fix(seo): guard keyword map semantic fit
This commit is contained in:
DCCONSTRUCTIONS
parent
1ed099140f
commit
43d2af454d
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@ -89,11 +89,28 @@ export type KeywordCleaningModelTaskContract = {
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decisionValues: KeywordCleaningDecision[];
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};
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decisionPolicy: {
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broadFrequencyDoesNotOverrideSemanticFit: boolean;
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demoteRelatedWhenDominantFacetMissing: boolean;
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preserveApprovedAnchorFacets: boolean;
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useRequiresExactEvidence: boolean;
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riskyWhenNoPageBinding: boolean;
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trashWhenOffContext: boolean;
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articleDoesNotEnterRewrite: boolean;
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};
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semanticGuard: {
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approvedAnchorCount: number;
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dominantProtectedFacets: Array<{
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approvedAnchorCount: number;
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approvedAnchorShare: number;
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examples: string[];
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stem: string;
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}>;
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policy: {
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broadFrequencyDoesNotOverrideSemanticFit: boolean;
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demoteRelatedWhenDominantFacetMissing: boolean;
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preserveSourceAnchorFacetsForRelated: boolean;
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};
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};
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stopConditions: string[];
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};
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@ -208,6 +225,110 @@ function wordCount(value: string) {
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return normalizePhrase(value).split(/\s+/).filter(Boolean).length;
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}
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const KEYWORD_CLEANING_FACET_STOP_WORDS = new Set([
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"a",
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"an",
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"and",
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"by",
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"for",
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"in",
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"of",
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"on",
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"or",
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"the",
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"to",
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"без",
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"в",
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"для",
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"и",
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"или",
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"как",
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"на",
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"о",
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"об",
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"от",
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"по",
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"при",
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"с",
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"со",
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"что",
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"это"
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]);
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const GENERIC_KEYWORD_CLEANING_FACET_STEMS = new Set([
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"автоматизац",
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"автоматизаци",
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"бизнес",
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"данн",
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"задач",
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"инструмент",
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"компан",
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"контур",
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"модул",
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"платформ",
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"приложен",
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"процесс",
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"проект",
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"разработк",
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"решен",
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"сервис",
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"систем",
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"сайт",
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"управлен",
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"услуг"
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]);
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function stemKeywordCleaningToken(token: string) {
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const normalizedToken = token.toLocaleLowerCase("ru-RU").replace(/ё/g, "е");
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if (/^(ai|ии)$/i.test(normalizedToken) || /^(artificial|intelligence)$/i.test(normalizedToken)) {
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return "ai";
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}
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if (/^(искусствен|интеллект)/i.test(normalizedToken)) {
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return "ai";
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}
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if (/^[a-z0-9]+$/i.test(normalizedToken)) {
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return normalizedToken.toLowerCase();
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}
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return normalizedToken.replace(
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/(иями|ями|ами|ого|его|ому|ему|ыми|ими|ых|их|ией|иям|ям|ам|ях|ах|ов|ев|ей|ой|ый|ий|ая|яя|ое|ее|ые|ие|ую|юю|ом|ем|а|я|ы|и|у|ю|е|о)$/i,
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""
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);
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}
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function getKeywordCleaningFacetStems(value: string) {
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return new Set(
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normalizePhrase(value)
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.replace(/ё/g, "е")
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.split(/[^a-zа-я0-9]+/i)
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.map((token) => token.trim())
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.filter((token) => token.length > 1 && !KEYWORD_CLEANING_FACET_STOP_WORDS.has(token))
