src/check_ws_errors.py

# /// script
# requires-python = ">=3.9"
# dependencies = []
# ///
"""Rule-based word segmentation error checker for UDD-1.1 BIO files.

Seven diagnostic rules:
1. Inconsistent segmentation (same form appears both merged and split)
2. Dictionary over-segmentation (adjacent words form a dictionary entry)
3. Dictionary under-segmentation (multi-syllable word not in dictionary)
4. Long token detection (4+ syllables)
5. Punctuation boundary errors (punctuation inside multi-syllable words)
6. Number-word boundary errors (mixed numeric + text syllables)
7. Single-character anomalies (non-Vietnamese artifacts)

Usage:
    uv run src/check_ws_errors.py                  # Run all rules
    uv run src/check_ws_errors.py --no-dict        # Skip dictionary rules (2, 3)
"""

import argparse
import re
import sys
from collections import Counter, defaultdict
from os.path import dirname, isfile, join


# ============================================================================
# Constants
# ============================================================================

FUNCTION_WORDS = {
    "là", "của", "và", "có", "trong", "cho", "được", "các", "này",
    "với", "không", "một", "để", "theo", "đã", "từ", "về", "người",
    "khi", "đến", "tại", "do", "bị", "cũng", "nhưng", "hoặc",
    "nếu", "thì", "phải", "hay", "vì", "đó", "nào", "lại",
    "những", "mà", "sẽ", "ra", "còn", "rất", "đều", "chỉ",
    "vẫn", "nên", "như", "bằng", "qua", "trên", "dưới",
}

PUNCT_CHARS = set(".,;:!?()[]{}\"'`-—–…/\\|@#$%^&*~<>")


# ============================================================================
# BIO parsing (inlined from fix_ws_errors.py to avoid import issues)
# ============================================================================

def parse_bio_file(filepath):
    """Parse BIO file into list of sentences.

    Returns list of dicts with keys: sent_id, text, syllables, tags.
    """
    sentences = []
    current = {"sent_id": "", "text": "", "syllables": [], "tags": []}

    with open(filepath, "r", encoding="utf-8") as f:
        for line in f:
            line = line.rstrip("\n")
            if line.startswith("# sent_id = "):
                current["sent_id"] = line.split("= ", 1)[1]
                continue
            if line.startswith("# text = "):
                current["text"] = line.split("= ", 1)[1]
                continue
            if line.startswith("#"):
                continue
            if not line:
                if current["syllables"]:
                    sentences.append(dict(current))
                    current = {"sent_id": "", "text": "", "syllables": [], "tags": []}
                continue
            parts = line.split("\t")
            if len(parts) == 2:
                current["syllables"].append(parts[0])
                current["tags"].append(parts[1])

    if current["syllables"]:
        sentences.append(dict(current))

    return sentences


def bio_to_words(syllables, tags):
    """Convert syllable-level BIO tags to word list (each word is a list of syllables)."""
    words = []
    current = []
    for syl, tag in zip(syllables, tags):
        if tag == "B-W":
            if current:
                words.append(current)
            current = [syl]
        else:
            current.append(syl)
    if current:
        words.append(current)
    return words


# ============================================================================
# Dictionary loading
# ============================================================================

def load_dictionary():
    """Load Vietnamese dictionary from underthesea (Viet74K / UTS_Dictionary)."""
    try:
        from underthesea.corpus import viet_dict_74K
        words = viet_dict_74K.words
        word_set = set(w.lower().strip() for w in words if w.strip())
        return word_set, "Viet74K"
    except Exception:
        pass
    try:
        from underthesea.datasets.uts_dictionary import UTSDictionary
        d = UTSDictionary()
        word_set = set(w.lower().strip() for w in d.words if w.strip())
        return word_set, "UTS_Dictionary"
    except Exception:
        pass
    return None, None


# ============================================================================
# Rule 1: Inconsistent Segmentation
# ============================================================================

def rule_inconsistency(all_sentences):
    """Find words that appear both merged and split across the dataset.

