Natural Language Processing (NLP) is a field of Artificial Intelligence (AI) and Machine Learning (ML) that enables computers to understand and interpret natural language, facilitating interaction with humans. Modern NLP has made significant advancements, particularly with the introduction of deep learning, which has played a major role in these developments. Furthermore, embedding is the process of transforming unstructured data such as words, sentences, or documents into high-dimensional vectors, which is useful for comparing and exploring the semantic similarities in text data. This article will detail the technologies of natural language processing using deep learning and how to build an embedding search engine using Faiss.
1. Basics of Natural Language Processing
The goal of natural language processing is to understand text data and utilize it to perform various tasks. Key tasks of NLP include the following:
- Tokenization: The process of breaking down a sentence into words.
- Part-of-Speech Tagging: Identifying the parts of speech for each word.
- Named Entity Recognition (NER): Recognizing proper nouns such as people, places, and organizations within the text.
- Sentiment Analysis: Assessing whether the text is positive or negative.
- Document Classification: Classifying text into pre-defined categories.
1.1 Introduction of Deep Learning Technologies
Deep learning is a subfield of artificial intelligence that uses artificial neural networks to learn patterns in high-dimensional data. To overcome the limitations of traditional NLP techniques, text data can be converted into vectors, allowing for the use of various deep learning models based on that.
2. Deep Learning Models in Natural Language Processing Development
There are various deep learning models used in natural language processing, with the following being representative examples:
- Recurrent Neural Networks (RNN): Useful for processing time-series data and excel in considering the order of text.
- Long Short-Term Memory (LSTM): A variant of RNN that is particularly advantageous for processing long sequence data.
- Transformer: Overcomes the limitations of RNNs and enables parallel processing, making it the most widely used model in the NLP field today.
2.1 Advancements in Transformer Models
The transformer model effectively processes text by maximizing the relative relationships between each element of the input data through self-attention mechanisms. This leads to better performance, showing excellent results in various NLP tasks.
3. The Need for Embedding and Vectorization Techniques
Embedding is a method of transforming text data into high-dimensional vectors to compare semantic similarities. The purpose of this vectorization is to optimize the arrangement of data so that machine learning models can perform tasks such as classification, clustering, and searching.
3.1 Word2Vec and GloVe
Word2Vec and GloVe are two of the most widely used embedding techniques. Word2Vec excels in finding similar words by learning the relationships between words, while GloVe converts words into vectors based on statistical information.
3.1.1 Principles of Word2Vec
Word2Vec uses two models, ‘Skip-Gram’ and ‘Continuous Bag of Words (CBOW),’ to convert words into vectors. This process contributes to learning relationships between words from large amounts of text data.
from gensim.models import Word2Vec
sentences = [['I', 'am', 'proud', 'to', 'be', 'a', 'deeplearning', 'person'],
['Natural', 'language', 'processing', 'is', 'an', 'interesting', 'field']]
model = Word2Vec(sentences, vector_size=100, window=5, min_count=1, workers=4)
3.1.2 Principles of GloVe
GloVe generates word vectors by leveraging global statistical information. This makes the distances and relationships among words in vector space meaningful, producing more attractive results.
import numpy as np
import glove
glove_model = glove.Glove(no_components=100, learning_rate=0.05)
glove_model.fit(sentences, epochs=30, no_threads=4, verbose=True)
4. Embedding Search Using Faiss
Faiss (Facebook AI Similarity Search) is a library for efficient similarity search, enabling fast and accurate searches using large-scale embedding vectors. Faiss provides various indexing structures and distance measurement methods to prevent performance degradation during high-dimensional vector searches.
4.1 Features of Faiss
- Fast search speeds in large-scale datasets
- Ability to understand proximity and similarity searches in vector space
- Various indexing methods provided (Flat, IVF, HNSW, etc.)
4.2 Installing and Using Faiss
!pip install faiss-cpu
import faiss
import numpy as np
# Data generation
d = 64 # Dimensions
nb = 100000 # Number of database vectors
nq = 10000 # Number of query vectors
np.random.seed(1234) # Fix random seed
xb = np.random.random((nb, d)).astype('float32') # Sample database
xq = np.random.random((nq, d)).astype('float32') # Query data
5. Building an Embedding Search Engine
Now let’s look at how to combine Faiss and deep learning-based embeddings to create a semantic search engine. In the next steps, we will generate embedding vectors using external datasets and explore how to search for those vectors with Faiss.
5.1 Data Collection and Preparation
Datasets for natural language processing can be collected from the internet or public databases. For example, you can gather various document samples from Korean news articles, SNS posts, blog articles, etc.
5.2 Data Preprocessing
The acquired data must be processed through text preprocessing to make it suitable for NLP models. The main preprocessing procedures are as follows:
- Lowercasing
- Removing punctuation and special characters
- Removing stop words
- Stemming or Lemmatization
5.3 Generating Embedding Vectors
Using the preprocessed data, create embedding vectors for each document using either the Word2Vec or GloVe model. The generated vectors will then be prepared for addition to the Faiss index.
# After embedding generation
embedding_vector = model.wv['Natural']
5.4 Adding to Faiss Index and Performing Search
Now, we can add the generated embedding vectors to the Faiss index and execute a fast search.
# Creating Faiss index and adding vectors
index = faiss.IndexFlatL2(d) # Using L2 distance
index.add(xb) # Adding database vectors
k = 5 # Searching for nearest neighbors
D, I = index.search(xq, k) # Performing the search
5.5 Interpreting Similarity Results
The indices and distances obtained from the search results from Faiss help to identify the documents most similar to the user-requested query. This allows users to easily find information that meets their needs.
6. Conclusion and Applications
Building an embedding search engine using deep learning-enabled natural language processing and Faiss is a highly effective way to explore information based on the semantic similarities of natural language data. These technologies are used in various fields and are widely applied in information retrieval, recommendation systems, sentiment analysis, and more. In the future, these technologies will continue to evolve and contribute to solving various problems in our society.
By implementing semantic search using deep learning and Faiss, confirm the value and potential of data and strive to address more challenges in the future.