매 linguistics 의 understanding 의 still relevant (eval, hallucination, multilingual).

## 매 핵심 layer

### Phonology / Phonetics
- 매 sound system.
- 매 IPA, 매 phoneme.

### Morphology
- 매 word structure.
- 매 inflection, derivation.
- 매 agglutinative (Korean, Turkish) vs analytic (Mandarin).

### Syntax
- 매 sentence structure.
- 매 parser, grammar.

### Semantics
- 매 meaning.
- 매 word sense, predicate-argument.

### Pragmatics
- 매 context, intent.
- 매 implicature, speech act.

### Discourse
- 매 multi-sentence, coherence.

### Sociolinguistics
- 매 register, dialect.

## 매 method history

### Symbolic / Rule-based (1950s-80s)
- Chomsky transformational grammar.
- HPSG, LFG, CCG.
- Expert system.

### Statistical (1990s-2010s)
- Hidden Markov Model (POS).
- PCFG (probabilistic CFG).
- IBM machine translation.
- BLEU metric.

### Neural (2010s-2020s)
- Word2Vec, GloVe.
- LSTM seq2seq.
- BERT, GPT.

### LLM (2022+)
- 매 implicit linguistics knowledge.
- 매 emergent.
- 매 multilingual zero-shot.

### 매 task
- **POS tagging**: noun, verb, ...
- **Parsing**: dependency, constituent.
- **NER**: named entity.
- **Coreference resolution**.
- **Word Sense Disambiguation**.
- **Machine Translation**.
- **Sentiment**.
- **Summarization**.
- **QA**.
- **Dialogue**.

### 매 modern relevance
- **LLM eval**: 매 specific linguistic phenomenon (BLiMP).
- **Multilingual NLP**: 매 typology-aware.
- **Hallucination analysis**: 매 syntax / semantics 의 mismatch.
- **Low-resource language**.
- **Code-switching**.

### 매 famous resource
- **WordNet**: 매 lexical database.
- **FrameNet**: 매 semantic frames.
- **PropBank** / **Penn Treebank**.
- **Universal Dependencies**.
- **CommonCrawl** + **OSCAR**.

## 💻 패턴

### POS tagging (spaCy)
```python
import spacy

nlp = spacy.load('en_core_web_sm')
doc = nlp('The quick brown fox jumps over the lazy dog')
for token in doc:
    print(f'{token.text:<10} {token.pos_:<10} {token.tag_}')
```

### Dependency parsing
```python
doc = nlp('Apple is looking at buying U.K. startup for $1 billion')
for token in doc:
    print(f'{token.text:<15} {token.dep_:<10} → {token.head.text}')

# 매 visualize
spacy.displacy.serve(doc, style='dep')
```

### NER
```python
import spacy
nlp = spacy.load('en_core_web_trf')  # 매 transformer-based
doc = nlp('Apple is looking at buying U.K. startup for $1 billion in 2024')
for ent in doc.ents:
    print(f'{ent.text}: {ent.label_}')
# Apple: ORG, U.K.: GPE, $1 billion: MONEY, 2024: DATE
```

### Universal Dependencies (Stanza)
```python
import stanza
nlp = stanza.Pipeline('en', processors='tokenize,pos,lemma,depparse')
doc = nlp('I drove to Berlin yesterday.')
for sent in doc.sentences:
    for w in sent.words:
        print(f'{w.text:<10} {w.upos:<8} → {sent.words[w.head-1].text if w.head > 0 else "ROOT"}')
```

### Constituency parsing (benepar)
```python
import benepar, spacy
nlp = spacy.load('en_core_web_md')
nlp.add_pipe('benepar', config={'model': 'benepar_en3'})
doc = nlp('The quick brown fox jumps over the lazy dog.')
for sent in doc.sents:
    print(sent._.parse_string)
# (S (NP (DT The) (JJ quick) (JJ brown) (NN fox)) (VP (VBZ jumps) ...))
```

### Word sense disambiguation
```python
from nltk.corpus import wordnet
from nltk.wsd import lesk

context = 'I went to the bank to deposit money'
sense = lesk(context.split(), 'bank')
print(sense)        # Synset('depository_financial_institution.n.01')
print(sense.definition())
```