    For each multi-syllable word, check if its syllables appear as separate
    single-syllable words elsewhere. For each adjacent pair of single-syllable
    words, check if their concatenation exists as a word elsewhere.
    """
    # Build vocabulary: word_form (space-joined lowercase) -> count
    word_counts = Counter()
    for sent in all_sentences:
        words = bio_to_words(sent["syllables"], sent["tags"])
        for w in words:
            form = " ".join(s.lower() for s in w)
            word_counts[form] += 1

    # Set of multi-syllable word forms
    multi_forms = {form for form in word_counts if " " in form}

    # Scan for split occurrences of multi-syllable words
    split_counts = Counter()
    split_examples = {}  # form -> first sent_id
    for sent in all_sentences:
        words = bio_to_words(sent["syllables"], sent["tags"])
        # Check bigrams of single-syllable words
        for i in range(len(words) - 1):
            if len(words[i]) == 1 and len(words[i + 1]) == 1:
                bigram = words[i][0].lower() + " " + words[i + 1][0].lower()
                if bigram in multi_forms:
                    split_counts[bigram] += 1
                    if bigram not in split_examples:
                        split_examples[bigram] = sent["sent_id"]
        # Check trigrams of single-syllable words
        for i in range(len(words) - 2):
            if len(words[i]) == 1 and len(words[i + 1]) == 1 and len(words[i + 2]) == 1:
                trigram = (words[i][0].lower() + " " +
                           words[i + 1][0].lower() + " " +
                           words[i + 2][0].lower())
                if trigram in multi_forms:
                    split_counts[trigram] += 1
                    if trigram not in split_examples:
                        split_examples[trigram] = sent["sent_id"]

    # Combine: only keep forms that appear BOTH ways
    findings = []
    for form in split_counts:
        as_single = word_counts.get(form, 0)
        as_split = split_counts[form]
        if as_single > 0 and as_split > 0:
            total = as_single + as_split
            majority = "single" if as_single >= as_split else "split"
            findings.append({
                "form": form,
                "as_single": as_single,
                "as_split": as_split,
                "total": total,
                "majority": majority,
                "example_sent_id": split_examples.get(form, ""),
            })

    findings.sort(key=lambda x: x["total"], reverse=True)
    return findings


# ============================================================================
# Rule 2: Dictionary Over-Segmentation
# ============================================================================

def rule_dict_over_seg(all_sentences, dict_set):
    """Find adjacent single-syllable words that together form a dictionary entry."""
    over_seg = Counter()
    over_seg_examples = {}

    for sent in all_sentences:
        words = bio_to_words(sent["syllables"], sent["tags"])
        word_forms = [" ".join(w).lower() for w in words]
        word_lengths = [len(w) for w in words]

        # Check bigrams of single-syllable words
        for i in range(len(words) - 1):
            if word_lengths[i] == 1 and word_lengths[i + 1] == 1:
                w1 = word_forms[i]
                w2 = word_forms[i + 1]
                # Skip if both are function words
                if w1 in FUNCTION_WORDS and w2 in FUNCTION_WORDS:
                    continue
                bigram = w1 + " " + w2
                if bigram in dict_set:
                    over_seg[bigram] += 1
                    if bigram not in over_seg_examples:
                        over_seg_examples[bigram] = sent["sent_id"]

        # Check trigrams of single-syllable words
        for i in range(len(words) - 2):
            if word_lengths[i] == 1 and word_lengths[i + 1] == 1 and word_lengths[i + 2] == 1:
                w1 = word_forms[i]
                w2 = word_forms[i + 1]
                w3 = word_forms[i + 2]
                # Skip if all are function words
                if w1 in FUNCTION_WORDS and w2 in FUNCTION_WORDS and w3 in FUNCTION_WORDS:
                    continue
                trigram = w1 + " " + w2 + " " + w3
                if trigram in dict_set:
                    over_seg[trigram] += 1
                    if trigram not in over_seg_examples:
                        over_seg_examples[trigram] = sent["sent_id"]

    findings = []
    for form, count in over_seg.most_common():
        findings.append({
            "form": form,
            "count": count,
            "example_sent_id": over_seg_examples.get(form, ""),
        })
    return findings


