How To Implement GloVe Embeddings In Python: 3 Tutorials & 9 Alternatives

by | Nov 27, 2023 | Natural Language Processing

What are GloVe Embeddings?

GloVe, or Global Vectors for Word Representation, is an unsupervised learning algorithm that obtains vector word representations by analyzing the co-occurrence statistics of words in a text corpus. These word vectors capture the semantic meaning and relationships between words.

The key idea behind GloVe is to learn word embeddings by examining the probability of word co-occurrences across the entire corpus. It constructs a global word-word co-occurrence matrix and then factorizes it to derive word vectors representing words in a continuous vector space.

These word vectors have gained popularity in natural language processing (NLP) tasks due to their ability to capture semantic relationships between words. They are used in various applications such as machine translation, sentiment analysis, text classification, and more, where understanding the meaning and context of words is crucial.

What is a bank? Semantic analysis will allow you to determine whether it's a financial institution or the side of a river.

Contextual understanding allows us to understand words from their surrounding words.

GloVe embeddings have been widely used alongside other embedding techniques, such as Word2Vec and FastText, significantly improving NLP models’ performance.

Understanding GloVe Word Embeddings

Word embeddings bridge the natural language humans use, and the mathematical language machines understand. They transform words into dense, real-valued vectors in a continuous space, representing semantic relationships between words based on their contexts in a given corpus. Here’s a deeper dive into understanding word embeddings:

Representation of Words as Vectors:

  • Traditional methods used one-hot encoding to represent words, resulting in high-dimensional, sparse vectors that lack semantic information.
  • Word embeddings, however, assign each word a fixed-size, dense vector where similar words have similar vector representations. For instance, words like “king” and “queen” might have vectors closer to the embedding space.

Context and Semantic Relationships:

  • Word embeddings capture relationships based on context. Words that appear in similar contexts tend to have similar embeddings. For example, “cat” and “dog” might be closer in the embedding space due to their similar contextual usage.
  • This contextual understanding allows algorithms to grasp semantic similarities and analogies between words (“king” is to “queen” as “man” is to “woman”).
glove vector example "king" is to "queen" as "man" is to "woman"

Training Word Embeddings:

  • Algorithms like GloVe, Word2Vec, and FastText learn word embeddings by processing large text corpora. They consider the context in which words appear by predicting surrounding words (Word2Vec, FastText) or by analyzing word co-occurrence statistics (GloVe).

Dimensionality Reduction and Continuous Space:

  • Word embeddings reduce the dimensionality of the word space. Instead of thousands of dimensions in a one-hot encoding, embeddings typically have a few hundred dimensions.
  • The continuous space representation enables mathematical operations between word vectors, such as addition and subtraction, to unveil relationships like analogies or semantic associations.

Utility in Natural Language Processing:

  • These embeddings serve as valuable inputs for NLP models. They help algorithms understand language nuances, sentiment, and context, improving the performance of various NLP tasks like machine translation, sentiment analysis, text classification, and named entity recognition.

Understanding word embeddings is pivotal in comprehending how machines interpret and process language, facilitating advancements in NLP and related fields.

How are GloVe Word Embeddings Created?

GloVe, an acronym for Global Vectors for Word Representation, uses word co-occurrence statistics to generate word embeddings. At its core, GloVe seeks to establish a comprehensive understanding of the relationships between words within a corpus by analyzing the frequency of their co-occurrences.

The fundamental concept revolves around constructing a word-word co-occurrence matrix, wherein each cell’s value signifies how often two distinct words appear together in a given context window across the entire corpus. Unlike other embedding methods, GloVe considers whether two words co-occur and the probability of their co-occurrence.

GloVe endeavours to encode global statistics about the entire corpus and local context information by capturing these co-occurrence patterns. It then employs matrix factorization techniques, such as Singular Value Decomposition (SVD), to extract the latent structure within the co-occurrence matrix. Through this factorization process, GloVe derives word embeddings—vectors in a continuous space that encapsulates the semantic relationships between words.

These embeddings possess the ability to reflect both syntactic and semantic similarities, showcasing how words relate to each other within the context of the corpus. GloVe’s approach provides a means to efficiently capture and represent these nuanced relationships, making it a powerful method for generating word embeddings used extensively across diverse Natural Language Processing applications.

What are the Advantages?

