nltk-linguistics
NLP and corpus analysis via NLTK. Use when: user asks about tokenization, POS tagging, parsing, sentiment, or corpus statistics. NOT for: deep learning NLP or transformer models.
Best use case
nltk-linguistics is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
NLP and corpus analysis via NLTK. Use when: user asks about tokenization, POS tagging, parsing, sentiment, or corpus statistics. NOT for: deep learning NLP or transformer models.
Teams using nltk-linguistics should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/nltk-linguistics/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How nltk-linguistics Compares
| Feature / Agent | nltk-linguistics | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
NLP and corpus analysis via NLTK. Use when: user asks about tokenization, POS tagging, parsing, sentiment, or corpus statistics. NOT for: deep learning NLP or transformer models.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# NLTK Linguistics
Natural language processing and corpus analysis using NLTK.
## Setup
```python
import nltk
for pkg in ['punkt_tab', 'averaged_perceptron_tagger_eng', 'maxent_ne_chunker_tab',
'words', 'vader_lexicon', 'wordnet', 'stopwords']:
nltk.download(pkg, quiet=True)
```
## Tokenization
```python
from nltk.tokenize import word_tokenize, sent_tokenize
sentences = sent_tokenize(text)
words = word_tokenize(text)
```
## POS Tagging
```python
from nltk import pos_tag
from nltk.tokenize import word_tokenize
tagged = pos_tag(word_tokenize(text)) # list of (word, tag) tuples
# Tags: NN=noun, VB=verb, JJ=adjective, RB=adverb, DT=determiner
```
## Named Entity Recognition
```python
from nltk import ne_chunk, pos_tag, word_tokenize
tree = ne_chunk(pos_tag(word_tokenize(text)))
for subtree in tree:
if hasattr(subtree, 'label'):
entity = " ".join(word for word, tag in subtree.leaves())
print(f"{subtree.label()}: {entity}")
```
## Sentiment Analysis (VADER)
```python
from nltk.sentiment.vader import SentimentIntensityAnalyzer
sia = SentimentIntensityAnalyzer()
scores = sia.polarity_scores(text)
# Returns: {'neg': 0.0, 'neu': 0.5, 'pos': 0.5, 'compound': 0.6369}
# compound: -1 (most negative) to +1 (most positive)
```
## Frequency Distributions and Concordance
```python
from nltk import FreqDist, Text
from nltk.tokenize import word_tokenize
fdist = FreqDist(word_tokenize(text.lower()))
fdist.most_common(20) # top 20 words
t = Text(word_tokenize(text))
t.concordance('language', width=80) # keyword-in-context
t.collocations() # frequent bigrams
```
## WordNet Lookups
```python
from nltk.corpus import wordnet as wn
synsets = wn.synsets('bank') # all senses
defn = synsets[0].definition() # definition string
sim = wn.synset('dog.n.01').wup_similarity(wn.synset('cat.n.01')) # Wu-Palmer similarity
synonyms = [l.name() for s in wn.synsets('good') for l in s.lemmas()]
hypernyms = wn.synset('dog.n.01').hypernyms()
```
## Stopword Filtering
```python
from nltk.corpus import stopwords
stop_words = set(stopwords.words('english'))
filtered = [w for w in tokens if w.lower() not in stop_words]
```
## Best Practices
1. Always download required NLTK data before first use.
2. Use `word_tokenize` over `split()` for proper tokenization.
3. VADER works best on short social-media-style text.
4. For large corpora, consider streaming with `PlaintextCorpusReader`.
5. POS tag sets: use `nltk.help.upenn_tagset()` for tag reference.
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