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Snowflake&body=${pageURL}`;\r\nwindow.open(shareURL);\r\n}\r\n\r\n\r\n\r\n\r\n\r\n});",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false}},":itemsOrder":["markup_editor"]}},":itemsOrder":["container_949147658"]},"cq:LiveSyncConfig":{"cq:isDeep":true,"cq:rolloutConfigs":[],"cq:master":"/content/experience-fragments/snowflake-site/language-masters/it/site/share-icons/share-icons",":type":"cq:LiveCopy"}},":itemsOrder":["root","cq:LiveSyncConfig"],"classNames":"aem-xf","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"}},":itemsOrder":["text","experiencefragment"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"fundamentals-main-content",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-large",":items":{"container_copy":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"overview",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-923429e7a4","additionalClasses":"headline-decoration","type":"heading2","lines":["Presentazione"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-f212085f29","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EL’elaborazione del linguaggio naturale (NLP) è uno dei principali modi in cui l’intelligenza artificiale interagisce con il mondo. È la tecnologia che consente alle macchine di interpretare la comunicazione umana, espressa come testo o lingua parlata, e trasformarla in azione. È alla base degli strumenti che le aziende utilizzano ogni giorno, inclusi chatbot, assistenti virtuali, app di traduzione e strumenti di analisi del sentiment. Aiutando sistemi come questi a comprendere il linguaggio umano, l’elaborazione del linguaggio naturale consente alle organizzazioni di estrarre insight da dati disordinati e non strutturati e creare esperienze più naturali per le persone.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"]},"container_copy_copy_":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"container_copy_copy__480869401":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__373061683":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1444615495":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__222865509":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1413360324":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__455068363":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__659376865":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1500909857":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1978652079":"aem-GridColumn aem-GridColumn--default--12","container_277802961":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"rel",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"container_copy_copy":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"what",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-44a3c4e715","additionalClasses":"headline-decoration","type":"heading2","lines":["Cos’è l’elaborazione del linguaggio naturale (NLP)? "],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-5a71c7af3c","text":"\u003Cp\u003ENLP è il ramo dell’\u003Ca href=\"https://www.snowflake.com/it/product/ai/\"\u003EAI\u003C/a\u003E che consente ai computer di leggere, comprendere e generare il linguaggio umano. Unisce linguistica e \u003Ca href=\"https://www.snowflake.com/en/fundamentals/building-effective-machine-learning-pipelines/\"\u003Emachine learning\u003C/a\u003E per analizzare testo o linguaggio parlato, trovare schemi e rispondere in modi adatti al contesto.\u003C/p\u003E\n\u003Cp\u003EQuesta tecnologia supporta un’ampia gamma di funzioni. Può ricavare il significato di \u003Ca href=\"https://www.snowflake.com/en/fundamentals/text-document-processing/\"\u003Edocumenti\u003C/a\u003E ed email, rilevare il sentiment nelle recensioni dei clienti, tradurre in più lingue e persino riconoscere e generare un discorso parlato. Queste funzioni sono estese a molte applicazioni aziendali quotidiane, per aiutare i team a gestire la comunicazione ed estrarre insight, migliorando inoltre le interazioni con i clienti.\u003C/p\u003E\n\u003Cp\u003EPer le organizzazioni, il valore dell’elaborazione del linguaggio naturale risiede nella capacità di lavorare su dati non strutturati. La maggior parte delle informazioni aziendali è intrappolata nel testo: ticket di assistenza, contratti, report, post sui social media e così via. L’elaborazione del linguaggio naturale rende utilizzabili questi tipi di dati. Aiuta le aziende a far emergere gli insight in modo più rapido, ad automatizzare le attività manuali e a creare metodi più naturali per interagire con i sistemi. Comprendere l’elaborazione NLP è fondamentale per utilizzare al meglio le grandi quantità di dati linguistici che scorrono attraverso ogni azienda.