# Syntax DAG Transformer

An English-language prototype that combines spaCy's tokenizer and learned
dependency parser with explicit graph rules. The graph interface can support
other encoders and relation miners as the prototype evolves.

```text
The road flooded because it rained.
  → Cause: it rained. Effect: The road flooded.
```

Run with Python 3.12 after installing `requirements.txt`.

```sh
python -m syntax_dag --commit 'The road flooded because it rained.'
python -m unittest -v
```

Omit `--commit` to recover the original token surfaces and whitespace. The
command prints one final newline.

1. `Encoder` creates token nodes with original whitespace and head-to-dependent
   edges. The causal rule adds clause nodes with edges to their member tokens.
   A token can have incoming edges from both a clause and a syntactic head.
2. The rule mines a candidate `cause → effect` edge with provenance from a
   trailing `because` clause. The candidate stays separate until committed.
3. `SyntaxDAG.commit` creates a new immutable graph revision and records the
   proposal. A cycle rejects the entire edit. Repeating a commit on the same
   revision returns that revision.
4. `Decoder` uses committed causal edges to select the clause order and labels.
   Text outside the selected clauses retains its original surface form.

The first rule covers simple affirmative past-tense statements with a trailing
finite causal clause. Extraction is heuristic and depends on parser accuracy.
Commits are in-memory graph edits. The prototype uses explicit authorization
via the CLI flag and has no learned rewrite policy.