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.map(stemKeywordCleaningToken)
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.filter((stem) => stem.length > 1 && !KEYWORD_CLEANING_FACET_STOP_WORDS.has(stem))
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);
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}
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function isProtectedKeywordCleaningFacetStem(
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stem: string,
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approvedAnchorCount: number,
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stemDocumentCounts: Map<string, number>
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) {
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if (GENERIC_KEYWORD_CLEANING_FACET_STEMS.has(stem)) {
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return false;
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}
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if (/^[a-z0-9]+$/i.test(stem)) {
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return true;
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}
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const documentShare = approvedAnchorCount > 0 ? (stemDocumentCounts.get(stem) ?? 0) / approvedAnchorCount : 0;
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return stem.length >= 7 || documentShare >= 0.18;
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}
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function hasPattern(value: string, patterns: RegExp[]) {
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return patterns.some((pattern) => pattern.test(value));
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}
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@ -300,6 +421,134 @@ function buildBriefClusterMap(contract: MarketEnrichmentContract) {
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return clusterMap;
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}
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type KeywordCleaningFacetGuard = {
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approvedAnchorCount: number;
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dominantProtectedStems: Array<{ count: number; stem: string }>;
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examplesByStem: Map<string, string[]>;
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stemDocumentCounts: Map<string, number>;
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};
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function buildKeywordCleaningFacetGuard(contract: MarketEnrichmentContract): KeywordCleaningFacetGuard {
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const stemDocumentCounts = new Map<string, number>();
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const examplesByStem = new Map<string, string[]>();
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const approvedAnchors = contract.anchorReview.wordstatQueue.slice(0, 120);
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for (const anchor of approvedAnchors) {
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for (const stem of getKeywordCleaningFacetStems(`${anchor.phrase} ${anchor.clusterTitle}`)) {
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stemDocumentCounts.set(stem, (stemDocumentCounts.get(stem) ?? 0) + 1);
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const examples = examplesByStem.get(stem) ?? [];
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if (examples.length < 3 && !examples.map(normalizePhrase).includes(normalizePhrase(anchor.phrase))) {
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examples.push(anchor.phrase);
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examplesByStem.set(stem, examples);
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}
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}
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}
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const dominantProtectedStems = [...stemDocumentCounts.entries()]
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.filter(([stem, count]) => {
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const share = approvedAnchors.length > 0 ? count / approvedAnchors.length : 0;
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return share >= 0.3 && isProtectedKeywordCleaningFacetStem(stem, approvedAnchors.length, stemDocumentCounts);
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})
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.sort((left, right) => right[1] - left[1] || left[0].localeCompare(right[0], "ru"))
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.slice(0, 12)
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.map(([stem, count]) => ({ count, stem }));
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return {
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approvedAnchorCount: approvedAnchors.length,
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dominantProtectedStems,
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examplesByStem,
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stemDocumentCounts
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};
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}
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function buildKeywordCleaningSemanticGuard(contract: MarketEnrichmentContract): KeywordCleaningModelTaskContract["semanticGuard"] {
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const guard = buildKeywordCleaningFacetGuard(contract);
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const dominantProtectedFacets = guard.dominantProtectedStems
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.map((facet) => ({
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approvedAnchorCount: facet.count,
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approvedAnchorShare: Number((facet.count / Math.max(guard.approvedAnchorCount, 1)).toFixed(3)),
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examples: guard.examplesByStem.get(facet.stem) ?? [],
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stem: facet.stem
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}));
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return {
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approvedAnchorCount: guard.approvedAnchorCount,
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dominantProtectedFacets,
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policy: {
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broadFrequencyDoesNotOverrideSemanticFit: true,
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demoteRelatedWhenDominantFacetMissing: true,
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preserveSourceAnchorFacetsForRelated: true
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}
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};
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}
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function getProtectedSourceFacetStems(item: KeywordCleaningItem, guard: KeywordCleaningFacetGuard) {
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if (item.source !== "wordstat_related" || !item.sourcePhrase) {
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return new Set<string>();
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}
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const sourceStems = getKeywordCleaningFacetStems(`${item.sourcePhrase} ${item.clusterTitle}`);
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return new Set(
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[...sourceStems].filter((stem) =>