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"]},"container_copy_copy_":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"a",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-e6ea2d578d","additionalClasses":"headline-decoration","type":"heading2","lines":["Come funziona l’elaborazione del linguaggio naturale?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-8a63a62261","text":"\u003Cp\u003EL’elaborazione del linguaggio naturale avviene attraverso una serie di passaggi. In primo luogo, i sistemi suddividono il testo in parole o frammenti significativi chiamati “token”, spesso eliminando parole come “e”, “per” o “il”. I sistemi analizzano quindi i token, applicando tag alle parti del discorso (sostantivo, verbo, aggettivo) e mappando le relazioni grammaticali. In tal modo si gettano le basi per la comprensione.\u003C/p\u003E\n\u003Cp\u003EAl termine di questo processo, l’elaborazione NLP passa all’analisi semantica, in cui \u003Ca href=\"https://www.snowflake.com/en/fundamentals/large-language-model/\"\u003Emodelli\u003C/a\u003E addestrati su enormi quantità di testo cercano di comprendere il contesto. Un approccio molto diffuso è il word embedding, un processo che trasforma le parole in numeri in modo da catturarne le relazioni. Questo consente al modello di posizionare vicine parole con significati simili, ad esempio “re” vicino a “regina” o “Parigi” vicino a “Francia”.\u003C/p\u003E\n\u003Cp\u003EL’asse portante della moderna elaborazione del linguaggio naturale è un ramo del \u003Ca href=\"https://www.snowflake.com/en/fundamentals/feature-extraction-machine-learning/\"\u003Emachine learning\u003C/a\u003E denominato deep learning. I modelli più diffusi, come BERT e GPT, sono prima addestrati su enormi quantità di testo e quindi adattati per processi specifici, come riassumere articoli, rispondere a domande o scrivere in modo naturale. Questi modelli utilizzano un design definito transformer, che consente di comprendere non solo le singole parole, ma anche il modo in cui queste parole si connettono e danno senso a intere frasi.\u003C/p\u003E\n\u003Cp\u003EI presupposti per l’addestramento dei modelli sono due. Innanzitutto una grande quantità di dati per l’addestramento, come testo da libri, siti web e log di conversazioni, a volte etichettati specificamente per task come “identificare il sentiment” o “tradurre dall’inglese allo spagnolo”. In secondo luogo sono necessarie architetture di modelli complesse, in particolare le reti transformer, che consentono ai modelli di ponderare l’importanza di ogni parola in relazione alle altre.\u003C/p\u003E\n\u003Cp\u003EQuesta sequenza (tokenizzazione, analisi, interpretazione, modello) trasforma il testo o il discorso grezzo in insight pronti per l’uso.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"]},"container_copy_copy__1444615495":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"use",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-c5bb15c92b","additionalClasses":"headline-decoration","type":"heading2","lines":["Perché è importante l’elaborazione del linguaggio naturale?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-f7a7209a9e","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EL’elaborazione del linguaggio naturale cambia il modo in cui le aziende gestiscono il linguaggio utilizzato in messaggi, recensioni, chat e altri formati, consentendo di trasformarlo in azione. Pone le basi per strumenti più intelligenti, rende scalabili le operazioni e offre agli utenti un’esperienza più fluida e umana.\u003C/p\u003E\n\u003Cp\u003EPensiamo al servizio clienti. L’elaborazione del linguaggio naturale consente la creazione di chatbot e voice bot disponibili 24 ore su 24 per rispondere a domande semplici. Questi bot sembrano più naturali e alleggeriscono il lavoro dei team di assistenza, riducendo i costi e liberando agenti per la gestione dei problemi più complessi. Quando gli strumenti vocali sono in grado di rilevare dal tono sentimenti come frustrazione o confusione, possono contrassegnare rapidamente i casi specifici come ad alta priorità.