GloVe, as a word embedding technique, offers several distinctive advantages that contribute to its significance in Natural Language Processing:

  • Semantic Precision: GloVe excels in capturing subtle semantic relationships between words. Leveraging global word co-occurrence statistics encapsulates nuanced semantic information, allowing for precise representation of word meanings and their contextual nuances.
  • Syntactic and Semantic Context: Unlike other embedding methods that focus solely on syntactic or semantic relationships, GloVe adeptly combines the two. It retains information about the syntax and semantics of words, enabling a more comprehensive understanding of word associations and their contextual usage.
  • Scalability and Efficiency: The methodology behind GloVe is scalable and efficient, even when processing large corpora. Its approach of constructing and factorizing co-occurrence matrices allows for relatively faster computation and makes it feasible to train embeddings on extensive datasets.
  • Application Flexibility: GloVe embeddings are versatile and applicable across various NLP tasks. Whether it’s sentiment analysis, machine translation, text classification, or named entity recognition, the embeddings derived from GloVe often enhance the performance of these tasks by providing rich semantic information.
  • Pre-trained Embeddings: GloVe offers pre-trained embeddings on large-scale datasets, providing a valuable resource for NLP practitioners. These pre-trained embeddings can be readily used or fine-tuned for specific tasks, saving time and computational resources in training from scratch.
  • Interpretability: The resulting GloVe embeddings often maintain an interpretable structure. Similar words tend to have similar vector representations, facilitating straightforward interpretation and analysis of the learned embeddings.

The amalgamation of these advantages positions GloVe as a powerful tool for NLP practitioners, enabling them to effectively capture and utilize rich semantic information embedded within textual data for various applications and tasks.

What NLP Applications Use GloVe Embeddings?

GloVe’s robust word embeddings find extensive applications across a spectrum of Natural Language Processing tasks, fostering advancements in language understanding and computational linguistics:

  • Sentiment Analysis: GloVe embeddings aid sentiment analysis by capturing nuanced word meanings. They enable models to discern the sentiment behind words or phrases, enhancing the accuracy of sentiment classification tasks.
  • Named Entity Recognition: In tasks involving the identification of named entities within the text, such as recognizing names of people, organizations, or locations, GloVe embeddings contribute by providing contextual information to improve entity recognition models.
  • Machine Translation: Leveraging semantic similarities encoded within GloVe embeddings improves the quality and accuracy of machine translation systems. It aids in mapping words with similar meanings across different languages, enhancing translation capabilities.
  • Text Classification: Whether it’s categorizing news articles, emails, or social media posts, GloVe embeddings bolster text classification models by capturing semantic relationships between words. This contributes to a more accurate categorization of textual data.
  • Question-Answering Systems: GloVe embeddings help understand the context of queries and documents in question-answering systems. By encoding semantic similarities, they facilitate retrieving relevant information or answers.
  • Semantic Similarity and Analogies: GloVe embeddings enable algorithms to compute semantic similarity between words or phrases and solve analogy-based tasks (e.g., “king” is to “queen” as “man” is to “woman”). This ability to capture relationships in the embedding space extends to various analogy-solving applications.
  • Recommendation Systems: In content-based recommendation systems, GloVe embeddings help understand the content and context of user interactions with textual data. They also enhance the system’s ability to suggest relevant content based on semantic similarities.

The adaptability and richness of semantic information encoded in GloVe embeddings make them indispensable in diverse NLP applications, playing a pivotal role in enhancing the performance and accuracy of these systems across different domains and industries.

How to Implement GloVe Word Embeddings in Practice

Implementing GloVe embeddings in practical applications within Natural Language Processing involves several key steps, from accessing pre-trained embeddings to fine-tuning them for specific tasks:

1. Accessing Pre-trained GloVe Embeddings

Initially, it is essential to obtain pre-trained GloVe embeddings. These embeddings are available in various dimensions (e.g., 50, 100, 300) and trained on extensive text corpora. You can access them from repositories or the GloVe website.

2. Loading GloVe Embeddings into Models

Load the downloaded GloVe embeddings into your preferred platform or library, such as TensorFlow or PyTorch—map words to their corresponding vectors using dictionaries or embedding matrices.

3. Integrating GloVe Embeddings in NLP Models

Embed GloVe vectors as the initial weights in an embedding layer within NLP models. For instance, in TensorFlow, these embeddings are the weights of an Embedding layer, allowing the network to learn from these pre-trained representations.

