”工欲善其事,必先利其器。“—孔子《论语.录灵公》
首页 > 编程 > 如何创建人类水平的自然语言理解 (NLU) 系统

如何创建人类水平的自然语言理解 (NLU) 系统

发布于2024-11-05
浏览:359

How to create a Human-Level Natural Language Understanding (NLU) System

Scope: Creating an NLU system that fully understands and processes human languages in a wide range of contexts, from conversations to literature.

Challenges:

  • Natural language is highly ambiguous, so creating models that resolve meaning in context is complex.
  • Developing models for multiple languages and dialects.
  • Ensuring systems understand cultural nuances, idiomatic expressions, and emotions.
  • Training on massive datasets and ensuring high accuracy.

To create a Natural Language Understanding (NLU) system that fully comprehends and processes human languages across contexts, the design process needs to tackle both the theoretical and practical challenges of language, context, and computing. Here's a thinking process that can guide the development of such a system:

1. Understanding the Problem: Scope and Requirements

  • Define Objectives: Break down what "understanding" means in various contexts. Does the system need to understand conversation, literature, legal text, etc.?
  • Identify Use Cases: Specify where the NLU will be applied (e.g., conversational agents, content analysis, or text-based decision-making).
  • Establish Constraints: Determine what resources are available, what level of accuracy is required, and what trade-offs will be acceptable (speed vs. accuracy, for instance).

    2. Data Collection: Building the Knowledge Base

  • Multilingual and Multidomain Corpora: Collect vast amounts of text from multiple languages and various domains like literature, technical writing, legal documents, informal text (e.g., tweets), and conversational transcripts.

  • Contextual Data: Language is understood in context. Collect meta-data such as the speaker's background, time period, cultural markers, sentiment, and tone.

  • Annotations: Manually annotate datasets with syntactic, semantic, and pragmatic information to train the system on ambiguity, idioms, and context.

    3. Developing a Theoretical Framework

  • Contextual Language Models: Leverage transformer models like GPT, BERT, or even specialized models like mBERT (multilingual BERT) for handling context-specific word embeddings. Incorporate memory networks or long-term dependencies so the system can remember previous conversations or earlier parts of a text.

  • Language and Culture Modeling: Transfer Learning: Use transfer learning to apply models trained on one language or context to another. For instance, a model trained on English literature can help understand the structure of French literature with proper fine-tuning.

  • Cross-Language Embeddings: Utilize models that map words and phrases into a shared semantic space, enabling the system to handle multiple languages at once.

  • Cultural and Emotional Sensitivity: Create sub-models or specialized attention layers to detect cultural references, emotions, and sentiment from specific regions or contexts.

4. Addressing Ambiguity and Pragmatic Understanding

  • Disambiguation Mechanisms: Supervised Learning: Train the model on ambiguous sentences (e.g., "bank" meaning a financial institution vs. a riverbank) and provide annotated resolutions.
  • Contextual Resolution: Use attention mechanisms to give more weight to recent conversational or textual context when interpreting ambiguous words.
  • Pragmatics and Speech Acts: Build a framework for pragmatic understanding (i.e., not just what is said but what is meant). Speech acts, like promises, requests, or questions, can be modeled using reinforcement learning to better understand intentions.

    5. Dealing with Idioms and Complex Expressions

  • Idiom Recognition: Collect idiomatic expressions from multiple languages and cultures. Train the model to recognize idioms not as compositional phrases but as whole entities with specific meanings. Apply pattern-matching techniques to identify idiomatic usage in real-time.

  • Metaphor and Humor Detection: Create sub-networks trained on metaphors and humor. Use unsupervised learning to detect non-literal language and assign alternative interpretations.

    6. Handling Large Datasets and Model Training

  • Data Augmentation: Leverage techniques like back-translation (translating data to another language and back) or paraphrasing to increase the size and diversity of datasets.

  • Multi-task Learning: Train the model on related tasks (like sentiment analysis, named entity recognition, and question answering) to help the system generalize better across various contexts.

  • Efficiency and Scalability: Use distributed computing and specialized hardware (GPUs, TPUs) for large-scale training. Leverage pruning, quantization, and model distillation to reduce model size while maintaining performance.

    7. Incorporating External Knowledge

  • Knowledge Graphs: Integrate external knowledge bases like Wikipedia, WordNet, or custom databases to provide the model with real-world context.

  • Commonsense Reasoning: Use models like COMET (Commonsense Transformers) to integrate reasoning about cause-and-effect, everyday events, and general knowledge.