\u003C/p\u003E\n\u003Cp\u003ESi deve poi considerare l’estrazione di insight. L’elaborazione del linguaggio naturale non si limita a leggere i feedback, ma rileva e misura anche il sentiment. I marchi possono quindi monitorare cosa pensano le persone in tempo reale, segnalando i complimenti e risolvendo i problemi prima che si aggravino rapidamente. I team possono così reagire più rapidamente e definire la strategia con maggiore precisione.\u003C/p\u003E\n\u003Cp\u003EPiù in generale, l’elaborazione NLP aiuta le aziende , ad esempio, a elaborare il testo non strutturato di email, trascrizioni di chiamate e conversazioni sui social senza tagging manuale. Trasforma queste informazioni in risultati chiari, aiutando i team ad automatizzare task come la creazione di riepiloghi delle chiamate o l’ordinamento delle richieste, ed estrae in pochi secondi insight chiave da documenti molto lunghi.\u003C/p\u003E\n\u003Cp\u003EIn breve, l’elaborazione del linguaggio naturale converte un’enorme quantità di dati basati sul linguaggio normalmente disordinati in segnali più chiari e fruibili. Aiuta i team a lavorare in modo più efficiente e a rispondere più rapidamente, oltre a offrire agli utenti interfacce che utilizzano il linguaggio quotidiano invece del gergo tecnico.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"]},"container_copy_copy__1978652079":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"benefits",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-8436b49b13","additionalClasses":"headline-decoration","type":"heading2","lines":["Tecniche di NLP"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-0b39e92aae","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EL’elaborazione NLP si basa su una serie di tecniche fondamentali che analizzano il linguaggio e lo rendono gestibile. Ciascuna tecnica svolge un ruolo diverso nel trasformare la comunicazione testuale in insight fruibili.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003ETokenizzazione\u003C/h4\u003E\n\u003Cp\u003ELa tokenizzazione è il processo di suddivisione del testo in parti più piccole, come parole o frasi, in modo che il sistema possa iniziare a comprenderlo. \u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003ELemmatizzazione e stemming\u003C/h4\u003E\n\u003Cp\u003EQueste tecniche riducono le parole a forme di base. Lo stemming accorcia le parole tagliando la parte finale. Ad esempio, “correre” potrebbe diventare “corr”. La lemmatizzazione utilizza regole del linguaggio o dizionari per restituire la forma base delle parole, ad esempio trasformando “migliore” in “buono”.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EAssegnazione di tag alle parti del discorso\u003C/h4\u003E\n\u003Cp\u003EIn questo caso il sistema assegna un tag a ciascuna parola, sostantivo, verbo, aggettivo e così via, contrassegnando le relazioni grammaticali delle parole. Aiuta così a interpretare il significato nel contesto, ad esempio a specificare se “guida” è un sostantivo o un verbo.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003ENamed entity recognition (NER)\u003C/h4\u003E\n\u003Cp\u003ELa NER individua i nomi propri nel testo: persone, luoghi, organizzazioni, date. Li contrassegna e li inserisce in categorie, in modo che gli utenti possano estrarre chi, dove, quando e cosa senza cercare manualmente.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EAnalisi delle dipendenze\u003C/h4\u003E\n\u003Cp\u003EQuesta tecnica mappa le relazioni grammaticali, ad esempio chi ha fatto cosa a chi. Nella pratica suddivide una frase nelle sue parti (soggetto, verbo e oggetto) in modo che il sistema possa comprenderne la struttura.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EAlgoritmi di analisi del sentiment\u003C/h4\u003E\n\u003Cp\u003EQuesti algoritmi classificano le emozioni espresse dal testo come positive, negative o neutre. I marchi li utilizzano per leggere recensioni o chat sui social media e farsi un’idea del tono generale.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EMachine translation\u003C/h4\u003E\n\u003Cp\u003ESono sistemi che trasformano il testo da una lingua all’altra. Alcuni seguono regole specifiche, altri si basano su statistiche e i più recenti utilizzano le reti neurali. Consentono ad app e servizi globali di supportare molte lingue senza dover creare un sistema separato per ciascuna lingua.