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isProtectedKeywordCleaningFacetStem(stem, guard.approvedAnchorCount, guard.stemDocumentCounts)
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)
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);
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}
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function getKeywordCleaningSemanticDowngradeReason(item: KeywordCleaningItem, guard: KeywordCleaningFacetGuard) {
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const phraseStems = getKeywordCleaningFacetStems(item.phrase);
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const sourceProtectedStems = getProtectedSourceFacetStems(item, guard);
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const missingSourceStems = [...sourceProtectedStems].filter((stem) => !phraseStems.has(stem));
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if (sourceProtectedStems.size > 0 && missingSourceStems.length === sourceProtectedStems.size) {
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return `Wordstat related потерял protected-признак исходного якоря (${missingSourceStems.join(", ")}).`;
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}
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const dominantProtectedStems = guard.dominantProtectedStems.map((item) => item.stem);
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const missingDominantStems = dominantProtectedStems.filter((stem) => !phraseStems.has(stem));
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if (dominantProtectedStems.length > 0 && missingDominantStems.length === dominantProtectedStems.length) {
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return `Фраза не содержит доминантный protected-смысл approved anchors (${missingDominantStems.join(", ")}).`;
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}
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return null;
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}
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function enforceKeywordCleaningSemanticGuard(
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market: MarketEnrichmentContract,
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items: KeywordCleaningItem[]
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): KeywordCleaningItem[] {
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const guard = buildKeywordCleaningFacetGuard(market);
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if (guard.approvedAnchorCount === 0) {
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return items;
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}
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return items.map((item) => {
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if (item.decision !== "use" && item.decision !== "support") {
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return item;
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}
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const semanticDowngradeReason = getKeywordCleaningSemanticDowngradeReason(item, guard);
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if (!semanticDowngradeReason) {
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return item;
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}
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return {
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...item,
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blockers: unique([...item.blockers, "semantic_facet_loss"]),
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confidence: Math.min(item.confidence, 0.62),
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decision: "risky",
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reason: `${semanticDowngradeReason} Backend guard понизил фразу в risky: частотность не перекрывает потерю смысла.`
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};
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});
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}
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function getTargetPath(seed: SeedQueueItem, briefPhraseMap: Map<string, string>, briefClusterMap: Map<string, string>) {
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const directTarget = briefPhraseMap.get(normalizePhrase(seed.phrase));
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@ -704,7 +953,11 @@ function buildReadiness(items: KeywordCleaningItem[], lanes: KeywordCleaningCont
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articleCount: lanes.article.length,
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exactEvidenceCount: items.filter((item) => item.frequency !== null).length,
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keywordMapCandidateCount: items.filter(
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(item) => item.decision !== "trash" && item.evidenceStatus === "collected" && item.frequency !== null && item.targetPath
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(item) =>
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(item.decision === "use" || item.decision === "support") &&
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item.evidenceStatus === "collected" &&
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item.frequency !== null &&
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item.targetPath
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).length,
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missingPageBindingCount: items.filter((item) => item.targetPath === null && item.decision !== "trash").length,
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riskyCount: lanes.risky.length,
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@ -734,6 +987,9 @@ function buildKeywordCleaningModelTask(
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],
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decisionPolicy: {
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articleDoesNotEnterRewrite: true,
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broadFrequencyDoesNotOverrideSemanticFit: true,
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demoteRelatedWhenDominantFacetMissing: true,
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preserveApprovedAnchorFacets: true,
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riskyWhenNoPageBinding: true,
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trashWhenOffContext: true,
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useRequiresExactEvidence: true
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@ -777,9 +1033,11 @@ function buildKeywordCleaningModelTask(
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providerMode: "model_provider_contract",
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schemaVersion: "seo-keyword-cleaning-task.v1",
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semanticRunId: contract.semanticRunId,
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semanticGuard: buildKeywordCleaningSemanticGuard(contract),
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stopConditions: [
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"Не отправлять trash в keyword map. Risky/article exact-фразы можно только предсортировать в Stage 3, но не считать approved без пользователя.",
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"Не придумывать спрос без Wordstat/SERP evidence.",
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"Не повышать broad/head phrase до use/support, если Wordstat related потерял protected-смысл исходного якоря или доминантный смысл approved anchors.",
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"Не менять бизнес-вектор сайта; соседние рынки держать как article/backlog proposal.",
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"Не сохранять rewrite/apply decisions."