4. Fine-tuning GloVe Embeddings (Optional)

Depending on the task, fine-tuning GloVe embeddings can optimize model performance. You can freeze the embeddings (trainable=False) to preserve their pre-trained features or update them during training (trainable=True) to adapt to specific domain nuances.

5. Customizing for Specific NLP Tasks

Tailor the GloVe embeddings for specialized NLP tasks. For instance, in sentiment analysis or text classification, feed these embeddings into models like recurrent neural networks (RNNs) or convolutional neural networks (CNNs) to classify sentiments or categorize texts.

6. Evaluating and Tuning Models

Assess model performance using validation sets and metrics pertinent to the task (accuracy, F1-score, etc.). Adjust hyperparameters to enhance model accuracy and generalization, including learning rate, architecture, and embedding dimensions.

7. Iterating and Refinement

Iterate through different approaches, experiment with various architectures, and consider ensembling techniques to refine model performance. To optimize results, fine-tune both the model and the GloVe embeddings.

Utilizing GloVe embeddings in NLP models empowers them with enriched semantic representations, enabling better comprehension of textual data. Effectively leveraging these embeddings contributes to superior performance across various NLP applications, enhancing language understanding, sentiment analysis, and information retrieval systems.

How to Use GloVe Word Embeddings In Python

Using GloVe embeddings in Python involves a few steps. You’ll either train your embeddings or use pre-trained ones. Here’s a basic overview using pre-trained embeddings in Python:

1. Downloading Pre-trained GloVe Embeddings

GloVe provides pre-trained word vectors trained on large corpora. You can download them from the GloVe website or other repositories.

2. Loading GloVe Embeddings into Python

Once downloaded, you’ll load these embeddings into your Python environment. You can use the embeddings directly or convert them into a Python dictionary for easy access.

# Load GloVe embeddings into a dictionary
def load_embeddings(file_path):
    embeddings = {}
    with open(file_path, 'r', encoding='utf-8') as f:
        for line in f:
            values = line.split()
            word = values[0]
            vector = np.asarray(values[1:], dtype='float32')
            embeddings[word] = vector
    return embeddings

glove_embeddings_path = 'path_to_glove_file/glove.6B.100d.txt'  # Adjust the path to your downloaded GloVe file
glove_embeddings = load_embeddings(glove_embeddings_path)

3. Using GloVe Embeddings

Once loaded, you can use these embeddings in various NLP tasks. For example, finding the embedding of a specific word or performing operations on word vectors:

import numpy as np

# Accessing word embeddings
word = 'example'
if word in glove_embeddings:
    embedding = glove_embeddings[word]
    print(f"Embedding for '{word}': {embedding}")
else:
    print(f"'{word}' not found in embeddings")

# Finding similarity between word embeddings
from scipy.spatial.distance import cosine

word1 = 'king'
word2 = 'queen'
similarity = 1 - cosine(glove_embeddings[word1], glove_embeddings[word2])
print(f"Similarity between '{word1}' and '{word2}': {similarity}")

4. Using GloVe Embeddings in Models

You can integrate these embeddings into your NLP models as input features for tasks like sentiment analysis, text classification, or any other application requiring word representations.

Remember to adjust the file paths and methods according to your specific use case and the dimensionality of the GloVe embeddings you’ve downloaded (e.g., glove.6B.100d.txt refers to 100-dimensional vectors trained on a 6-billion-token corpus). If not already in your environment, ensure you have the necessary dependencies installed, such as NumPy for array operations and SciPy for similarity computations.

How to Use GloVe Word Embeddings In Gensim

Gensim doesn’t directly support training GloVe embeddings, but it provides a convenient way to load pre-trained GloVe embeddings and work with them in Python. Here’s a simple guide on how to use gensim to load pre-trained GloVe embeddings:

First, ensure you have gensim installed. You can install it via pip:

pip install gensim

Once installed, you can load pre-trained GloVe embeddings using gensim:

from gensim.test.utils import datapath, get_tmpfile
from gensim.models import KeyedVectors
from gensim.scripts.glove2word2vec import glove2word2vec

# Replace 'path_to_glove_file/glove.6B.100d.txt' with your GloVe file path
glove_file = 'glove.6B.100d.txt'

# Convert GloVe format to Word2Vec format
word2vec_temp_file = get_tmpfile("glove_word2vec.txt")
glove2word2vec(glove_file, word2vec_temp_file)

# Load GloVe embeddings using Gensim
glove_model = KeyedVectors.load_word2vec_format(word2vec_temp_file)

This code loads the GloVe embeddings from the file specified and stores them in glove_model.