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"]},"container_copy_copy__455068363":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"how",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-63a728be9d","additionalClasses":"headline-decoration","type":"heading2","lines":["Che cosa sono i task NLP?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-3a29526dfb","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EI task NLP sono i processi pratici che i modelli linguistici eseguono una volta elaborato il testo. Mappano tecniche specifiche in base alle reali esigenze aziendali, dalla comprensione dei feedback dei clienti all’abilitazione della comunicazione globale. Suddividendo i problemi linguistici in task distinti, le organizzazioni possono scegliere l’approccio appropriato per i propri obiettivi, che si tratti di mettere in ordine i ticket di assistenza o di creare un assistente alla ricerca.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EClassificazione del testo\u003C/h4\u003E\n\u003Cp\u003EQuesto task ordina il testo in categorie. Un’azienda ha la possibilità di classificare le email in arrivo come “supporto”, “fatturazione” o “vendite”, oppure di filtrare i documenti in base all’argomento. Aiuta i team a gestire automaticamente grandi volumi di testo invece di basarsi sull’assegnazione manuale dei tag.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003ENamed entity recognition (NER)\u003C/h4\u003E\n\u003Cp\u003EQuesta funzionalità identifica ed estrae le entità chiave del testo. Ad esempio, un team responsabile della conformità può analizzare i contratti per individuare nomi delle aziende e date oppure un aggregatore di notizie può indicizzare le storie in base ai luoghi e ai soggetti chiave.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EAnalisi del sentiment\u003C/h4\u003E\n\u003Cp\u003EQuesto task rileva il tono nel linguaggio utilizzato. Fornisce ai team \u003Ca href=\"https://www.snowflake.com/it/solutions/departments/marketing/\"\u003Emarketing\u003C/a\u003E e customer experience una lettura rapida delle reazioni del pubblico al lancio di un prodotto, dell’interazione con un servizio o della presenza di un marchio, spesso sui social media.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EMachine translation\u003C/h4\u003E\n\u003Cp\u003EI sistemi di traduzione convertono il testo da una lingua all’altra. I moderni metodi neurali catturano il contesto e le sfumature, consentendo ad aziende di tutto il mondo di supportare gli utenti nelle loro lingue native, senza creare contenuti da zero per ciascuna lingua.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003ERiepilogo del testo\u003C/h4\u003E\n\u003Cp\u003EQuesto task condensa grandi blocchi di testo in versioni più brevi e gestibili. È utile per dirigenti e manager che necessitano di un briefing su lunghi report o per le app che generano riassunti di notizie da articoli più lunghi.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003ESistemi di risposta alle domande\u003C/h4\u003E\n\u003Cp\u003EI sistemi di controllo qualità consentono agli utenti di porre domande in linguaggio naturale e ricevere risposte dirette. Consentono a chatbot, assistenti vocali e strumenti di ricerca aziendale di restituire il fatto o il passaggio più rilevante invece di un lungo elenco di documenti.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"]},"container_copy_copy__222865509":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"ai",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-45c0ca4d24","additionalClasses":"headline-decoration","type":"heading2","lines":["Vantaggi dell’uso dell’elaborazione del linguaggio naturale"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-d28988cb1b","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EEcco i quattro vantaggi essenziali dell’utilizzo dell’elaborazione NLP. Ognuno di essi aiuta le aziende consentendo scalabilità più intelligente, reazioni più rapide e posizionamento sempre all’avanguardia.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EInsight più rapidi da dati non strutturati\u003C/h4\u003E\n\u003Cp\u003ELa maggior parte dei dati aziendali risiede in email, log delle chat, recensioni dei clienti, trascrizioni e simili. L’elaborazione del linguaggio naturale estrae gli insight dal testo in tempo reale e su vasta scala, senza l’applicazione manuale di tag o congetture. Di conseguenza, i team riescono a individuare trend, rischi o opportunità in pochi minuti anziché in giorni.