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Antigravity Agent
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id: wiki-2026-0508-computational-linguistics
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title: Computational Linguistics
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [computational linguistics, NLP roots, syntax, semantics, pragmatics, formal grammar, Chomsky]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.88
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verification_status: applied
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tags: [linguistics, nlp, syntax, semantics, parsing, llm, chomsky, formal-grammar]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: Python
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framework: spaCy / NLTK / Stanza / Transformers
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---
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# Computational Linguistics
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## 매 한 줄
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> **"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).
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## 매 핵심 layer
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### Phonology / Phonetics
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- 매 sound system.
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- 매 IPA, 매 phoneme.
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### Morphology
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- 매 word structure.
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- 매 inflection, derivation.
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- 매 agglutinative (Korean, Turkish) vs analytic (Mandarin).
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### Syntax
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- 매 sentence structure.
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- 매 parser, grammar.
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### Semantics
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- 매 meaning.
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- 매 word sense, predicate-argument.
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### Pragmatics
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- 매 context, intent.
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- 매 implicature, speech act.
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### Discourse
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- 매 multi-sentence, coherence.
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### Sociolinguistics
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- 매 register, dialect.
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## 매 method history
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### Symbolic / Rule-based (1950s-80s)
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- Chomsky transformational grammar.
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- HPSG, LFG, CCG.
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- Expert system.
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### Statistical (1990s-2010s)
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- Hidden Markov Model (POS).
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- PCFG (probabilistic CFG).
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- IBM machine translation.
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- BLEU metric.
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### Neural (2010s-2020s)
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- Word2Vec, GloVe.
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- LSTM seq2seq.
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- BERT, GPT.
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### LLM (2022+)
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- 매 implicit linguistics knowledge.
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- 매 emergent.
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- 매 multilingual zero-shot.
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### 매 task
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- **POS tagging**: noun, verb, ...
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- **Parsing**: dependency, constituent.
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- **NER**: named entity.
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- **Coreference resolution**.
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- **Word Sense Disambiguation**.
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- **Machine Translation**.
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- **Sentiment**.
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- **Summarization**.
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- **QA**.
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- **Dialogue**.
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### 매 modern relevance
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- **LLM eval**: 매 specific linguistic phenomenon (BLiMP).
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- **Multilingual NLP**: 매 typology-aware.
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- **Hallucination analysis**: 매 syntax / semantics 의 mismatch.
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- **Low-resource language**.
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- **Code-switching**.
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### 매 famous resource
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- **WordNet**: 매 lexical database.
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- **FrameNet**: 매 semantic frames.
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- **PropBank** / **Penn Treebank**.
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- **Universal Dependencies**.
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- **CommonCrawl** + **OSCAR**.
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## 💻 패턴
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### POS tagging (spaCy)
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```python
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import spacy
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nlp = spacy.load('en_core_web_sm')
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doc = nlp('The quick brown fox jumps over the lazy dog')
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for token in doc:
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print(f'{token.text:<10} {token.pos_:<10} {token.tag_}')
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```
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### Dependency parsing
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```python
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doc = nlp('Apple is looking at buying U.K. startup for $1 billion')
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for token in doc:
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print(f'{token.text:<15} {token.dep_:<10} → {token.head.text}')
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# 매 visualize
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spacy.displacy.serve(doc, style='dep')
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```
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### NER
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```python
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import spacy
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nlp = spacy.load('en_core_web_trf') # 매 transformer-based
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doc = nlp('Apple is looking at buying U.K. startup for $1 billion in 2024')
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for ent in doc.ents:
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print(f'{ent.text}: {ent.label_}')
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# Apple: ORG, U.K.: GPE, $1 billion: MONEY, 2024: DATE
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```
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### Universal Dependencies (Stanza)
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```python
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import stanza
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nlp = stanza.Pipeline('en', processors='tokenize,pos,lemma,depparse')
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doc = nlp('I drove to Berlin yesterday.')
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for sent in doc.sentences:
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for w in sent.words:
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print(f'{w.text:<10} {w.upos:<8} → {sent.words[w.head-1].text if w.head > 0 else "ROOT"}')
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```
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### Constituency parsing (benepar)
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```python
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import benepar, spacy
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nlp = spacy.load('en_core_web_md')
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nlp.add_pipe('benepar', config={'model': 'benepar_en3'})
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doc = nlp('The quick brown fox jumps over the lazy dog.')
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for sent in doc.sents:
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print(sent._.parse_string)
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# (S (NP (DT The) (JJ quick) (JJ brown) (NN fox)) (VP (VBZ jumps) ...))