Once loaded, you can perform various operations with the loaded model, such as finding the vector for a specific word or calculating the similarity between words:

# Example usage
word = 'example'
if word in glove_model:
    embedding = glove_model[word]
    print(f"Embedding for '{word}': {embedding}")
else:
    print(f"'{word}' not found in embeddings")

word1 = 'king'
word2 = 'queen'
similarity = glove_model.similarity(word1, word2)
print(f"Similarity between '{word1}' and '{word2}': {similarity}")

This code snippet demonstrates how to access the embedding of a specific word and find the similarity between two words using the loaded GloVe model.

Adjust the file path (glove_file) to point to your downloaded GloVe file, considering the specific dimensionality of the GloVe embeddings you are using (glove.6B.100d.txt refers to 100-dimensional vectors trained on a 6-billion-token corpus).

How to use GloVe Embeddings in TensorFlow

In TensorFlow, you can use GloVe embeddings as pre-trained word vectors and fine-tune them within your neural network models. Here’s an essential guide on how to incorporate GloVe embeddings into a TensorFlow-based NLP model:

import numpy as np
import tensorflow as tf

# Sample sentences for illustration
sentences = [
    "This is an example sentence.",
    "Another example sentence here.",
    # Add more sentences as needed
]

# Create a tokenizer and fit on text
tokenizer = tf.keras.preprocessing.text.Tokenizer()
tokenizer.fit_on_texts(sentences)

# Define your TensorFlow model
vocab_size = len(tokenizer.word_index) + 1  # Add 1 for the padding token
embedding_dim = 100  # Assuming GloVe embeddings of dimension 100

# Create an embedding matrix
embedding_matrix = np.zeros((vocab_size, embedding_dim))

# Load GloVe embeddings
def load_glove_embeddings(file_path):
    embeddings = {}
    with open(file_path, 'r', encoding='utf-8') as file:
        for line in file:
            values = line.split()
            word = values[0]
            vector = np.asarray(values[1:], dtype='float32')
            embeddings[word] = vector
    return embeddings

# Replace 'path_to_glove_file/glove.6B.100d.txt' with your GloVe file path
glove_embeddings_path = 'path_to_glove_file/glove.6B.100d.txt'
glove_embeddings = load_glove_embeddings(glove_embeddings_path)

for word, i in tokenizer.word_index.items():
    embedding_vector = glove_embeddings.get(word)
    if embedding_vector is not None:
        embedding_matrix[i] = embedding_vector

# Create an Embedding layer
embedding_layer = tf.keras.layers.Embedding(
    input_dim=vocab_size,
    output_dim=embedding_dim,
    weights=[embedding_matrix],
    trainable=True  # Set to False to freeze GloVe embeddings
)

# Use the embedding layer in your TensorFlow model
model = tf.keras.Sequential([
    embedding_layer,
    # Add other layers (e.g., LSTM, Dense) as needed
])

In this example, the tokenizer represents a tokenization step where words are converted into indices. The embedding matrix is populated with GloVe vectors for words in both GloVe and your dataset vocabulary.

Adjust the vocab_size and embedding_dim variables according to your dataset and the dimensions of your GloVe embeddings.

By utilizing GloVe embeddings as the initial weights in the Embedding layer, you can then train your TensorFlow model for specific NLP tasks while allowing the network to fine-tune these embeddings during training (trainable=True) or keep them fixed (trainable=False). Adjust this parameter based on your model’s requirements and dataset size.

What are the Alternatives to GloVe?

The main alternative to GloVe is Word2Vec. Here’s a comparison table between the two:

FeatureGloVeWord2Vec
AlgorithmGloVe uses global word co-occurrence statisticsWord2Vec has two models: CBOW and Skip-gram
Training ApproachFactorization of word co-occurrence matrixNeural network-based learning of word context
ContextConsiders global word-word co-occurrencesFocuses on local context around words
Semantic RelationsCaptures both syntactic and semantic relationsEmphasizes on capturing semantic relationships
Vector SimilarityCaptures linear relationships between wordsExhibits additive relationships between words
ScalabilityEfficient for large-scale corpus analysisWorks well with large datasets
EfficiencySlower to train due to matrix factorizationFaster training due to neural network methods
Pre-trained ModelsAvailable pre-trained models for various sizesPopular pre-trained models for general usage

Both GloVe and Word2Vec are prominent techniques for generating word embeddings. GloVe emphasizes capturing global word co-occurrences to derive word representations, while Word2Vec focuses on local context and learns through neural networks. The choice between the two often depends on the specific needs of the NLP task, dataset characteristics, and computational resources available for training and inference.