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EMaggiore coinvolgimento dei clienti grazie all’automazione\u003C/h4\u003E\n\u003Cp\u003EL’elaborazione NLP consente a chatbot, agenti virtuali e strumenti di supporto automatizzati di gestire domande di routine in qualsiasi momento della giornata, senza far attendere i clienti. In tal modo il personale dell’azienda può concentrarsi su questioni più business-critical e offrire esperienze più rapide e fluide in ogni touchpoint.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EMaggiore precisione dei processi decisionali\u003C/h4\u003E\n\u003Cp\u003ECon la classificazione di emozioni, temi e intenti all’interno del testo, l’elaborazione del linguaggio naturale aiuta a far emergere segnali più profondi proprio quando sono più importanti. Che si tratti di cosa pensano i clienti di una nuova funzione, di quali argomenti dominano i feedback o di quali domande continuano a comparire nelle chat dell’assistenza, l’elaborazione NLP aumenta la precisione degli insight e aiuta i leader ad agire sui problemi reali.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EScalabilità su set di dati di grandi dimensioni\u003C/h4\u003E\n\u003Cp\u003EL’NLP automatizza le attività linguistiche che altrimenti sovraccaricherebbero i team. Può riassumere lunghi documenti, estrarre dettagli importanti dai contratti o esaminare migliaia di ticket di assistenza in modo quasi immediato. Diventa quindi possibile scalare le operazioni senza un aumento vertiginoso dell’organico o la creazione di un backlog di dati.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"]},"container_copy_copy__1413360324":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"tools",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2_copy":{"id":"title-v2-e0aa1712a0","additionalClasses":"headline-decoration","type":"heading2","lines":["Sfide dell’elaborazione del linguaggio naturale"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-fa4dd7d0b3","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003ESe da un lato l’elaborazione NLP apre le porte all’automazione e agli insight, dall’altro la sua applicazione trova alcuni ostacoli. Ecco alcune delle sfide più comuni relative all’elaborazione del linguaggio naturale e come le organizzazioni possono affrontarle.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EGestione di dati ambigui o irrilevanti\u003C/h4\u003E\n\u003Cp\u003EIl linguaggio umano è disordinato. Errori ortografici, gergo, sarcasmo e terminologie specifiche dei diversi settori possono far confondere i modelli. Anche il testo chiaro e organizzato ha spesso significati diversi a seconda del contesto. Il metodo di gestione più efficace consiste nell’utilizzare data set di addestramento ampi e diversificati e consentire il fine‐tuning continuo dei modelli con esempi specifici per i diversi settori, in modo che il modello apprenda come le persone parlano davvero in un determinato contesto aziendale.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EGestione di contenuti in più lingue\u003C/h4\u003E\n\u003Cp\u003ELe aziende globali devono gestire le comunicazioni in molte lingue, dialetti e variazioni culturali. Una frase in inglese può non essere traducibile direttamente in mandarino o spagnolo. La machine translation moderna basata su reti neurali è utile, ma richiede un training continuo e un’attenta valutazione. Molti team combinano modelli di traduzione predefiniti con fine-tuning interno, per coprire termini di nicchia o terminologia specifica del settore.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EGarantire privacy e compliance\u003C/h4\u003E\n\u003Cp\u003EL’elaborazione NLP viene spesso utilizzata per elaborare dati sensibili, sollevando potenzialmente problemi di conformità. Le normative stabiliscono requisiti rigorosi per la gestione di questo tipo di dati. Le organizzazioni devono utilizzare solide misure di garanzia: \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-anonymization/\"\u003Eanonimizzazione\u003C/a\u003E, crittografia e audit trail che mostrino come vengono elaborate e archiviate le informazioni.