Rileva gli elementi del documento con Amazon Comprehend e un AWS SDK

L'esempio di codice seguente mostra come:

• Rileva lingue, entità e frasi chiave in un documento.

• Rileva informazioni di identificazione personale (PII) in un documento.

• Rileva il sentimento di un documento.

• Rileva gli elementi della sintassi in un documento.

Python

SDKper Python (Boto3)

Nota

C'è di più su. GitHub Trova l'esempio completo e scopri di più sulla configurazione e l'esecuzione nel Repository di esempi di codice AWS.

Crea una classe che racchiuda le azioni di Amazon Comprehend.

import logging
from pprint import pprint
import boto3
from botocore.exceptions import ClientError

logger = logging.getLogger(__name__)

class ComprehendDetect:
    """Encapsulates Comprehend detection functions."""

    def __init__(self, comprehend_client):
        """
        :param comprehend_client: A Boto3 Comprehend client.
        """
        self.comprehend_client = comprehend_client


    def detect_languages(self, text):
        """
        Detects languages used in a document.

        :param text: The document to inspect.
        :return: The list of languages along with their confidence scores.
        """
        try:
            response = self.comprehend_client.detect_dominant_language(Text=text)
            languages = response["Languages"]
            logger.info("Detected %s languages.", len(languages))
        except ClientError:
            logger.exception("Couldn't detect languages.")
            raise
        else:
            return languages


    def detect_entities(self, text, language_code):
        """
        Detects entities in a document. Entities can be things like people and places
        or other common terms.

        :param text: The document to inspect.
        :param language_code: The language of the document.
        :return: The list of entities along with their confidence scores.
        """
        try:
            response = self.comprehend_client.detect_entities(
                Text=text, LanguageCode=language_code
            )
            entities = response["Entities"]
            logger.info("Detected %s entities.", len(entities))
        except ClientError:
            logger.exception("Couldn't detect entities.")
            raise
        else:
            return entities


    def detect_key_phrases(self, text, language_code):
        """
        Detects key phrases in a document. A key phrase is typically a noun and its
        modifiers.

        :param text: The document to inspect.
        :param language_code: The language of the document.
        :return: The list of key phrases along with their confidence scores.
        """
        try:
            response = self.comprehend_client.detect_key_phrases(
                Text=text, LanguageCode=language_code
            )
            phrases = response["KeyPhrases"]
            logger.info("Detected %s phrases.", len(phrases))
        except ClientError:
            logger.exception("Couldn't detect phrases.")
            raise
        else:
            return phrases


    def detect_pii(self, text, language_code):
        """
        Detects personally identifiable information (PII) in a document. PII can be
        things like names, account numbers, or addresses.

        :param text: The document to inspect.
        :param language_code: The language of the document.
        :return: The list of PII entities along with their confidence scores.
        """
        try:
            response = self.comprehend_client.detect_pii_entities(
                Text=text, LanguageCode=language_code
            )
            entities = response["Entities"]
            logger.info("Detected %s PII entities.", len(entities))
        except ClientError:
            logger.exception("Couldn't detect PII entities.")
            raise
        else:
            return entities


    def detect_sentiment(self, text, language_code):
        """
        Detects the overall sentiment expressed in a document. Sentiment can
        be positive, negative, neutral, or a mixture.

        :param text: The document to inspect.
        :param language_code: The language of the document.
        :return: The sentiments along with their confidence scores.
        """
        try:
            response = self.comprehend_client.detect_sentiment(
                Text=text, LanguageCode=language_code
            )
            logger.info("Detected primary sentiment %s.", response["Sentiment"])
        except ClientError:
            logger.exception("Couldn't detect sentiment.")
            raise
        else:
            return response


    def detect_syntax(self, text, language_code):
        """
        Detects syntactical elements of a document. Syntax tokens are portions of
        text along with their use as parts of speech, such as nouns, verbs, and
        interjections.

        :param text: The document to inspect.
        :param language_code: The language of the document.
        :return: The list of syntax tokens along with their confidence scores.
        """
        try:
            response = self.comprehend_client.detect_syntax(
                Text=text, LanguageCode=language_code
            )
            tokens = response["SyntaxTokens"]
            logger.info("Detected %s syntax tokens.", len(tokens))
        except ClientError:
            logger.exception("Couldn't detect syntax.")
            raise
        else:
            return tokens

Richiama le funzioni sulla classe wrapper per rilevare entità, frasi e altro in un documento.

def usage_demo():
    print("-" * 88)
    print("Welcome to the Amazon Comprehend detection demo!")
    print("-" * 88)

    logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")

    comp_detect = ComprehendDetect(boto3.client("comprehend"))
    with open("detect_sample.txt") as sample_file:
        sample_text = sample_file.read()

    demo_size = 3

    print("Sample text used for this demo:")
    print("-" * 88)
    print(sample_text)
    print("-" * 88)

    print("Detecting languages.")
    languages = comp_detect.detect_languages(sample_text)
    pprint(languages)
    lang_code = languages[0]["LanguageCode"]

    print("Detecting entities.")
    entities = comp_detect.detect_entities(sample_text, lang_code)
    print(f"The first {demo_size} are:")
    pprint(entities[:demo_size])

    print("Detecting key phrases.")
    phrases = comp_detect.detect_key_phrases(sample_text, lang_code)
    print(f"The first {demo_size} are:")
    pprint(phrases[:demo_size])

    print("Detecting personally identifiable information (PII).")
    pii_entities = comp_detect.detect_pii(sample_text, lang_code)
    print(f"The first {demo_size} are:")
    pprint(pii_entities[:demo_size])

    print("Detecting sentiment.")
    sentiment = comp_detect.detect_sentiment(sample_text, lang_code)
    print(f"Sentiment: {sentiment['Sentiment']}")
    print("SentimentScore:")
    pprint(sentiment["SentimentScore"])

    print("Detecting syntax elements.")
    syntax_tokens = comp_detect.detect_syntax(sample_text, lang_code)
    print(f"The first {demo_size} are:")
    pprint(syntax_tokens[:demo_size])

    print("Thanks for watching!")
    print("-" * 88)

• Per API i dettagli, consulta i seguenti argomenti in AWS SDKPython (Boto3) Reference. API

  • DetectDominantLanguage

  • DetectEntities

  • DetectKeyPhrases

  • DetectPiiEntities

  • DetectSentiment

  • DetectSyntax

Per un elenco completo delle guide per gli AWS SDK sviluppatori e degli esempi di codice, consulta. Utilizzo di Amazon Comprehend con un SDK AWS Questo argomento include anche informazioni su come iniziare e dettagli sulle SDK versioni precedenti.