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],
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@ -824,7 +1082,7 @@ function buildNextActions(contract: Omit<KeywordCleaningContract, "nextActions">
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function buildFallbackContract(projectId: string, market: MarketEnrichmentContract): KeywordCleaningContract {
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const modelTask = buildKeywordCleaningModelTask(projectId, market);
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const items = classifyItems(market, getBaseItems(market));
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const items = enforceKeywordCleaningSemanticGuard(market, classifyItems(market, getBaseItems(market)));
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const lanes = buildLanes(items);
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const readiness = buildReadiness(items, lanes);
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const contractWithoutActions: Omit<KeywordCleaningContract, "nextActions"> = {
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@ -990,13 +1248,34 @@ export async function getKeywordCleaningContract(projectId: string): Promise<Key
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const latest = await getLatestAlignedKeywordCleaning(projectId, market.semanticRunId, market.wordstat.latestJob?.id ?? null);
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return latest
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? {
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...latest,
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modelTask: fallbackContract.modelTask,
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sourceTaskId: fallbackContract.sourceTaskId
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if (!latest) {
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return fallbackContract;
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}
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: fallbackContract;
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const items = enforceKeywordCleaningSemanticGuard(market, latest.items);
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const lanes = buildLanes(items);
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const readiness = buildReadiness(items, lanes);
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return {
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...latest,
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items,
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lanes,
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modelTask: fallbackContract.modelTask,
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nextActions: buildNextActions({
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...latest,
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items,
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lanes,
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modelTask: fallbackContract.modelTask,
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readiness,
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sourceTaskId: fallbackContract.sourceTaskId
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}),
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readiness,
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sourceTaskId: fallbackContract.sourceTaskId,
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summary:
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items.length > 0
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? `Очищено ${items.length} фраз: ${readiness.useCount} use, ${readiness.supportCount} support, ${readiness.articleCount} article, ${readiness.riskyCount} risky, ${readiness.trashCount} trash.`
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: "Нет фраз для keyword cleaning."
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};
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}
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export async function saveKeywordCleaningManual(
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@ -1051,7 +1330,7 @@ export async function saveKeywordCleaningFromModelProvider(
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const alignedOutput: KeywordCleaningContract = {
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...output,
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items: mergeModelProviderItemsWithMarketEvidence(output.items, market),
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items: enforceKeywordCleaningSemanticGuard(market, mergeModelProviderItemsWithMarketEvidence(output.items, market)),
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modelTask,
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projectOntologyVersion: market.projectOntologyVersion,
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semanticRunId: market.semanticRunId,
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@ -103,30 +103,6 @@ function getKeywordProfileText(item: Pick<KeywordCleaningItem, "clusterTitle" |
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return normalizePhrase(`${item.phrase} ${item.clusterTitle}`);