# ============================================================================
# Rule 3: Dictionary Under-Segmentation
# ============================================================================

def rule_dict_under_seg(all_sentences, dict_set):
    """Find multi-syllable words NOT in dictionary where all syllables ARE."""
    under_seg = Counter()
    under_seg_examples = {}

    for sent in all_sentences:
        words = bio_to_words(sent["syllables"], sent["tags"])
        for w in words:
            if len(w) < 2:
                continue
            form_lower = " ".join(s.lower() for s in w)
            if form_lower in dict_set:
                continue
            # Check if all individual syllables are in dictionary
            syllables = [s.lower() for s in w]
            if all(s in dict_set for s in syllables):
                under_seg[form_lower] += 1
                if form_lower not in under_seg_examples:
                    under_seg_examples[form_lower] = sent["sent_id"]

    findings = []
    for form, count in under_seg.most_common():
        syllables = form.split()
        findings.append({
            "form": form,
            "count": count,
            "syllables": syllables,
            "example_sent_id": under_seg_examples.get(form, ""),
        })
    return findings


# ============================================================================
# Rule 4: Long Token Detection
# ============================================================================

def rule_long_tokens(all_sentences):
    """Find tokens with 4+ syllables (suspicious for Vietnamese)."""
    long_tokens = Counter()
    long_examples = {}
    long_lengths = {}

    for sent in all_sentences:
        words = bio_to_words(sent["syllables"], sent["tags"])
        for w in words:
            if len(w) >= 4:
                form = " ".join(w)
                long_tokens[form] += 1
                long_lengths[form] = len(w)
                if form not in long_examples:
                    long_examples[form] = sent["sent_id"]

    findings = []
    for form, count in long_tokens.most_common():
        findings.append({
            "form": form,
            "count": count,
            "syllable_count": long_lengths[form],
            "example_sent_id": long_examples.get(form, ""),
        })
    return findings


# ============================================================================
# Rule 5: Punctuation Boundary Errors
# ============================================================================

def rule_punct_boundary(all_sentences):
    """Find punctuation characters inside multi-syllable word groups."""
    punct_errors = Counter()
    punct_examples = {}

    for sent in all_sentences:
        words = bio_to_words(sent["syllables"], sent["tags"])
        for w in words:
            if len(w) < 2:
                continue
            # Check if any syllable is purely punctuation
            has_punct = False
            has_text = False
            for syl in w:
                if all(c in PUNCT_CHARS for c in syl):
                    has_punct = True
                else:
                    has_text = True
            if has_punct and has_text:
                form = " ".join(w)
                punct_errors[form] += 1
                if form not in punct_examples:
                    punct_examples[form] = sent["sent_id"]

    findings = []
    for form, count in punct_errors.most_common():
        findings.append({
            "form": form,
            "count": count,
            "example_sent_id": punct_examples.get(form, ""),
        })
    return findings


# ============================================================================
# Rule 6: Number-Word Boundary Errors
# ============================================================================

def _is_numeric(s):
    """Check if a syllable is numeric (digits, decimals, commas in numbers)."""
    return bool(re.match(r'^[\d.,]+$', s))


def rule_number_boundary(all_sentences):
    """Find multi-syllable words with mixed numeric and text syllables."""
    mixed_tokens = Counter()
    mixed_examples = {}

    for sent in all_sentences:
        words = bio_to_words(sent["syllables"], sent["tags"])
        for w in words:
            if len(w) < 2:
                continue
            has_num = False
            has_text = False
            for syl in w:
                if all(c in PUNCT_CHARS for c in syl):
                    continue  # ignore pure punctuation syllables
                if _is_numeric(syl):
                    has_num = True
                else:
                    has_text = True
            if has_num and has_text:
                form = " ".join(w)
                mixed_tokens[form] += 1
                if form not in mixed_examples:
                    mixed_examples[form] = sent["sent_id"]

    findings = []
    for form, count in mixed_tokens.most_common():
        findings.append({
            "form": form,
            "count": count,
            "example_sent_id": mixed_examples.get(form, ""),
        })
    return findings


# ============================================================================
# Rule 7: Single-Character Anomalies
# ============================================================================

# Vietnamese base consonants and vowels (without tone marks)
_VIETNAMESE_BASE_CHARS = set(
    "aăâbcdđeêghiklmnoôơpqrstuưvxy"
)