    8. Real-World Contextual Adaptation

  • Fine-Tuning and Continuous Learning: Implement techniques for continuous learning so that the model can evolve with time and adapt to new languages, cultural changes, and evolving linguistic expressions. Fine-tune models on user-specific or region-specific data to make the system more culturally aware and contextually relevant.

  • Zero-Shot and Few-Shot Learning: Develop zero-shot learning capabilities, allowing the system to make educated guesses on tasks or languages it hasn’t been explicitly trained on. Few-shot learning can be used to rapidly adapt to new dialects, idioms, or cultural nuances with minimal new training data.

    9. Evaluation and Iteration

  • Cross-Language Accuracy Metrics: Create benchmarks that test the system's ability to handle multiple languages and dialects, including edge cases (idioms, rare phrases, obscure language use).

  • Error Analysis: Systematically track and analyze errors related to ambiguity, sentiment misclassification, idiomatic misinterpretation, and context loss. Constantly refine models to improve understanding.

  • Human-in-the-Loop Systems: Include mechanisms for humans to intervene when the system encounters difficult-to-interpret text or when it fails. This feedback will guide iterative improvements.

    10. Ethical Considerations and Bias Mitigation

  • Bias Detection: Regularly check for biases related to gender, race, and cultural issues. Biases in data can result in skewed NLU interpretations.

  • Ethical Language Usage: Ensure that the system can identify and address ethically sensitive topics, such as hate speech or misinformation.

  • Explainability: Incorporate models that offer transparent decision-making to ensure the NLU’s reasoning can be explained and corrected if necessary.

    11. Deployment and Continuous Maintenance

  • Integration: Seamlessly integrate the NLU into various applications (chatbots, content analysis tools, sentiment analysis engines).

  • Monitoring: Implement monitoring tools to measure the system's performance over time and in new linguistic environments.

  • Continuous Data Input: Set up feedback loops where the system can learn from interactions in the wild and improve its understanding through active learning.

This step-by-step process is an iterative, evolving approach that balances linguistic theory, computational power, and practical constraints. Building a system of this scale will require extensive collaboration across linguistics, machine learning, and computational infrastructure.

Creating a complete Natural Language Understanding (NLU) system that works in the way described would require thousands of lines of code and a complex architecture. However, I can give you a simplified framework for building a basic NLU system in Python that incorporates some of the key elements. For this, we’ll break it down into smaller components that could be expanded upon over time.

Here is a basic code structure using popular machine learning and NLP libraries such as transformers, spaCy, and nltk. This will provide a foundation for the larger system.

1. Installing Dependencies

First, you’ll need to install some dependencies:

pip install transformers torch spacy nltk
python -m spacy download en_core_web_sm

2. Basic Structure of NLU System

We'll start with:

  • Loading Pre-trained Models for language understanding (e.g., BERT).
  • Contextual Analysis using spaCy and nltk for parsing sentences.
  • Sentiment Analysis as an example task.
import torch
from transformers import BertTokenizer, BertForSequenceClassification
import spacy
import nltk
from nltk.sentiment import SentimentIntensityAnalyzer

# Load pre-trained models
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForSequenceClassification.from_pretrained('bert-base-uncased')

# Load spaCy for NLP
nlp = spacy.load('en_core_web_sm')

# NLTK for sentiment analysis
nltk.download('vader_lexicon')
sia = SentimentIntensityAnalyzer()

# Function to analyze text with BERT
def analyze_text_with_bert(text):
    inputs = tokenizer(text, return_tensors='pt', padding=True, truncation=True, max_length=512)
    outputs = model(**inputs)
    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
    return predictions

# Function for syntactic analysis using spaCy
def syntactic_analysis(text):
    doc = nlp(text)
    for token in doc:
        print(f'{token.text}: {token.dep_} ({token.head.text})')

# Function for sentiment analysis using NLTK
def sentiment_analysis(text):
    sentiment_scores = sia.polarity_scores(text)
    print(f"Sentiment: {sentiment_scores}")

# Basic function to combine different analyses
def nlu_system(text):
    print(f"Analyzing: {text}\n")

    # Syntactic Analysis
    print("Syntactic Analysis (spaCy):")
    syntactic_analysis(text)

    # Sentiment Analysis
    print("\nSentiment Analysis (NLTK):")
    sentiment_analysis(text)

    # BERT Analysis (classification)
    print("\nBERT-based Text Analysis:")
    predictions = analyze_text_with_bert(text)
    print(f"Predictions: {predictions}")