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}
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function isAiDifferentiatorCandidate(item: Pick<KeywordCleaningItem, "clusterTitle" | "phrase">) {
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const text = getKeywordProfileText(item);
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return /(^|\s)(ai|ии)(\s|$)/i.test(text) || /(агент|агентн|ассистент|assistant|agentic)/i.test(text);
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}
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function hasProductLikeHybridSignal(value: string) {
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const text = normalizePhrase(value);
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const hasProductNoun = /(систем[ауы]?|платформ[ауы]?|сервис|software|product|suite)/i.test(text);
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const hasProcessNoun = /(бизнес[-\s]?процесс|bpms?|workflow|процесс[а-яё]*)/i.test(text);
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return hasProductNoun && hasProcessNoun;
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}
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function isBroadArticleBacklogPhrase(value: string) {
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const text = normalizePhrase(value);
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return /^автоматизаци[яи]\s+бизнес[-\s]?процесс(ов|ы)?$/i.test(text);
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}
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function isHybridSecondaryCandidate(item: KeywordCleaningItem) {
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return item.frequency !== null && hasProductLikeHybridSignal(item.phrase) && !isAiDifferentiatorCandidate(item);
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}
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function isReviewedOntology(version: MarketEnrichmentContract["projectOntologyVersion"]) {
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return (
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Boolean(version) &&
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@ -214,6 +190,17 @@ type SemanticFacetGuard = {
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stemDocumentCounts: Map<string, number>;
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};
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type SemanticFitAssessment = {
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label: "lost_project_facet" | "lost_source_facet" | "strong" | "weak";
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matchedProjectStems: string[];
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matchedSourceStems: string[];
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missingProjectStems: string[];
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missingSourceStems: string[];
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projectCoverage: number | null;
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score: number;
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sourceCoverage: number | null;
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};
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function stemKeywordToken(token: string) {
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const normalizedToken = token.toLocaleLowerCase("ru-RU").replace(/ё/g, "е");
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@ -318,14 +305,62 @@ function hasSemanticFacetLoss(item: KeywordCleaningItem, guard: SemanticFacetGua
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return getMissingSemanticFacetStems(item, guard).length > 0;
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}
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function missesDominantProjectFacet(item: KeywordCleaningItem, guard: SemanticFacetGuard) {
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if (guard.dominantProtectedStems.size === 0) {
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return false;
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function getCoverage(matchedCount: number, totalCount: number) {
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return totalCount > 0 ? matchedCount / totalCount : null;
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}
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function getSemanticFitAssessment(item: KeywordCleaningItem, guard: SemanticFacetGuard): SemanticFitAssessment {
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const phraseStems = getKeywordFacetStems(item.phrase);
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const sourceStems = item.source === "wordstat_related" ? getProtectedSourceFacetStems(item, guard) : new Set<string>();
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const projectStems = guard.dominantProtectedStems;
|
||||
const matchedSourceStems = [...sourceStems].filter((stem) => phraseStems.has(stem));
|
||||
const matchedProjectStems = [...projectStems].filter((stem) => phraseStems.has(stem));
|
||||
const missingSourceStems = [...sourceStems].filter((stem) => !phraseStems.has(stem));
|
||||
