# liuhuanyong/ComplexEventExtraction

A concept and obvious expression pattern collection of Chinese compound event extraction which then be evolved into ComplexEventGraph，本项目提出了中文复合事件的概念与显式模式，包括条件事件、因果事件、顺承事件、反转事件等事件抽取，并形成事理图谱。

Repository: https://github.com/liuhuanyong/ComplexEventExtraction
Canonical: https://ross.abutalabs.com/products/complexeventextraction
Language: Python
License Family: other
Last push: 2018-12-15T04:53:17+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2893, "days_push": 2818, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1227, forks 282 (observed 2026-08-28T04:04:03.337360+00:00)

## What it is
A Python library for Chinese compound event extraction that identifies conditional, causal, sequential, and adversative events using explicit conjunction patterns, building event graphs (event logic graphs). It collects Chinese complex-sentence connective patterns and applies them to large-scale news corpora to extract event pairs.

## Use cases
- extract causal events from Chinese text
- build an event logic graph from news corpora
- detect conditional and adversative event relations in Chinese sentences
- mine sequential event pairs for intent prediction
- research Chinese complex sentence connective patterns

## When to choose
- you need rule-based Chinese event relation extraction without training data
- you want to construct event graphs from large Chinese text corpora
- you need a curated collection of Chinese complex-sentence conjunction patterns

## When to avoid
- you need multilingual event extraction
- you require actively maintained software with a license
- you need neural or state-of-the-art event extraction models

## Facets
- artifact type: library
- maturity: maintenance
- function: nlp, parser, machine-learning
- domain: artificial-intelligence
- platform: python, cross-platform
- tags: event-extraction, event-graph, chinese-nlp, causality, information-extraction, complex-events, natural-language-processing, algorithms

## Member repositories
- liuhuanyong/ComplexEventExtraction (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:03.337360+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:15:16.247943+00:00, confidence not recorded.
  - readme: https://github.com/liuhuanyong/ComplexEventExtraction (fetched 2026-08-28T04:04:03.337360+00:00, sha 7d10d2b3ae7e)
- Data as of 2026-08-30T08:39:29.467469+00:00.