# Non-Vietnamese Latin letters (not in Vietnamese alphabet)
_NON_VIETNAMESE_LATIN = set("fjwz")


def _is_vietnamese_letter(ch):
    """Check if a single character is a Vietnamese letter (including tonal variants)."""
    import unicodedata
    # Decompose to get the base character (strip combining marks)
    decomposed = unicodedata.normalize("NFD", ch.lower())
    base = decomposed[0] if decomposed else ch.lower()
    # đ doesn't decompose, check directly
    if ch.lower() == "đ":
        return True
    return base in _VIETNAMESE_BASE_CHARS


def rule_single_char(all_sentences):
    """Find standalone single characters that are likely segmentation artifacts."""
    char_counts = Counter()
    char_examples = {}

    for sent in all_sentences:
        words = bio_to_words(sent["syllables"], sent["tags"])
        for w in words:
            if len(w) != 1:
                continue
            syl = w[0]
            if len(syl) != 1:
                continue
            ch = syl
            # Flag non-alphanumeric single chars (excluding common punctuation)
            if ch in "]|{}[":
                char_counts[ch] += 1
                if ch not in char_examples:
                    char_examples[ch] = sent["sent_id"]
                continue
            # Flag standalone single Latin letters not in Vietnamese alphabet
            if ch.isalpha() and ch.lower() in _NON_VIETNAMESE_LATIN:
                char_counts[ch] += 1
                if ch not in char_examples:
                    char_examples[ch] = sent["sent_id"]

    findings = []
    for ch, count in char_counts.most_common():
        findings.append({
            "char": ch,
            "count": count,
            "example_sent_id": char_examples.get(ch, ""),
        })
    return findings


# ============================================================================
# Report generation
# ============================================================================

def generate_report(results, output_path):
    """Generate WS_CHECK_REPORT.md from all rule findings."""
    lines = []
    lines.append("# WS Check Report")
    lines.append("")
    lines.append("Diagnostic report generated by `src/check_ws_errors.py`.")
    lines.append("Detects potential word segmentation errors in UDD-1.1 BIO files.")
    lines.append("")

    # Summary table
    lines.append("## Summary")
    lines.append("")
    lines.append("| Rule | Description | Unique Forms | Total Occurrences |")
    lines.append("|:-----|:------------|-------------:|------------------:|")
    rule_names = [
        ("rule1", "Inconsistent segmentation"),
        ("rule2", "Dictionary over-segmentation"),
        ("rule3", "Dictionary under-segmentation"),
        ("rule4", "Long tokens (4+ syllables)"),
        ("rule5", "Punctuation boundary errors"),
        ("rule6", "Number-word boundary errors"),
        ("rule7", "Single-character anomalies"),
    ]
    for key, desc in rule_names:
        findings = results.get(key)
        if findings is None:
            lines.append(f"| {key} | {desc} | skipped | skipped |")
            continue
        unique = len(findings)
        if key == "rule1":
            total = sum(f["total"] for f in findings)
        elif key == "rule7":
            total = sum(f["count"] for f in findings)
        elif key in ("rule2", "rule3", "rule4", "rule5", "rule6"):
            total = sum(f["count"] for f in findings)
        else:
            total = unique
        lines.append(f"| {key} | {desc} | {unique:,} | {total:,} |")
    lines.append("")

    # Rule 1: Inconsistency
    if results.get("rule1") is not None:
        findings = results["rule1"]
        lines.append("## Rule 1: Inconsistent Segmentation")
        lines.append("")
        lines.append(f"Words appearing both as single token and split across the dataset. "
                      f"**{len(findings):,}** unique forms found.")
        lines.append("")
        if findings:
            lines.append("| Form | As Single | As Split | Total | Majority | Example |")
            lines.append("|:-----|----------:|---------:|------:|:---------|:--------|")
            for f in findings[:50]:
                lines.append(
                    f"| {f['form']} | {f['as_single']:,} | {f['as_split']:,} | "
                    f"{f['total']:,} | {f['majority']} | {f['example_sent_id']} |"
                )
            if len(findings) > 50:
                lines.append(f"| ... | | | | | *{len(findings) - 50} more* |")
        lines.append("")