const missingProjectStems = [...projectStems].filter((stem) => !phraseStems.has(stem));
|
||||
const sourceCoverage = getCoverage(matchedSourceStems.length, sourceStems.size);
|
||||
const projectCoverage = getCoverage(matchedProjectStems.length, projectStems.size);
|
||||
const score = Math.min(sourceCoverage ?? 1, projectCoverage ?? 1);
|
||||
|
||||
if (sourceCoverage === 0 && sourceStems.size > 0) {
|
||||
return {
|
||||
label: "lost_source_facet",
|
||||
matchedProjectStems,
|
||||
matchedSourceStems,
|
||||
missingProjectStems,
|
||||
missingSourceStems,
|
||||
projectCoverage,
|
||||
score,
|
||||
sourceCoverage
|
||||
};
|
||||
}
|
||||
|
||||
const phraseStems = getKeywordFacetStems(item.phrase);
|
||||
if (projectCoverage === 0 && projectStems.size > 0) {
|
||||
return {
|
||||
label: "lost_project_facet",
|
||||
matchedProjectStems,
|
||||
matchedSourceStems,
|
||||
missingProjectStems,
|
||||
missingSourceStems,
|
||||
projectCoverage,
|
||||
score,
|
||||
sourceCoverage
|
||||
};
|
||||
}
|
||||
|
||||
return [...guard.dominantProtectedStems].every((stem) => !phraseStems.has(stem));
|
||||
return {
|
||||
label: score >= 0.5 ? "strong" : "weak",
|
||||
matchedProjectStems,
|
||||
matchedSourceStems,
|
||||
missingProjectStems,
|
||||
missingSourceStems,
|
||||
projectCoverage,
|
||||
score,
|
||||
sourceCoverage
|
||||
};
|
||||
}
|
||||
|
||||
function missesDominantProjectFacet(item: KeywordCleaningItem, guard: SemanticFacetGuard) {
|
||||
return getSemanticFitAssessment(item, guard).label === "lost_project_facet";
|
||||
}
|
||||
|
||||
function getKeywordPriorityWeight(priority: KeywordCleaningItem["priority"]) {
|
||||
|
|
@ -335,13 +370,16 @@ function getKeywordPriorityWeight(priority: KeywordCleaningItem["priority"]) {
|
|||
}
|
||||
|
||||
function canModelRouteKeyword(item: KeywordCleaningItem, guard?: SemanticFacetGuard) {
|
||||
const semanticFit = guard ? getSemanticFitAssessment(item, guard) : null;
|
||||
|
||||
return (
|
||||
(item.decision === "use" || item.decision === "support") &&
|
||||
item.evidenceStatus === "collected" &&
|
||||
item.frequency !== null &&
|
||||
Boolean(item.projectOntologyVersionId) &&
|
||||
Boolean(item.wordstatResultId) &&
|
||||
Boolean(item.targetPath) &&
|
||||
(!guard || (!hasSemanticFacetLoss(item, guard) && !missesDominantProjectFacet(item, guard)))
|
||||
(!semanticFit || (semanticFit.label === "strong" && semanticFit.score >= 0.5))
|
||||
);
|
||||
}
|
||||
|
||||
|
|
@ -374,14 +412,14 @@ function getKeywordRole(item: KeywordCleaningItem, indexInTarget: number, guard:
|
|||
return "support";
|
||||
}
|
||||
|
||||
if (indexInTarget <= 7 && (item.priority === "high" || item.priority === "medium") && item.decision !== "article") {
|
||||
return "secondary";
|
||||
}
|
||||
|
||||
if (isClarifyingKeywordCandidate(item)) {
|
||||
return "differentiator";
|
||||
}
|
||||
|
||||
if (indexInTarget <= 7 && (item.priority === "high" || item.priority === "medium") && item.decision !== "article") {
|
||||
return "secondary";
|
||||
}
|
||||
|
||||
if (item.priority === "high" || item.priority === "medium") {
|
||||
return "secondary";
|
||||
}
|
||||
|
|
@ -439,6 +477,13 @@ function buildItems(contract: MarketEnrichmentContract, cleaning: KeywordCleanin
|
|||
return rightPriorityWeight - leftPriorityWeight;
|
||||
}
|
||||
|
||||
const leftSemanticFit = getSemanticFitAssessment(left, semanticFacetGuard).score;
|
||||
const rightSemanticFit = getSemanticFitAssessment(right, semanticFacetGuard).score;
|
||||
|
||||
if (leftSemanticFit !== rightSemanticFit) {
|
||||
return rightSemanticFit - leftSemanticFit;
|
||||
}
|
||||
|
||||
const leftFrequency = left.frequency ?? -1;
|
||||
const rightFrequency = right.frequency ?? -1;
|
||||
|
||||
|
|
@ -514,15 +559,15 @@ function getKeywordMapItemReason(
|
|||
}
|
||||
|
||||
if (role === "validate" && hasSemanticFacetLoss(item, semanticFacetGuard)) {
|
||||
return "Wordstat related потерял доминантный признак исходного якоря: оставляем на ручной разбор, чтобы широкий спрос не вытеснил смысл проекта.";
|
||||
const semanticFit = getSemanticFitAssessment(item, semanticFacetGuard);
|
||||
|
||||
return `Wordstat related потерял protected-признак исходного якоря (${semanticFit.missingSourceStems.join(", ")}): оставляем на ручной разбор, чтобы широкий спрос не вытеснил смысл проекта.`;
|
||||
}
|
||||
|
||||
if (role === "validate" && missesDominantProjectFacet(item, semanticFacetGuard)) {
|
||||
return "Фраза не содержит доминантный смысл из подтверждённой очереди якорей: оставляем на ручной разбор, чтобы широкий спрос не стал ядром автоматически.";
|
||||
}
|
||||
const semanticFit = getSemanticFitAssessment(item, semanticFacetGuard);
|
||||
|
||||
if (isBroadArticleBacklogPhrase(item.phrase) && role === "validate") {
|
||||
return "Широкая head-фраза уходит в article/backlog: не делаем её прямой основной ставкой текущей посадочной без отдельного SERP page-type решения.";
|
||||
return `Фраза не содержит доминантный protected-смысл из подтверждённой очереди (${semanticFit.missingProjectStems.join(", ")}): оставляем на ручной разбор, чтобы широкий спрос не стал ядром автоматически.`;
|
||||
}
|
||||
|
||||
if (item.decision === "article") {
|
||||
|
|
|
|||
Loading…
Reference in New Issue