What are Word & Sentence Embedding? 5 Applications

Sentence and word embeddings are the linguistic backbone of large language models (LLMs). They transform human language into a format computers can understand, enabling them to process and analyze text accurately. Sentence and word embeddings have several applications in language translation, sentiment analysis, and question answering. 

This article will delve deeper into the concept of word embeddings, exploring their history, how they are created, and their practical applications. It will also discuss the role of TF-IDF and the challenges associated with this conventional method. 

What Is a Word Embedding? 

Word embedding is a powerful technique in natural language processing (NLP) that represents words numerically as vectors in a high-dimensional space. Each word is assigned a unique vector, and the distance between two vectors in this space is the semantic similarity between the corresponding words. 

Word embedding models capture relationships between words based on several features, including verb tense, age, gender, and more. Consider the diagram above, which shows the vector representations of the words “king,” “queen,” “man,” and “woman.” 

The word embedding model represents these words as vectors such that the relationship between “king” and “queen” is similar to “man” and “woman.” This similarity is captured by the relative positions of these vectors in space. 

You can also perform mathematical operations on these vectors to discover interesting relationships between words. For example, the vector difference between “king” and “queen” is similar to the difference between “man” and “woman”.

What Is Sentence Embedding? 

Sentence embeddings are numerical representations that capture the semantic meaning of entire sentences. Like word embeddings, sentence embeddings map sentences to dense vectors, where similar sentences are positioned close together in the vector space. You can generate these embeddings using the Universal Sentence Encoder (USE), Smooth Inference Frequency (SIF), InferSent, and BERT.

Let’s consider the sentence mentioned in the image above and use the SIF method to understand how sentence embedding is generated. 

In the given sentence, “I want to cancel my shoes order,” each word gets converted into a word embedding, representing its meaning in a numerical form. For example, “I” corresponds to [0.3,0.95,0.1…], “want” corresponds to [0.85,0.21,0,..], and so on. The word embeddings are then averaged to obtain a preliminary sentence embedding.

The next step involves assigning weight to each word based on its frequency in the corpus. Less frequent words are given higher weights. Finally, the weighted average of the word embeddings is calculated. This is the final sentence embedding, capturing the semantic and syntactic information of the sentence.

By leveraging deep learning and neural networks, you can create sentence embedding models that understand sentence semantics and predict surrounding text. Such advanced models help implement reliable text classification, clustering, machine translation, and text summarization. 

For instance, the CNN-non-static model, which uses sentence embeddings, achieved a new state-of-the-art result on the TREC dataset. It has improved the previous best accuracy of 95% by 3.6%.

Multilingual Sentence Embeddings 

Multilingual sentence embedding is a method that encodes text from different languages into a shared vector space. It aims to unify multiple languages into a single, cohesive representation. This implies that sentences with similar meanings, regardless of the language, will be positioned closely together in the semantic space, as shown below.

This language-agnostic approach supports various applications, such as multilingual text classification and cross-lingual information retrieval. Recent efforts to enhance language models include the development of masked language model (MLM) pre-training and its extension to multilingual settings using Translation Language Modeling (TLM).

By providing a common representation, multilingual sentence embeddings facilitate better understanding and processing of text across different languages, enabling seamless interaction in a globalized context.

5 Real World Applications of Word Embeddings 

Word embeddings have varied use in natural language processing. Here are five key applications for you to explore.

Text Classification

Text classification involves assigning labels to text documents, such as determining whether an email is spam or categorizing news articles by topic. Word embeddings enhance the performance of text classification models by providing a more informative representation of the text. 

By representing text as numerical vectors, word embeddings enable machine learning models to identify patterns and relationships between words easily. For example, word embeddings can help distinguish between positive and negative sentiments by capturing nuances of words like “great” and “not so great.”

Name Entity Recognition (NER)

Named Entity Recognition (NER) identifies and categorizes named entities, such as people, organizations, locations, and dates within a text. Word embeddings enhance NER by providing a rich context of words and their relationships. 

NER allows the models to identify entities accurately, even if they appear in different forms or situations. For example, the word "Apple" can refer to the fruit or the technology company based on the context of other words around it. Word embeddings can help ML models distinguish them.  

Machine Translation

Machine translation is a subdomain of computational linguistics that focuses on developing systems with auto-translation capabilities. By learning the relationships between words in different languages, word embeddings enable AI algorithms to translate text more accurately, even for words without direct translations. 

For example, Facebook's fastText, a library of multi-lingual word embeddings, offers word vectors for 176 languages. This makes digital content more accessible to global users and improves their user experience.   

