jate 3.0.0

Just Automatic Term Extraction — the definitive Python library for automatic term extraction

JATE — Just Automatic Term Extraction

A Python library for automatic term extraction (ATE) from text corpora. JATE provides 14 ATE algorithms (13 classical + ensemble voting), corpus-level statistics, built-in evaluation, and a CLI — all pip-installable with no external services required.

Previously known as "Java Automatic Term Extraction" (84+ stars). The original Java/Solr library is preserved on the legacy/java branch.

Installation

pip install jate

Or from source:

git clone https://github.com/ziqizhang/jate.git
cd jate
pip install .

Requires Python 3.11+ and a spaCy model:

python -m spacy download en_core_web_sm

Quick start

Single document

import jate

# Extract terms from text (default: C-Value + POS pattern extraction)
result = jate.extract("Your document text here...")

for term in result:
    print(f"{term.string:30s}  score={term.score:.4f}  surfaces={term.surface_forms}")

Corpus-level extraction

import jate

# From a list of texts
result = jate.extract_corpus(
    ["First document...", "Second document..."],
    algorithm="tfidf",
)

# From a directory of text files
result = jate.extract_corpus("path/to/corpus/", algorithm="cvalue")

# Export results
df = result.to_dataframe()
print(result.to_csv())

Compare algorithms

import jate

results = jate.compare(
    ["Doc one...", "Doc two..."],
    algorithms=["cvalue", "tfidf", "rake", "weirdness"],
)

for algo_name, result in results.items():
    print(f"\n{algo_name}: {len(result)} terms")
    for term in list(result)[:5]:
        print(f"  {term.string:30s}  {term.score:.4f}")

For large corpora, speed up with parallel processing:

config = jate.JATEConfig(max_workers=4)
results = jate.compare(docs, algorithms=["cvalue", "tfidf", "rake"], config=config)

Evaluation against a gold standard

import jate

result = jate.extract_corpus(docs, algorithm="cvalue")

evaluator = jate.Evaluator(gold_terms={"machine learning", "neural network", ...})
eval_result = evaluator.evaluate(result)
print(eval_result.summary())
# P=0.2800  R=0.0644  F1=0.1047  TP=28  FP=72  FN=407  predicted=100  gold=435

# Evaluate top-k
eval_at_50 = evaluator.evaluate_at_k(result, k=50)

CLI

# Extract terms from text
jate extract "Your text here" --algorithm cvalue --top 20

# Extract from a corpus directory
jate corpus path/to/docs/ --algorithm tfidf --output csv

# Compare algorithms on a corpus
jate compare path/to/docs/ --algorithms cvalue tfidf rake

# Run benchmark on built-in dataset
jate benchmark --top 100

Algorithms

Algorithm	Description	Reference
tfidf	TF-IDF at corpus level	—
cvalue	Multi-word term extraction via nested term frequency	Frantzi et al. 2000
ncvalue	C-Value extended with context word information	Frantzi et al. 2000
basic	Frequency + containment scoring	Bordea et al. 2013
combobasic	Basic with parent and child containment	Bordea et al. 2013
attf	Average total term frequency (TTF / DF)	—
ttf	Raw total term frequency	—
ridf	Residual IDF (deviation from Poisson)	Church & Gale 1995
rake	Rapid Automatic Keyword Extraction	Rose et al. 2010
chi_square	Chi-square test for term independence	Matsuo & Ishizuka 2003
weirdness	Target vs reference corpus frequency ratio	Ahmad et al. 1999
termex	Domain pertinence + context + lexical cohesion	Sclano et al. 2007
glossex	Domain specificity via glossary comparison	Park et al. 2002
voting	Ensemble via reciprocal rank fusion	—

Candidate extractors

Extractor	Description
pos_pattern (default)	Regex over Universal POS tags (e.g. (ADJ )*(NOUN )+)
ngram	Contiguous token n-grams (configurable min/max n)
noun_phrase	spaCy noun chunk detection

How it works

1. Candidate extraction — identifies potential terms using POS patterns, n-grams, or noun phrases
2. Lemmatisation — normalises candidates to their lemmatised form (e.g. "neural networks" and "neural network" become one entry)
3. Sentence context (automatic) — builds sentence co-occurrence and adjacency features for algorithms that use them (Chi-Square, NC-Value)
4. Corpus statistics — builds frequency and co-occurrence counts (in-memory or SQLite-backed)
5. Scoring — applies the chosen algorithm to rank candidates
6. Output — returns TermExtractionResult with the normalised term, score, and all observed surface forms

Each Term in the result contains:

• string — the canonical (lemmatised) form, used for scoring and evaluation
• score — algorithm-assigned score
• frequency — total corpus frequency
• surface_forms — all surface variants observed (e.g. {"neural network", "neural networks", "Neural Networks"})

Roadmap

• spaCy pipeline integration — nlp.add_pipe("jate")
• Interactive web demo — Streamlit UI with HuggingFace Spaces deployment
• More benchmarks — ACTER, GENIA, CoastTerm, TermEval datasets
• Neural methods — BERT-based sequence labeling, embedding-based scoring
• LLM-augmented extraction — optional LLM re-ranking and validation
• Agentic pipeline — LangGraph-powered orchestration for automatic algorithm selection
• Multilingual support — works with any spaCy language model
• Production-ready — strict typing, >90% test coverage, Docker, PyPI publishing

Get involved

JATE is in active development. We'd love your input:

• Feature requests: Open an issue
• Bug reports: Report here
• Star the repo to follow progress

Background

JATE was originally developed as part of research at the University of Sheffield, with publications in venues including ACM TKDD and the Semantic Web Journal. The library has been used in academic and industry settings for terminology extraction, ontology learning, and knowledge graph construction.

Key publications:

• Zhang, Z., Gao, J., Ciravegna, F. (2018). SemRe-Rank: Improving Automatic Term Extraction By Incorporating Semantic Relatedness With Personalised PageRank. ACM TKDD.
• Zhang, Z., Iria, J., Brewster, C., Ciravegna, F. (2008). A Comparative Evaluation of Term Recognition Algorithms. LREC.

License

Apache 2.0 — see LICENSE for details.

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