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Dall’analisi del Tier 2 – una classificazione semantica specialistica incentrata su pattern lessicali strutturali per contenuti specialistici in italiano – emerge una limitazione cruciale: la categorizzazione temporale rimane spesso astratta o superficiale. L’estrazione automatica di espressioni temporali e la loro assegnazione a una gerarchia precisa – immediato, recente, storico, prospettico – richiede un approccio che superi il riconoscimento superficiale per catturare sfumature morfologiche, sintattiche e contestuali. Questo articolo presenta un processo dettagliato, passo dopo passo, per costruire un sistema di clustering semantico lessicale personalizzato, capace di identificare e classificare automaticamente sottotemi temporali in articoli Tier 2, con applicazione pratica, validazione rigorosa e ottimizzazioni avanzate per il contesto italiano. — 1. Contesto: Limiti del Tier 2 e necessità di un approccio sperimentale Il Tier 2 si distingue per l’uso di meta-pattern lessicali e strutturali che consentono una classificazione semantica fine, ma tende a trattare le espressioni temporali come elementi isolati, senza una gerarchizzazione semantica profonda. Nonostante il corpus Tier 2 contenga articoli con tematiche complesse – giuridiche, giornalistiche, accademiche – l’analisi temporale è spesso ridotta a label generiche come “recente” o “storico”, senza una suddivisione granulare che rifletta la dinamica temporale reale. Già il Tier 1, fondamento della classificazione semantica, ha stabilito principi di base basati su pattern lessicali e strutturali: espressioni avverbiali (“ieri”, “quest’anno”), locuzioni temporali (“nella settimana scorsa”, “nel 2023”), indicatori di durata (“per due giorni”, “da ieri a oggi”) costituiscono la base. Il Tier 3, estendendo questa logica, introduce il clustering semantico per sottotemi temporali, ma rimane focalizzato su applicazioni generiche. L’esatta definizione di una gerarchia temporale italiana – immediato (giorno presente), recente (ultimi giorni/settimane), storico (periodi definiti), prospettico (anticipazioni future) – richiede un sistema capace di interpretare non solo la presenza di espressioni, ma anche la loro intensità, contesto sintattico e ambiguità semantica, che il Tier 2 non affronta in profondità. — 2. Estrazione e Classificazione Automatica delle Espressioni Temporali L’esatto riconoscimento di sottotemi temporali in articoli Tier 2 richiede un’architettura di elaborazione testuale multilivello, integrata con regole linguistiche e analisi contestuale. **Fase 1: Raccolta e Annotazione del Corpus Tier 2** Si parte da un corpus rappresentativo di almeno 500 articoli Tier 2, provenienti da settori come giornalismo, diritto e accademia. Ogni documento viene annotato manualmente da linguisti specializzati, marcando espressioni temporali e assegnandole a una delle quattro categorie gerarchiche: – **Immediato**: “ieri”, “oggi”, “adesso” – **Recente**: “nella settimana scorsa”, “ultimi sei mesi”, “aggiornato a settembre” – **Storico**: “nel 2020”, “un anno fa”, “epoca del dopoguerra” – **Prospettico**: “tra due settimane”, “entro la fine del trimestre”, “si prevede nel 2025” La qualità dell’annotazione viene validata con accordo inter-annotatore (Kappa ≥ 0.85), garantendo affidabilità semantica. **Fase 2: Costruzione del Lessico Esteso con Pesi Contestuali** Dal corpus annotato si estrae un lessico italiano di espressioni temporali, arricchito con: – **Frequenze d’uso** per lingua – **Collocuzioni tipiche** (es. “prima di”, “dopo che”, “in seguito a”) – **Indicatori sintattici** (avverbi, locuzioni, marcatori temporali) – **Pesi contestuali** derivati da contesto d’uso e intensità temporale (es. “ieri” ha peso più alto di “recente” […]

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