# Example usage
if __name__ == "__main__":
    sample_text = "The movie was fantastic, but the ending was a bit disappointing."
    nlu_system(sample_text)

3. Explanation of the Code

Components:

  1. BERT-based Analysis:

    • The analyze_text_with_bert function uses a pre-trained BERT model for sequence classification (e.g., sentiment analysis, question answering, or general text classification).
    • It tokenizes the input text and uses a BERT model to analyze it, returning the output predictions.
  2. Syntactic Analysis with spaCy:

    • The syntactic_analysis function uses spaCy to parse the input text and provide a dependency tree, identifying syntactic relationships between words (subject, object, verb, etc.).
  3. Sentiment Analysis with NLTK:

    • The sentiment_analysis function uses NLTK’s VADER model for basic sentiment analysis (positive, negative, neutral).
  4. NLU System:

    • The nlu_system function combines these components and outputs the analysis for a given piece of text.

4. Scaling Up the System

To build the system as described in your earlier inquiry, you would need to:

  • Expand the BERT model to handle multi-task learning, such as Named Entity Recognition (NER), Question Answering, and Text Summarization.
  • Fine-tune models on specific datasets to handle domain-specific text and multi-lingual contexts.
  • Add Pragmatics: Implement specific logic for cultural nuances and idiomatic expressions. This may involve custom datasets or specific attention mechanisms in your transformer models.
  • Integrate Knowledge Graphs to provide real-world context to the NLU system. This could be done by adding knowledge retrieval functions from external sources like Wikidata or custom-built knowledge graphs.
  • Continuous Learning: Incorporate reinforcement learning techniques to allow the system to adapt to new text as it interacts with users.

This basic framework provides the backbone for larger, more complex NLU tasks, and you can grow it by implementing more specific models, handling additional languages, and introducing components like contextual memory or dialogue systems.