### LLM 의 linguistic eval (BLiMP)
```python
# 매 BLiMP: 매 67 minimal pair phenomenon
def blimp_score(model, blimp_examples):
    correct = 0
    for ex in blimp_examples:
        ll_good = model.score(ex.acceptable_sentence)
        ll_bad = model.score(ex.unacceptable_sentence)
        if ll_good > ll_bad: correct += 1
    return correct / len(blimp_examples)
```

### Multilingual (XLM-R)
```python
from transformers import pipeline
pipe = pipeline('fill-mask', model='xlm-roberta-large')

# 매 zero-shot multilingual
print(pipe('Hello, my name is <mask>.'))
print(pipe('Bonjour, je m\'appelle <mask>.'))
print(pipe('안녕하세요, 제 이름은 <mask>입니다.'))
```

### Code-switching detection
```python
def detect_codeswitch(text, langid_model):
    """매 sentence 의 multiple language 의 detect."""
    tokens = text.split()
    langs = [langid_model.predict(t) for t in tokens]
    unique_langs = set(langs)
    if len(unique_langs) > 1:
        return f'Code-switching: {unique_langs}'
    return None
```

### Linguistic feature extraction (Korean morphology)
```python
from konlpy.tag import Mecab
mecab = Mecab()

text = '나는 학교에 갔다'
print(mecab.pos(text))
# [('나', 'NP'), ('는', 'JX'), ('학교', 'NNG'), ('에', 'JKB'), ('가', 'VV'), ('았', 'EP'), ('다', 'EF')]
```

### Hallucination via syntactic check
```python
def syntactic_consistency_check(generated, source_facts):
    """매 LLM 의 generated 의 매 source 의 entity 의 match?"""
    gen_doc = nlp(generated)
    gen_entities = {(ent.text, ent.label_) for ent in gen_doc.ents}
    
    source_entities = extract_entities(source_facts)
    
    invented = gen_entities - source_entities
    if invented:
        return f'Possible hallucination: {invented}'
    return None
```

## 🤔 결정 기준
| 응용 | Tool |
|---|---|
| Production NLP | spaCy / Stanza |
| Korean | Mecab / KoNLPy |
| State-of-art | Transformers (HF) |
| Linguistic phenomenon eval | BLiMP / SuperGLUE |
| Multilingual | XLM-R / mBERT |
| Low-resource | Parameter-efficient FT |
| Discourse | Coref + LLM |
| Hallucination | NER + cross-check |

**기본값**: spaCy (production) + Transformers (SOTA).

## 🔗 Graph
- 부모: [[NLP]] · [[AI]]
- 변형: [[Syntax]] · [[Semantics]] · [[Pragmatics]]
- 응용: [[Transformer_Architecture_and_LLM_Foundations|BERT]] · [[Transformer_Architecture_and_LLM_Foundations|LLM]] · [[Bag of Words (BoW)]] · [[CLIP]]
- Adjacent: [[Articulateness]] · [[Bayesian-Brain-Hypothesis]] · [[Beckett]] (literature)

## 🤖 LLM 활용
**언제**: 매 NLP system 설계. 매 LLM eval 의 linguistic 측. 매 multilingual product. 매 hallucination analysis.
**언제 X**: 매 simple text task (LLM 의 enough).

## ❌ 안티패턴
- **English-only assumption**: 매 multilingual fail.
- **No morphology** (agglutinative): 매 Korean / Turkish / Finnish 의 fail.
- **Statistical era 의 stuck**: 매 LLM 의 leverage X.
- **LLM 의 alone (no linguistic eval)**: 매 specific phenomenon 의 miss.

## 🧪 검증 / 중복
- Verified (Jurafsky-Martin "Speech and Language Processing", Manning Stanford NLP).
- 신뢰도 A.
- Related: [[NLP]] · [[Transformer_Architecture_and_LLM_Foundations|BERT]] · [[Bag of Words (BoW)]] · [[Articulateness]] · [[Bayesian-Brain-Hypothesis]].

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — layer + history + 매 spaCy / Stanza / BLiMP / XLM-R / Korean code |