Beyond GloVe and Word2Vec, several other word embedding techniques have emerged, each with unique approaches to capturing word semantics. Here are some notable ones:

1. FastText

Developed by Facebook AI Research (FAIR), FastText extends Word2Vec by considering subword information. It breaks words into smaller character n-grams and generates embeddings for these subword units. This helps handle out-of-vocabulary words and improves representations for morphologically rich languages.

2. BERT (Bidirectional Encoder Representations from Transformers)

Google’s BERT introduced the concept of contextual embeddings. It employs a transformer architecture to generate bidirectional context representations, capturing the meaning of words in a sentence or paragraph based on their surrounding context.

3. ELMo (Embeddings from Language Models)

Similar to BERT, ELMo also focuses on contextual embeddings. It generates embeddings using a bidirectional LSTM (Long Short-Term Memory) model, considering word meanings based on their context in a sentence.

4. GPT (Generative Pre-trained Transformer)

Another model by OpenAI, GPT, uses transformer architectures to learn context-aware word representations. It employs a decoder-only transformer architecture and is trained using unsupervised learning on a large corpus, effectively capturing context and semantics.

5. USE (Universal Sentence Encoder)

Developed by Google, USE generates embeddings for words and entire sentences or short texts. It’s trained on various tasks to create universal representations that capture syntax and semantics.

6. Doc2Vec

An extension of Word2Vec, Doc2Vec, generates embeddings for entire documents. It considers the context of words within a document and assigns embeddings to words and whole documents, enabling document-level similarity calculations.

7. SWEM (Simple Word-Embedding-based Models)

SWEM is a model family that generates sentence embeddings by aggregating word embeddings. Creating sentence representations uses simple operations like averaging or max-pooling over word embeddings.

8. Gaussian Embeddings

Gaussian embeddings represent words as Gaussian distributions in the embedding space. They capture uncertainty and can be beneficial in scenarios where the certainty or variability of word meanings is essential.

Each embedding technique offers unique advantages and is suited to different NLP tasks, corpus types, or computational constraints. Researchers and practitioners often choose embedding techniques based on the specific requirements of their projects.

Challenges and Future Developments

While GloVe embeddings have significantly advanced Natural Language Processing, several challenges and future directions are shaping their evolution:

  1. Domain Adaptation and Specialization: Adapting pre-trained embeddings to specialized domains remains challenging. Future developments may focus on methods to efficiently fine-tune embeddings for domain-specific tasks while retaining their general semantic knowledge.
  2. Handling Polysemy and Ambiguity: Dealing with words with multiple meanings (polysemy) or ambiguous contexts poses challenges. Future research aims to enhance embeddings to capture subtle context shifts and disambiguate word senses more accurately.
  3. Multilingual Embeddings and Cross-lingual Applications: Expanding GloVe embeddings to support multiple languages and facilitate cross-lingual applications is an emerging area. Future developments may involve creating multilingual embeddings or transfer learning techniques for language-agnostic representations.
  4. Ethical Considerations and Bias Mitigation: Addressing biases encoded in embeddings derived from biased datasets is critical. Future advancements focus on developing debiasing techniques to ensure fairness and reduce biases in learned embeddings.
  5. Efficiency and Scalability: Enhancing the efficiency and scalability of embedding techniques for larger datasets or real-time applications is crucial. Future methods might explore lightweight embedding approaches or distributed representations for faster computations.
  6. Dynamic Contextual Embeddings: Contextual embeddings (like those from BERT or GPT models) have gained attention for capturing dynamic context. Future developments may integrate GloVe-style semantic embeddings with contextual information for improved language understanding.
  7. Interpretability and Explainability: Improving the interpretability and explainability of embeddings is essential for gaining trust in NLP systems. Future research might focus on methods to visualize and explain the learned semantic relationships within embeddings.
  8. Continuous Learning and Adaptability: This intriguing area involves enabling embeddings to continually learn and adapt to evolving language patterns over time without forgetting previous knowledge. Future developments may explore lifelong learning techniques for embeddings.

Addressing these challenges and venturing into these future developments will lead to more robust, adaptable, and interpretable GloVe-style embeddings, driving advancements in natural language processing and empowering diverse applications to understand human language more comprehensively.

