From sentiment analysis to information extraction, to machine translation, question and answer systems, chat robots... the applications of Natural Language Processing (NLP) are complex and diverse. The addition of vector database brings more possibilities to NLP.
In order to facilitate everyone to have an in-depth understanding of the relationship and application of vector databases and NLP, we have launched the "X" Embedding in NLP series of topics, divided into two parts: basic and advanced. This article is the first introductory article. It will introduce in detail how NLP and vector databases represented by Zilliz Cloud and Milvus empower NLP.
01.What is NLP?
Natural language processing (NLP) is an interdisciplinary machine learning technique that combines artificial intelligence and computational linguistics. Its main goal is to enable computers to understand and respond to human language in a meaningful and valuable way.
Of course, we could build a dictionary of all sentences to achieve this goal, but this is somewhat impractical because the combinations of words used to form sentences in human languages are endless. Not only that, accents, diverse synonyms, mispronunciations or omissions of words in sentences further add to the complexity of human language.
NLP uses a variety of techniques and algorithms to process natural language data. Essentially, NLP is used to process unstructured data, specifically unstructured text, and through natural language understanding (NLU), uses syntactic and semantic analysis of text and speech to determine the meaning of sentences and generate structured data that computers can use text. In contrast, natural language generation (NLG) is when a computer generates a human-language text response based on some data input.
By leveraging NLP technology, developers can extract information and insights from text data, enable machines to understand and respond to human queries, and automate all tasks involving language processing. It can be said that NLP makes the human-computer interaction process more intuitive, efficient and smooth. NLP has numerous real-world applications such as virtual assistants, chatbots, information retrieval systems, language translation services, sentiment analysis tools, and automated content generation. Vector databases, especially their efficient embedding vector storage and retrieval capabilities, can bring innovation to the field of NLP and simplify the search process for similar documents or phrases.
02.NLP use cases
Developers can use NLP to build a variety of applications, including:
emotion analysis
Sentiment analysis refers to determining the emotion or emotion expressed in text. Sentiment analysis involves classifying text as positive, negative, or neutral. Sentiment analysis techniques may use machine learning algorithms to train models on labeled data sets, or leverage pre-trained models to capture the sentiment of words and phrases. One of the common scenarios of sentiment analysis is the classification of movie reviews, which can calculate the proportion of positive and negative movie reviews.
information extraction
Information extraction refers to identifying specific information from text, such as extracting names, dates, or numerical values. Information extraction extracts structured data from unstructured text using named entity recognition (NER) and relationship extraction.
machine translation
NLP enables machine translation by leveraging statistical or neural network machine translation models. These models learn patterns and relationships between languages from large amounts of parallel text data, allowing them to translate text from one language to another with appropriate context.
question and answer system
Question answering systems use NLP techniques to understand user questions and retrieve relevant information from a given text corpus. The question and answer system includes steps such as text understanding, document retrieval, and information extraction to provide users with accurate and relevant answers to their queries.
Virtual assistant or chatbot
Virtual assistants are products like Alexa or Siri that receive human speech and derive commands from human language to trigger actions. (Example: Hey Alexa, turn on the lights!). Chatbots use written language to interact with humans to assist users with account or billing issues or other general issues. After processing the text, the chatbot can traverse the decision tree to take the correct action.
text generation
NLP models can generate text based on a given prompt or input. This includes tasks such as language modeling, text summarization, and text generation using techniques such as Recurrent Neural Networks (RNN) or Transformer models.
Spam detection
Natural language processing can aid spam detection. For example, examine the content of an email to determine whether it is spam by looking for overused words, bad grammar, or inappropriate emergency statements.
03.NLP principles
NLP refers to a series of technologies and algorithms that enable computers to process, understand and generate human language. The following is the NLP workflow:
Text Preprocessing – The initial step in NLP is usually the preprocessing of text data. Preprocessing involves things like segmentation (breaking sentences into component words), tokenization (splitting text into individual words or tokens), stopwords (removing unusual words like stop words and common words like "the" or "is" Punctuation that carries too much meaning) and the task of applying stemming (derivating a stem for a given token) or lemmatization (getting the meaning of a token from a dictionary to get the root) to reduce a word to its base form.
Language Understanding – NLP algorithms use various techniques to understand the meaning and structure of text. These techniques include: part-of-speech tagging (grammatical analysis by assigning grammatical tags to each word), syntactic parsing (analyzing sentence structure), and named entity recognition (identifying and classifying named entities such as people, organizations, places, or pop culture references). Task.
“You shall know a word by the company it keeps”
-- British linguist JR Firth
04.NLP model
Deep learning models that are trained on large datasets to perform specific NLP tasks are called pre-trained models (PTMs) for NLP, and they can help downstream NLP tasks by avoiding the need to train a new model from scratch. Here are some famous natural language processing models for the model to perform more accurately:
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BERT (Bidirectional Encoder Representations from Transformer) is a natural language processing model developed by Google that can learn bidirectional representations of text.
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XLNet is a model released by the CMU and Google Brain teams in the paper "XLNet: Generalized Autoregressive Pretraining for Language Understanding" in June 2019 .
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RoBERTa was proposed in the paper "RoBERTa: A Robustly Optimized BERT Pretraining Approach" in 2019 .
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The ALBERT model comes from the paper "ALBERT: A LITE BERT FOR SELF-SUPERVISED LEARNING OF LANGUAGE REPRESENTATIONS" published by Google in 2019.
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StructBERT is an improvement of Alibaba's BERT, which was proposed in the paper "StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding" in 2019 .
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PaLM 2 is a next-generation large language model that has been trained on large amounts of data to predict the next word following human input.
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GPT-4 is a multi-modal large language model developed by OpenAI. It is the fourth model in the GPT series and is known for its powerful natural language generation capabilities.
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SentenceTransformers is a Python framework for sentence, text and image Embedding, originally proposed in the paper "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks".
05.How does Zilliz empower NLP?
Developers are using vector databases to revolutionize the field of NLP. The vector database can effectively store and retrieve Embedding vectors generated by NLP models , simplifying the process of finding similar documents, phrases, or even single words based on semantic similarity. In addition, using a vector database, developers can quickly summarize Collection documents. Using the NLP algorithm, the most important sentences can be extracted from the text corpus, and then with the help of Milvus, the phrases most semantically similar to the extracted phrases can be found.
Another widespread vector database + NLP use case is Retrieval Augmented Generation (RAG). RAG usually comes in the form of a chatbot. Big language models are trained only on publicly available data. Therefore, they may lack domain-specific knowledge or private information. Developers can store domain-specific data in vector databases outside of LLM, and perform similarity searches to return top-K results related to user questions. These results are finally combined and sent to LLM to generate accurate answers.
06. Summary
The use of vector databases , especially their efficient embedding vector storage and retrieval capabilities, can bring innovation to the field of NLP and simplify the search process for similar documents or phrases. NLP combines artificial intelligence and computational linguistics to help computers understand and respond to human language. It has a wide range of application scenarios, including virtual assistants, chat robots, translation services, and sentiment analysis. NLP models such as BERT, XLNet, RoBERTa, ALBERT and GPT-4 and vector databases such as Zilliz Cloud can further enhance NLP and simplify the process of retrieving similar documents or phrases based on semantic similarity.
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