Nltk Linguistics
- 17 installs
- 869 repo stars
- Updated June 8, 2026
- beita6969/scienceclaw
nltk-linguistics is a skill for NLP and corpus analysis with NLTK, covering tokenization, POS tagging, NER, sentiment, and WordNet lookups.
About
This skill covers classic NLP and corpus analysis with NLTK in Python. A developer uses it for tokenization, part-of-speech tagging, named entity recognition, VADER sentiment, frequency distributions, concordance, and WordNet lookups. It states it is not for deep learning or transformer models. It matters for lightweight linguistic analysis without heavy dependencies.
- Tokenization, POS tagging, and NLTK named entity recognition
- VADER sentiment, frequency distributions, concordance, and collocations
- WordNet lookups for definitions, synonyms, and similarity
Nltk Linguistics by the numbers
- 17 all-time installs (skills.sh)
- Ranked #1,286 of 2,065 Data Science & ML skills by installs in the Skillselion catalog
- Data as of Aug 2, 2026 (Skillselion catalog sync)
nltk-linguistics capabilities & compatibility
Free; installs nltk via uv and downloads NLTK data.
- Capabilities
- data analysis · research
- Use cases
- data analysis · research
- Pricing
- Free
What nltk-linguistics says it does
NLP and corpus analysis via NLTK.
VADER works best on short social-media-style text.
NOT for: deep learning NLP or transformer models.
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| Installs | 17 |
|---|---|
| repo stars | ★ 869 |
| Last updated | June 8, 2026 |
| Repository | beita6969/scienceclaw ↗ |
What it does
Do tokenization, POS tagging, NER, sentiment, and corpus statistics in Python with NLTK.
Who is it for?
Classic linguistic analysis: tokenization, POS tagging, NER, VADER sentiment, and corpus statistics in Python.
Skip if: Deep learning NLP or transformer models, which the docs exclude.
When should I use this skill?
You need tokenization, POS tagging, parsing, sentiment, or corpus statistics.
What you get
The developer gets tokens, POS tags, entities, sentiment scores, and WordNet relations.
By the numbers
- 7 NLTK data packages to download in setup
Files
NLTK Linguistics
Natural language processing and corpus analysis using NLTK.
Setup
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
from nltk.tokenize import word_tokenize, sent_tokenize
sentences = sent_tokenize(text)
words = word_tokenize(text)POS Tagging
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=determinerNamed Entity Recognition
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)
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
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 bigramsWordNet Lookups
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
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. 6. WordNet similarity requires both synsets to share a common hypernym.
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What is this not for?
Deep learning NLP or transformer models; it targets classic NLTK-based analysis.
Does VADER work best on any particular text?
Yes, VADER works best on short social-media-style text.