Question Answering

In question-answering systems, word embeddings are critical in understanding the relationship between the question and the potential answers. They represent words in a dense vector space and capture semantic similarities, allowing systems to determine which parts of the knowledge base contain the answer.

With word embeddings, question-answering systems can identify the most relevant answers based on the meaning rather than just the surface form of the words. For example, OpenAI’s LLMs use word embeddings to measure the relatedness of text strings and provide relevant results.

Information Search and Retrieval

In modern information retrieval, word embeddings convert documents and queries into vectors, representing their overall semantic meaning. The similarity between the query vector and each document vector is calculated using a similarity metric like cosine similarity. The documents with the highest similarity scores to the query return as search results.

For example, when you enter “best restaurants in New York City” as a query, the search engine converts it into a vector representation using word embeddings. It then compares it to vectors representing thousands of restaurant reviews, identifies those with the highest similarity scores, and recommends relevant restaurants to you. 

History of Word Embeddings 

The concept of representing words as numerical vectors emerged in the 2000s when researchers explored the potential of neural networks for language modeling. This involved understanding the relationships between words in a continuous space. 

In 2003, Bengio and other researchers experimented with feedforward neural networks to capture word relationships. The early models struggled to handle large vocabularies effectively. Subsequent work by Mnih and Hinton in 2009 focused on using probabilistic models to represent semantic connections between words more accurately.

A significant breakthrough came with the introduction of the Word2Vec model by Tomas Mikolov and his team at Google in 2013. This model utilized Continuous Bag of Words (CBOW) and Continuous Skip-gram methods to learn word embeddings efficiently from large datasets. 

In 2014, Pennington proposed GloVe (Global Vectors for Word Representation), which utilized global word co-occurrence statistics to generate embeddings. It furthered the ability to capture semantic relationships and has since found several applications in various natural language processing (NLP).

Building upon these advancements and leveraging deep learning, embedding layers have become a standard component of neural networks for NLP. Embeddings have expanded beyond words to represent entities, phrases, and other linguistic units. This has accelerated the development of other sophisticated models, including Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, BERT, and GPT. 

All these advancements in LLMs are a result of continuous innovation and a growing understanding of how to represent language computationally.

How are Word Embeddings Created? 

Word embeddings are typically generated using neural network-based models trained on large text corpora. You can employ techniques like GloVe, ELMo, and CoVe to create word embeddings. However, this section will explore Word2Vec and BERT methods. 

Google's Word2Vec is one of the earliest and most influential techniques for generating word embeddings. It uses a shallow neural network to learn word representations. Word2Vec operates by either predicting a word based on its surrounding context (Continuous Bag of Words, CBOW) or predicting the context of the given word (Skip-gram).

This model uses one-hot encoded vectors as input, which are then projected into a dense vector space by multiplying with an embedding matrix. This matrix maps each word in the vocabulary to a word embedding, which captures the semantic meaning of the word. 

The word embeddings become the input to the hidden layer and are further processed using CBOW or Skip-gram. This model structure allows Word2Vec to learn word meanings in a way that positions semantically related words close to each other in the vector space.

Another popular method for creating word embeddings is BERT, which stands for Bidirectional Encoder Representations from Transformers. It is a more advanced technique that uses deep learning to develop contextual embeddings. 

Using masked language modeling, BERT produces dynamic embeddings that change depending on the word's context within a sentence. This enables the model to predict missing words in a sentence by considering context from both directions, left and right, resulting in more nuanced representations.

The BERT-flow method improves BERT's performance by up to 12.70 points and an average of 8.16 points. This stat is based on the Spearman correlation between cosine embedding similarity and human-annotated similarity.

These ML models train on massive datasets to improve performance and generate more accurate word embeddings. Tools like Airbyte can play a crucial role in streamlining data collection and integration from disparate sources to train and test these data models. Its robust data pipelines can make the entire process seamless and cost-efficient.   

The Role of TF-IDF in Word Embeddings 

TF-IDF is a powerful technique for understanding the significance of words within documents and can serve as a valuable preprocessing step for creating word embeddings. By assigning weights to words based on their frequency within a document and their rarity across a corpus, TF-IDF helps you identify the most informative terms. 

You can further use the weighted terms as input features for training word embedding models like Word2Vec or those available through Hugging Face. These models can then learn more nuanced semantic relationships between words by considering their co-occurrence patterns in text.

By using Python libraries like Scikit-learn, Spacy, and NLTK, you can improve the efficiency of implementations for calculating TF-IDF values. The latter two libraries offer a comprehensive toolkit for NLP, providing tokenization, stemming, and stop word removal functions, which are helpful for TD-IDF. You can also use the Gensim library and leverage TD-IDF-weighted features for topic modeling and document similarity. 