Conclusion

In the evolving landscape of Natural Language Processing, word embeddings are fundamental tools for understanding language semantics. Techniques like GloVe, Word2Vec, and various newer approaches have revolutionized how machines interpret and process textual data.

GloVe, with its emphasis on capturing global word co-occurrences, provides rich semantic representations that reveal subtle semantic relationships between words. Word2Vec, on the other hand, focuses on local context and exhibits strong performance in capturing syntactic and semantic similarities.

Beyond these, newer models like BERT, ELMo, and FastText have introduced contextual embeddings and subword information, enhancing the understanding of language nuances and improving representations for morphologically diverse languages.

As the field progresses, addressing challenges like domain adaptation, polysemy, bias mitigation, and scalability remains pivotal. Future developments aim to create more adaptable, interpretable, and bias-aware embeddings while advancing multilingual and cross-lingual applications.

In this quest for better word representations, the choice of embedding technique often hinges on the specific demands of the NLP task, dataset intricacies, and computational resources available. These embeddings power various NLP applications and pave the way for more nuanced language understanding and innovative solutions across diverse domains.

About the Author

Neri Van Otten

Neri Van Otten

Neri Van Otten is the founder of Spot Intelligence, a machine learning engineer with over 12 years of experience specialising in Natural Language Processing (NLP) and deep learning innovation. Dedicated to making your projects succeed.

Recent Articles

fact checking with large language models LLMs

Fact-Checking With Large Language Models (LLMs): Is It A Powerful NLP Verification Tool?

Can a Machine Tell a Lie? Picture this: you're scrolling through social media, bombarded by claims about the latest scientific breakthrough, political scandal, or...

key elements of cognitive computing

Cognitive Computing Made Simple: Powerful Artificial Intelligence (AI) Capabilities & Examples

What is Cognitive Computing? The term "cognitive computing" has become increasingly prominent in today's rapidly evolving technological landscape. As our society...

Multilayer Perceptron Architecture

Multilayer Perceptron Explained And How To Train & Optimise MLPs

What is a Multilayer perceptron (MLP)? In artificial intelligence and machine learning, the Multilayer Perceptron (MLP) stands as one of the foundational architectures,...

Left: Illustration of SGD optimization with a typical learning rate schedule. The model converges to a minimum at the end of training. Right: Illustration of Snapshot Ensembling. The model undergoes several learning rate annealing cycles, converging to and escaping from multiple local minima. We take a snapshot at each minimum for test-time ensembling

Learning Rate In Machine Learning And Deep Learning Made Simple

Machine learning algorithms are at the core of many modern technological advancements, powering everything from recommendation systems to autonomous vehicles....

What causes the cold-start problem?

The Cold-Start Problem In Machine Learning Explained & 6 Mitigating Strategies

What is the Cold-Start Problem in Machine Learning? The cold-start problem refers to a common challenge encountered in machine learning systems, particularly in...

Nodes and edges in a bayesian network

Bayesian Network Made Simple [How It Is Used In Artificial Intelligence & Machine Learning]

What is a Bayesian Network? Bayesian network, also known as belief networks or Bayes nets, are probabilistic graphical models representing random variables and their...

Query2vec is an example of knowledge graph reasoning. Conjunctive queries: Where did Canadian citizens with Turing Award Graduate?

Knowledge Graph Reasoning Made Simple [3 Technical Methods & How To Handle Uncertanty]

What is Knowledge Graph Reasoning? Knowledge Graph Reasoning refers to drawing logical inferences, making deductions, and uncovering implicit information within a...

the process of speech recognition

How To Implement Speech Recognition [3 Ways & 7 Machine Learning Models]

What is Speech Recognition? Speech recognition, also known as automatic speech recognition (ASR) or voice recognition, is a technology that converts spoken language...

Key components of conversational AI

Conversational AI Explained: Top 9 Tools & How To Guide [Including GPT]

What is Conversational AI? Conversational AI, short for Conversational Artificial Intelligence, refers to using artificial intelligence and natural language processing...

0 Comments

Submit a Comment

Your email address will not be published. Required fields are marked *

nlp trends

2024 NLP Expert Trend Predictions

Get a FREE PDF with expert predictions for 2024. How will natural language processing (NLP) impact businesses? What can we expect from the state-of-the-art models?

Find out this and more by subscribing* to our NLP newsletter.

You have Successfully Subscribed!