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EAffrontare i problemi di bias dei modelli e di equità\u003C/h4\u003E\n\u003Cp\u003EPoiché apprendono dal linguaggio umano, i modelli NLP possono assorbire anche i bias umani. Questo può produrre output discriminatori o offensivi in applicazioni come gli strumenti utilizzati per la selezione del personale o i chatbot. La risoluzione dei bias inizia con dati di addestramento diversificati e monitoraggio continuo. Le aziende devono inoltre implementare processi di revisione per individuare output non previsti e adeguare i modelli prima di scalarli.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2_copy","text_copy"]},"container_277802961":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text_copy_copy":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"faced",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{"title_v2_copy_copy":{"id":"title-v2-40e1b9b32c","additionalClasses":"headline-decoration","type":"heading2","lines":["Esempi e casi d’uso dell’elaborazione del linguaggio naturale"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy_copy":{"id":"text-d9a5168ab6","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EDi seguito sono riportati cinque modi in cui le aziende applicano l’elaborazione del linguaggio naturale e perché questi casi d’uso sono importanti.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EAnalisi del sentiment dei social media\u003C/h4\u003E\n\u003Cp\u003EI marchi utilizzano l’elaborazione del linguaggio naturale per analizzare i sistemi di feedback online come social media, forum e recensioni per scoprire cosa pensano le persone in tempo reale. Questo consente ai team di reagire rapidamente ai cambiamenti nella percezione delle persone, che si tratti di un intoppo nelle pubbliche relazioni o di un picco improvviso di commenti positivi. L’NLP analizza il sentiment su vasta scala, in modo che i team umani non debbano analizzare ogni tweet o commento singolarmente.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EChatbot e assistenti virtuali\u003C/h4\u003E\n\u003Cp\u003EL’assistenza clienti viene ormai fornita quasi esclusivamente online e l’NLP facilita le operazioni. Chatbot e assistenti vocali leggono ciò che gli utenti digitano o dicono e reagiscono con risposte sensibili al contesto. Questo alleggerisce la pressione sugli agenti umani e fornisce ai clienti risposte rapide, senza tuttavia sembrare un’interazione robotizzata. Siri, Alexa e altri bot di supporto sul web utilizzano questi sistemi.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EServizi di traduzione linguistica\u003C/h4\u003E\n\u003Cp\u003EPer espandersi in nuovi mercati non è più necessario eseguire tutte le traduzioni a mano. I sistemi NLP, in particolare i sistemi di traduzione basati su reti neurali, traducono accuratamente i contenuti in tutte le lingue. Questo aiuta le aziende a localizzare le app, supportare la produzione di contenuti e testi pubblicitari e a farlo rapidamente, senza creare pipeline separate per le diverse aree linguistiche.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003ERiepilogo del testo\u003C/h4\u003E\n\u003Cp\u003ELunghi report, documenti tecnici o trascrizioni di riunioni? L’elaborazione del linguaggio naturale può estrarre i punti chiave e offrire un riepilogo conciso. Si risparmia tempo e si consente ai team di concentrarsi su attività aziendali più importanti.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003ENamed entity recognition (NER)\u003C/h4\u003E\n\u003Cp\u003ELa NER è in grado di individuare i riferimenti ai concorrenti, di seguire i nomi di chi sta parlando nelle trascrizioni delle chiamate o di estrarre le date di scadenza dai contratti. Offre contesto sulle informazioni con un lavoro manuale minimo.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2_copy_copy","text_copy_copy"]},"container_copy_copy__659376865":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"conclusion",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-4d11e04095","additionalClasses":"headline-decoration","type":"heading2","lines":["Conclusione"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-cef8a93fad","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EL’elaborazione del linguaggio naturale è il modo in cui l’AI comprende e utilizza il linguaggio umano. Consentendo ai computer di leggere, interpretare e generare il linguaggio, l’NLP è al centro delle moderne strategie di AI e analisi dei dati. Trasforma i dati grezzi e non strutturati in risorse che le organizzazioni possono utilizzare per decidere e agire.