    # Rule 2: Dictionary over-segmentation
    if results.get("rule2") is not None:
        findings = results["rule2"]
        lines.append("## Rule 2: Dictionary Over-Segmentation")
        lines.append("")
        lines.append(f"Adjacent single-syllable words forming a dictionary entry. "
                      f"**{len(findings):,}** unique forms found.")
        lines.append("")
        if findings:
            lines.append("| Dictionary Word | Times Split | Example |")
            lines.append("|:----------------|------------:|:--------|")
            for f in findings[:50]:
                lines.append(f"| {f['form']} | {f['count']:,} | {f['example_sent_id']} |")
            if len(findings) > 50:
                lines.append(f"| ... | | *{len(findings) - 50} more* |")
        lines.append("")

    # Rule 3: Dictionary under-segmentation
    if results.get("rule3") is not None:
        findings = results["rule3"]
        lines.append("## Rule 3: Dictionary Under-Segmentation")
        lines.append("")
        lines.append(f"Multi-syllable words NOT in dictionary, but all syllables are. "
                      f"**{len(findings):,}** unique forms found.")
        lines.append("")
        if findings:
            lines.append("| Word | Count | Sub-parts | Example |")
            lines.append("|:-----|------:|:----------|:--------|")
            for f in findings[:50]:
                parts = " + ".join(f["syllables"])
                lines.append(f"| {f['form']} | {f['count']:,} | {parts} | {f['example_sent_id']} |")
            if len(findings) > 50:
                lines.append(f"| ... | | | *{len(findings) - 50} more* |")
        lines.append("")

    # Rule 4: Long tokens
    if results.get("rule4") is not None:
        findings = results["rule4"]
        lines.append("## Rule 4: Long Tokens (4+ Syllables)")
        lines.append("")
        lines.append(f"Tokens with 4 or more syllables (unusual for Vietnamese). "
                      f"**{len(findings):,}** unique forms found.")
        lines.append("")
        if findings:
            lines.append("| Word | Syllables | Count | Example |")
            lines.append("|:-----|----------:|------:|:--------|")
            for f in findings[:50]:
                lines.append(
                    f"| {f['form']} | {f['syllable_count']} | {f['count']:,} | "
                    f"{f['example_sent_id']} |"
                )
            if len(findings) > 50:
                lines.append(f"| ... | | | *{len(findings) - 50} more* |")
        lines.append("")

    # Rule 5: Punctuation boundary
    if results.get("rule5") is not None:
        findings = results["rule5"]
        lines.append("## Rule 5: Punctuation Boundary Errors")
        lines.append("")
        lines.append(f"Punctuation characters inside multi-syllable word groups. "
                      f"**{len(findings):,}** unique forms found.")
        lines.append("")
        if findings:
            lines.append("| Word | Count | Example |")
            lines.append("|:-----|------:|:--------|")
            for f in findings[:50]:
                lines.append(f"| {f['form']} | {f['count']:,} | {f['example_sent_id']} |")
            if len(findings) > 50:
                lines.append(f"| ... | | *{len(findings) - 50} more* |")
        lines.append("")

    # Rule 6: Number-word boundary
    if results.get("rule6") is not None:
        findings = results["rule6"]
        lines.append("## Rule 6: Number-Word Boundary Errors")
        lines.append("")
        lines.append(f"Multi-syllable words with mixed numeric and text syllables. "
                      f"**{len(findings):,}** unique forms found.")
        lines.append("")
        if findings:
            lines.append("| Token | Count | Example |")
            lines.append("|:------|------:|:--------|")
            for f in findings[:50]:
                lines.append(f"| {f['form']} | {f['count']:,} | {f['example_sent_id']} |")
            if len(findings) > 50:
                lines.append(f"| ... | | *{len(findings) - 50} more* |")
        lines.append("")