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```
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### Word sense disambiguation
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```python
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from nltk.corpus import wordnet
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from nltk.wsd import lesk
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context = 'I went to the bank to deposit money'
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sense = lesk(context.split(), 'bank')
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print(sense) # Synset('depository_financial_institution.n.01')
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print(sense.definition())
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```
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### LLM 의 linguistic eval (BLiMP)
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```python
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# 매 BLiMP: 매 67 minimal pair phenomenon
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def blimp_score(model, blimp_examples):
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correct = 0
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for ex in blimp_examples:
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ll_good = model.score(ex.acceptable_sentence)
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ll_bad = model.score(ex.unacceptable_sentence)
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if ll_good > ll_bad: correct += 1
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return correct / len(blimp_examples)
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```
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### Multilingual (XLM-R)
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```python
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from transformers import pipeline
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pipe = pipeline('fill-mask', model='xlm-roberta-large')
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# 매 zero-shot multilingual
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print(pipe('Hello, my name is <mask>.'))
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print(pipe('Bonjour, je m\'appelle <mask>.'))
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print(pipe('안녕하세요, 제 이름은 <mask>입니다.'))
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```
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### Code-switching detection
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```python
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def detect_codeswitch(text, langid_model):
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"""매 sentence 의 multiple language 의 detect."""
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tokens = text.split()
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langs = [langid_model.predict(t) for t in tokens]
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unique_langs = set(langs)
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if len(unique_langs) > 1:
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return f'Code-switching: {unique_langs}'
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return None
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```
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### Linguistic feature extraction (Korean morphology)
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```python
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from konlpy.tag import Mecab
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mecab = Mecab()
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text = '나는 학교에 갔다'
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print(mecab.pos(text))
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# [('나', 'NP'), ('는', 'JX'), ('학교', 'NNG'), ('에', 'JKB'), ('가', 'VV'), ('았', 'EP'), ('다', 'EF')]
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```
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### Hallucination via syntactic check
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```python
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def syntactic_consistency_check(generated, source_facts):
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"""매 LLM 의 generated 의 매 source 의 entity 의 match?"""
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gen_doc = nlp(generated)
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gen_entities = {(ent.text, ent.label_) for ent in gen_doc.ents}
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source_entities = extract_entities(source_facts)
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invented = gen_entities - source_entities
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if invented:
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return f'Possible hallucination: {invented}'
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return None
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```
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## 🤔 결정 기준
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| 응용 | Tool |
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| Production NLP | spaCy / Stanza |
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| Korean | Mecab / KoNLPy |
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| State-of-art | Transformers (HF) |
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| Linguistic phenomenon eval | BLiMP / SuperGLUE |
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| Multilingual | XLM-R / mBERT |
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| Low-resource | Parameter-efficient FT |
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| Discourse | Coref + LLM |
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| Hallucination | NER + cross-check |
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**기본값**: spaCy (production) + Transformers (SOTA).
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## 🔗 Graph
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- 부모: [[NLP]] · [[AI]]
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- 변형: [[Syntax]] · [[Semantics]] · [[Pragmatics]]
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- 응용: [[Transformer_Architecture_and_LLM_Foundations|BERT]] · [[Transformer_Architecture_and_LLM_Foundations|LLM]] · [[Bag of Words (BoW)]] · [[CLIP]]
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- Adjacent: [[Articulateness]] · [[Bayesian-Brain-Hypothesis]] · [[Beckett]] (literature)
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## 🤖 LLM 활용
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**언제**: 매 NLP system 설계. 매 LLM eval 의 linguistic 측. 매 multilingual product. 매 hallucination analysis.
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**언제 X**: 매 simple text task (LLM 의 enough).
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## ❌ 안티패턴
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- **English-only assumption**: 매 multilingual fail.
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- **No morphology** (agglutinative): 매 Korean / Turkish / Finnish 의 fail.
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- **Statistical era 의 stuck**: 매 LLM 의 leverage X.
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- **LLM 의 alone (no linguistic eval)**: 매 specific phenomenon 의 miss.
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## 🧪 검증 / 중복
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- Verified (Jurafsky-Martin "Speech and Language Processing", Manning Stanford NLP).
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- 신뢰도 A.
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- Related: [[NLP]] · [[Transformer_Architecture_and_LLM_Foundations|BERT]] · [[Bag of Words (BoW)]] · [[Articulateness]] · [[Bayesian-Brain-Hypothesis]].
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## 🕓 Changelog
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| 날짜 | 변경 |
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — layer + history + 매 spaCy / Stanza / BLiMP / XLM-R / Korean code |
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