Advanced NLU at Advanced NLU Integration

版本声明 本文转载于:https://dev.to/kavya-sahai-god/how-to-create-a-human-level-natural-language-understanding-nlu-system-3gmp?1如有侵犯,请联系[email protected]删除
最新教程 更多>
  • 为什么使用固定定位时,为什么具有100%网格板柱的网格超越身体?
    为什么使用固定定位时,为什么具有100%网格板柱的网格超越身体?
    网格超过身体,用100%grid-template-columns 为什么在grid-template-colms中具有100%的显示器,当位置设置为设置的位置时,grid-template-colly修复了?问题: 考虑以下CSS和html: class =“ snippet-code”> g...
    编程 发布于2025-07-10
  • 如何有效地转换PHP中的时区?
    如何有效地转换PHP中的时区?
    在PHP 利用dateTime对象和functions DateTime对象及其相应的功能别名为时区转换提供方便的方法。例如: //定义用户的时区 date_default_timezone_set('欧洲/伦敦'); //创建DateTime对象 $ dateTime = ne...
    编程 发布于2025-07-10
  • 如何从Google API中检索最新的jQuery库?
    如何从Google API中检索最新的jQuery库?
    从Google APIS 问题中提供的jQuery URL是版本1.2.6。对于检索最新版本,以前有一种使用特定版本编号的替代方法,它是使用以下语法:获取最新版本:未压缩)While these legacy URLs still remain in use, it is recommended ...
    编程 发布于2025-07-10
  • 如何在Java中正确显示“ DD/MM/YYYY HH:MM:SS.SS”格式的当前日期和时间?
    如何在Java中正确显示“ DD/MM/YYYY HH:MM:SS.SS”格式的当前日期和时间?
    如何在“ dd/mm/yyyy hh:mm:mm:ss.ss”格式“ gormat 解决方案: args)抛出异常{ 日历cal = calendar.getInstance(); SimpleDateFormat SDF =新的SimpleDateFormat(“...
    编程 发布于2025-07-10
  • 如何使用Depimal.parse()中的指数表示法中的数字?
    如何使用Depimal.parse()中的指数表示法中的数字?
    在尝试使用Decimal.parse(“ 1.2345e-02”中的指数符号表示法表示的字符串时,您可能会遇到错误。这是因为默认解析方法无法识别指数符号。 成功解析这样的字符串,您需要明确指定它代表浮点数。您可以使用numbersTyles.Float样式进行此操作,如下所示:[&& && && ...
    编程 发布于2025-07-10
  • 表单刷新后如何防止重复提交?
    表单刷新后如何防止重复提交?
    在Web开发中预防重复提交 在表格提交后刷新页面时,遇到重复提交的问题是常见的。要解决这个问题,请考虑以下方法: 想象一下具有这样的代码段,看起来像这样的代码段:)){ //数据库操作... 回声“操作完成”; 死(); } ?> ...
    编程 发布于2025-07-10
  • 如何干净地删除匿名JavaScript事件处理程序?
    如何干净地删除匿名JavaScript事件处理程序?
    删除匿名事件侦听器将匿名事件侦听器添加到元素中会提供灵活性和简单性,但是当要删除它们时,可以构成挑战,而无需替换元素本身就可以替换一个问题。 element? element.addeventlistener(event,function(){/在这里工作/},false); 要解决此问题,请考虑...
    编程 发布于2025-07-10
  • 如何同步迭代并从PHP中的两个等级阵列打印值?
    如何同步迭代并从PHP中的两个等级阵列打印值?
    同步的迭代和打印值来自相同大小的两个数组使用两个数组相等大小的selectbox时,一个包含country代码的数组,另一个包含乡村代码,另一个包含其相应名称的数组,可能会因不当提供了exply for for for the uncore for the forsion for for ytry...
    编程 发布于2025-07-10
  • `console.log`显示修改后对象值异常的原因
    `console.log`显示修改后对象值异常的原因
    foo = [{id:1},{id:2},{id:3},{id:4},{id:id:5},],]; console.log('foo1',foo,foo.length); foo.splice(2,1); console.log('foo2', foo, foo....
    编程 发布于2025-07-10
  • 如何在Java字符串中有效替换多个子字符串?
    如何在Java字符串中有效替换多个子字符串?
    在java 中有效地替换多个substring,需要在需要替换一个字符串中的多个substring的情况下,很容易求助于重复应用字符串的刺激力量。 However, this can be inefficient for large strings or when working with nu...
    编程 发布于2025-07-10
  • 如何避免Go语言切片时的内存泄漏?
    如何避免Go语言切片时的内存泄漏?
    ,a [j:] ...虽然通常有效,但如果使用指针,可能会导致内存泄漏。这是因为原始的备份阵列保持完整,这意味着新切片外部指针引用的任何对象仍然可能占据内存。 copy(a [i:] 对于k,n:= len(a)-j i,len(a); k
    编程 发布于2025-07-10
  • 为什么PHP的DateTime :: Modify('+1个月')会产生意外的结果?
    为什么PHP的DateTime :: Modify('+1个月')会产生意外的结果?
    使用php dateTime修改月份:发现预期的行为在使用PHP的DateTime类时,添加或减去几个月可能并不总是会产生预期的结果。正如文档所警告的那样,“当心”这些操作的“不像看起来那样直观。 考虑文档中给出的示例:这是内部发生的事情: 现在在3月3日添加另一个月,因为2月在2001年只有2...
    编程 发布于2025-07-10
  • 您可以使用CSS在Chrome和Firefox中染色控制台输出吗?
    您可以使用CSS在Chrome和Firefox中染色控制台输出吗?
    在javascript console 中显示颜色是可以使用chrome的控制台显示彩色文本,例如红色的redors,for for for for错误消息?回答是的,可以使用CSS将颜色添加到Chrome和Firefox中的控制台显示的消息(版本31或更高版本)中。要实现这一目标,请使用以下模...
    编程 发布于2025-07-10
  • Go web应用何时关闭数据库连接?
    Go web应用何时关闭数据库连接?
    在GO Web Applications中管理数据库连接很少,考虑以下简化的web应用程序代码:出现的问题:何时应在DB连接上调用Close()方法?,该特定方案将自动关闭程序时,该程序将在EXITS EXITS EXITS出现时自动关闭。但是,其他考虑因素可能保证手动处理。选项1:隐式关闭终止数...
    编程 发布于2025-07-10
  • CSS强类型语言解析
    CSS强类型语言解析
    您可以通过其强度或弱输入的方式对编程语言进行分类的方式之一。在这里,“键入”意味着是否在编译时已知变量。一个例子是一个场景,将整数(1)添加到包含整数(“ 1”)的字符串: result = 1 "1";包含整数的字符串可能是由带有许多运动部件的复杂逻辑套件无意间生成的。它也可以是故意从单个真理...
    编程 发布于2025-07-10

免责声明: 提供的所有资源部分来自互联网,如果有侵犯您的版权或其他权益,请说明详细缘由并提供版权或权益证明然后发到邮箱:[email protected] 我们会第一时间内为您处理。

Copyright© 2022 湘ICP备2022001581号-3