    # Rule 7: Single-character anomalies
    if results.get("rule7") is not None:
        findings = results["rule7"]
        lines.append("## Rule 7: Single-Character Anomalies")
        lines.append("")
        lines.append(f"Standalone single characters that are likely artifacts. "
                      f"**{len(findings):,}** unique forms found.")
        lines.append("")
        if findings:
            lines.append("| Character | Count | Example |")
            lines.append("|:----------|------:|:--------|")
            for f in findings:
                lines.append(f"| `{f['char']}` | {f['count']:,} | {f['example_sent_id']} |")
        lines.append("")

    report = "\n".join(lines)
    with open(output_path, "w", encoding="utf-8") as f:
        f.write(report)
    print(f"\nReport written to {output_path}")
    return report


# ============================================================================
# Main
# ============================================================================

def main():
    parser = argparse.ArgumentParser(
        description="Check word segmentation errors in UDD-1.1 BIO files."
    )
    parser.add_argument(
        "--no-dict", action="store_true",
        help="Skip dictionary-based rules (2, 3)"
    )
    args = parser.parse_args()

    base_dir = dirname(dirname(__file__))
    bio_files = [
        join(base_dir, f"udd-ws-v1.1-{split}.txt")
        for split in ("train", "dev", "test")
    ]

    # Check all files exist
    for path in bio_files:
        if not isfile(path):
            print(f"ERROR: {path} not found", file=sys.stderr)
            sys.exit(1)

    # Load all sentences
    print("Loading BIO files...")
    all_sentences = []
    for path in bio_files:
        sents = parse_bio_file(path)
        print(f"  {path.rsplit('/', 1)[-1]}: {len(sents):,} sentences")
        all_sentences.extend(sents)
    print(f"  Total: {len(all_sentences):,} sentences")

    # Load dictionary if needed
    dict_set = None
    if not args.no_dict:
        print("\nLoading dictionary...")
        dict_set, dict_name = load_dictionary()
        if dict_set:
            print(f"  Loaded {dict_name}: {len(dict_set):,} entries")
        else:
            print("  WARNING: No dictionary available, skipping rules 2 & 3")

    # Run rules
    results = {}

    print("\nRule 1: Inconsistent segmentation...")
    results["rule1"] = rule_inconsistency(all_sentences)
    print(f"  Found {len(results['rule1']):,} inconsistent forms")

    if dict_set:
        print("\nRule 2: Dictionary over-segmentation...")
        results["rule2"] = rule_dict_over_seg(all_sentences, dict_set)
        print(f"  Found {len(results['rule2']):,} over-segmented forms")

        print("\nRule 3: Dictionary under-segmentation...")
        results["rule3"] = rule_dict_under_seg(all_sentences, dict_set)
        print(f"  Found {len(results['rule3']):,} under-segmented forms")
    else:
        results["rule2"] = None
        results["rule3"] = None

    print("\nRule 4: Long tokens (4+ syllables)...")
    results["rule4"] = rule_long_tokens(all_sentences)
    print(f"  Found {len(results['rule4']):,} long token forms")

    print("\nRule 5: Punctuation boundary errors...")
    results["rule5"] = rule_punct_boundary(all_sentences)
    print(f"  Found {len(results['rule5']):,} punctuation boundary errors")

    print("\nRule 6: Number-word boundary errors...")
    results["rule6"] = rule_number_boundary(all_sentences)
    print(f"  Found {len(results['rule6']):,} number-word boundary errors")

    print("\nRule 7: Single-character anomalies...")
    results["rule7"] = rule_single_char(all_sentences)
    print(f"  Found {len(results['rule7']):,} single-character anomalies")

    # Generate report
    report_path = join(base_dir, "WS_CHECK_REPORT.md")
    generate_report(results, report_path)

    # Print summary
    print(f"\n{'=' * 50}")
    print("SUMMARY")
    for key, label in [
        ("rule1", "Inconsistent segmentation"),
        ("rule2", "Dict over-segmentation"),
        ("rule3", "Dict under-segmentation"),
        ("rule4", "Long tokens (4+ syl)"),
        ("rule5", "Punctuation boundary"),
        ("rule6", "Number-word boundary"),
        ("rule7", "Single-char anomalies"),
    ]:
        findings = results.get(key)
        if findings is None:
            print(f"  {label}: skipped")
        else:
            print(f"  {label}: {len(findings):,} unique forms")


if __name__ == "__main__":
    main()