While Airbyte’s Python library, PyAirbyte, primarily streamlines data integration, it can indirectly play a crucial role in the word embedding process. It allows you to develop custom data pipelines to extract, transform, and load necessary text data into a data warehouse or lake. PyAirbyte enables you to facilitate a central repository that you can use to train and evaluate word embedding models.     

Challenges with TF-IDF When Compared to Word Embeddings 

While TF-IDF has been a fundamental principle in text representation, its limitations become apparent when compared to word embeddings. Below are some challenges that you can face with TF-IDF:

Lack of Semantic Understanding 

TF-IDF treats words as individual units and represents them statistically based on their frequency within a document while ignoring their semantic relationships. Word embeddings, however, are designed to capture semantic relationships between words. They represent words as dense vectors in a high-dimensional space where similar words are closer together.

Difficulty in Handling Out-of-Vocabulary Words

TF-IDF cannot handle words that are not present in the training corpus. This can be a problem when dealing with new or domain-specific vocabulary. In contrast, word embeddings can often handle out-of-vocabulary words using techniques like nearest neighbor search.

Polysemy and Synonymy

Polysemy refers to a word with multiple meanings, while synonymy refers to words with similar meanings. TF-IDF struggles with these linguistic cases and assigns incorrect weights to words. On the other hand, word embeddings consider the context in which words appear and represent words with similar meanings with similar vector representations.

Contextual Understanding

TF-IDF is a bag-of-words model that doesn't consider the order in which the words appear in a sentence, limiting its ability to understand nuances. Conversely, word embeddings capture different meanings of a word based on its surrounding context. This makes it suitable for tasks requiring a deep understanding of language, such as sentiment analysis and question answering. 

Computational Efficiency

TF-IDF involves calculating term frequencies and inverse document frequencies for every word in an extensive vocabulary, making it computationally expensive. Contrarily, word embeddings are typically pre-trained on massive datasets, allowing for efficient lookup and use in downstream tasks. This pre-computation and reusability make them significantly more efficient.

Building Robust Data Pipelines for Word Embeddings with Airbyte 

Generating word embeddings relies heavily on the quality of the datasets used for training the ML models. This helps increase the accuracy and reliability of the models. By training on large-scale datasets, the ML models are exposed to diverse linguistic contexts, enabling them to learn subtle semantic nuances and associations between words.

Airbyte, an AI-powered data integration tool, can help you address these crucial requirements seamlessly. You can leverage Airbyte’s catalog of 350+ pre-built connectors to extract relevant data from disparate sources for embedding generation. It also allows you to create custom pipelines using low-code Connector Development Kit (CDK) and PyAirbyte (Airbyte’s Python library).

Once you have all the data in a centralized location, you can use the dbt Cloud integration and perform complex transformations to clean and enrich your data. This helps improve the quality of your training models. 

Airbyte also supports integrations with popular vector databases like Pinecone, Milvus, and Weaviate. You can configure Airbyte to store the generated embeddings in your chosen vector database and utilize them in various downstream applications such as semantic search and recommendation systems.

With Airbyte’s comprehensive AI capabilities and integration with frameworks like LangChain, OpenAI, and Cohere, you can create better training datasets, RAG pipelines, and retrieval-based LLMs. Establishing a data infrastructure with Airbyte enables you to leverage its intuitive dashboard to monitor pipeline performance and identify errors, streamlining the troubleshooting process.    

To learn more about Airbyte, you can refer to the official documentation. 

FAQs

What is the difference between word and sentence embeddings?

Word embeddings represent individual words as numerical vectors, capturing semantic and syntactic similarities. Sentence embeddings represent entire sentences as vectors, capturing the overall meaning and context of the sentence.

What are word, sentence, and document embedding?

• Word Embedding: Numerical representation of a word that captures its semantic meaning.
• Sentence Embedding: Numerical representation of a sentence that captures its overall meaning and context. 
• Document Embedding: Numerical representation of a document that captures the content's underlying semantic concepts and main theme.

What is the difference between BERT and sentence-transformers?

BERT is a powerful language model that can understand the context within a sentence. Sentence-transformers are designed to create high-quality sentence embeddings for semantic search and text clustering.

What is the difference between sentence embedding and token embedding?

Token embeddings represent words or subwords as vectors capturing their semantic and syntactic properties, while sentence embeddings represent entire sentences as a single vector.

What is an example of a sentence embedding?

Representing a given sentence, “Today is a sunny day,” into a vector of numbers like [0.32, 0.42, 0.15, …, 0.72] is an example of a sentence embedding.