Semantic understanding issues in natural language processing technology

Semantic understanding issues in natural language processing technology require specific code examples

Introduction:

With the rapid development of artificial intelligence, natural language Natural Language Processing (NLP) is widely used in many fields. Among them, semantic understanding is an important link in NLP. The purpose is to enable computers to understand the meaning of human language and give corresponding feedback. The key to semantic understanding is to extract the information contained in a piece of text and convert it into a form that a computer can process.

Problems with semantic understanding:
In semantic understanding, common problems include named entity recognition, sentiment analysis, semantic role annotation, etc. These problems can be solved with the help of some basic natural language processing technologies, including word segmentation, part-of-speech tagging, syntactic analysis, etc.

Code examples:

Two examples will be given below to show how to implement two common semantic understanding tasks through code.

Named Entity Recognition (NER):
The task of named entity recognition is to identify entities with specific meanings from text, such as names of people, places, organizations, etc. The following is a simple Python code example that shows how to extract named entities from a piece of text using NER technology.

import nltk
from nltk.chunk import ne_chunk

def named_entity_recognition(text):
    sentences = nltk.sent_tokenize(text)
    for sent in sentences:
        words = nltk.word_tokenize(sent)
        pos_tags = nltk.pos_tag(words)
        chunked = ne_chunk(pos_tags)
        for chunk in chunked:
            if hasattr(chunk, 'label') and chunk.label() == 'PERSON':
                print('Person:', ' '.join(c[0] for c in chunk))
            elif hasattr(chunk, 'label') and chunk.label() == 'GPE':
                print('Location:', ' '.join(c[0] for c in chunk))
            elif hasattr(chunk, 'label') and chunk.label() == 'ORGANIZATION':
                print('Organization:', ' '.join(c[0] for c in chunk))

text = "John Smith is from New York and works for Google."
named_entity_recognition(text)

Sentiment Analysis:
The task of sentiment analysis is to determine the emotional tendency in a text, such as determining whether an article is positive or negative, or whether a user's comment is Positive or negative. Below is a simple Python code example that shows how to perform sentiment analysis on text using sentiment analysis techniques.

from textblob import TextBlob

def sentiment_analysis(text):
    blob = TextBlob(text)
    polarity = blob.sentiment.polarity
    subjectivity = blob.sentiment.subjectivity
    if polarity > 0:
        sentiment = 'Positive'
    elif polarity < 0:
        sentiment = 'Negative'
    else:
        sentiment = 'Neutral'
    print('Sentiment:', sentiment)
    print('Subjectivity:', subjectivity)

text = "I love this movie! It's amazing!"
sentiment_analysis(text)

Summary:

Semantic understanding is a key link in natural language processing. By extracting information from text and converting it into a form that can be processed by the computer, the computer can understand the meaning of human language. This article illustrates how to use natural language processing technology to achieve semantic understanding tasks such as named entity recognition and sentiment analysis by showing two specific code examples. With the continuous advancement and development of technology, semantic understanding will be applied in more fields and provide strong support for the development of artificial intelligence.

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