\u003C/p\u003E\n\u003Cp\u003ELe tecniche e i modelli alla base dell’NLP sono usciti dai laboratori di ricerca per entrare nell’uso aziendale quotidiano. Consentono alle aziende di lavorare più velocemente, prendere decisioni più informate e creare esperienze più naturali per chi le utilizza.\u003C/p\u003E\n\u003Cp\u003ENaturalmente, le sfide rimangono. Dati disorganizzati, l’uso di più lingue, requisiti di conformità e bias nei modelli possono influire sulla qualità dei risultati dell’NLP. Ma con l’approccio giusto, le organizzazioni possono superare questi ostacoli ottenendo vantaggi in termini di efficienza e scalabilità e insight più approfonditi.\u003C/p\u003E\n\u003Cp\u003EL’elaborazione del linguaggio naturale è più di una capacità tecnica. È un asset strategico, che consente alle aziende di comprendere il linguaggio su vasta scala e trasformarlo in azione. Con l’evoluzione continua dell’AI, chi investe in questo tipo di asset godrà di maggiori possibilità di innovare, competere e crescere.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"]},"container_copy_copy__1500909857":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","simple_snowflake_acc":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"faq",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-a0e5d2434b","additionalClasses":"headline-decoration","type":"heading2","lines":["Domande frequenti sull’elaborazione del linguaggio naturale (NLP)"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"simple_snowflake_acc":{"id":"simple-snowflake-accordion-4dbc24bf8a","additionalClasses":"list--blue-bullets","showDivider":false,"accordionItemsList":[{"title":"In che modo sono correlate elaborazione del linguaggio naturale e intelligenza artificiale?","richText":"\u003Cp\u003EL’NLP è un ramo dell’intelligenza artificiale. Mentre l’AI copre un’ampia gamma di tecniche per imitare l’intelligenza umana, l’NLP si concentra in modo specifico sul linguaggio, ossia sul modo in cui le macchine leggono, interpretano e generano testo o linguaggio. L’AI è il campo più vasto e l’elaborazione del linguaggio naturale è una delle sue applicazioni principali.\u003C/p\u003E\n"},{"title":"Come funziona l’elaborazione del linguaggio naturale con il machine learning?","richText":"\u003Cp\u003ELa maggior parte dei moderni sistemi NLP è basata sul machine learning. Invece di affidarsi solo a regole grammaticali scritte a mano, imparano da enormi data set di testo. Modelli come BERT e GPT utilizzano architetture di deep learning per comprendere il contesto e il significato e diventano sempre più efficaci per attività come la traduzione, la sintesi e la risposta alle domande.\u003C/p\u003E\n"},{"title":"Quali servizi sono disponibili per l’elaborazione del linguaggio naturale?","richText":"\u003Cp\u003EI cloud provider e i fornitori di software offrono servizi NLP che si possono collegare direttamente ai flussi di lavoro aziendali. Alcuni esempi sono API di \u003Ca href=\"https://www.snowflake.com/en/fundamentals/what-is-sentiment-analysis/\"\u003Eanalisi del sentiment\u003C/a\u003E, servizi di traduzione, strumenti di riconoscimento delle entità e piattaforme chatbot di provider come AWS, Google, IBM e Microsoft. Questi servizi consentono alle aziende di adottare rapidamente l’NLP senza creare tutto da zero.\u003C/p\u003E\n"},{"title":"Quali sono alcuni casi d’uso comuni di elaborazione del linguaggio naturale in ambito aziendale?","richText":"\u003Cp\u003ELe applicazioni aziendali dell’NLP spaziano dai chatbot dell’assistenza clienti e il monitoraggio dei social media, ai controlli di conformità, la revisione dei contratti e gli assistenti vocali. Ogni caso d’uso punta a trasformare il linguaggio non strutturato in insight strutturati di utilità pratica per i team.\u003C/p\u003E\n"}],":type":"snowflake-site/components/simple-snowflake-accordion","appliedCssClassNames":"snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","simple_snowflake_acc"]},"container_copy_copy__480869401":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"customer",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-b098e2948d","additionalClasses":"headline-decoration","type":"heading2","lines":["Clienti che utilizzano Snowflake per 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