Text Embedding and Encoders
Embedding techniques are widely used in modern NLP. These methods transform text into numerical vectors, capturing both semantic and syntactic relationships with high fidelity (Patil et al., 2023). Conceptually, this process can be viewed as factor analysis or principal component analysis of the text to extract latent information. However, compared to those techniques, embedding vectors are usually of higher dimensionality (e.g., 768 dimensions), which allows for a more detailed representation of semantic and linguistic features.
The evolution of word embedding techniques has been substantial, from basic one-hot encoding to approaches such as Word2Vec, GloVe, and transformer-based models. Notably, transformer models like BERT (Bidirectional Encoder Representations from Transformers) (Devlin et al., 2018) and SentenceBERT (Reimers & Gurevych, 2019) have significantly advanced context-aware sentence embeddings. These models are initially pre-trained on extensive text corpora and can be fine-tuned for specific applications, enhancing their adaptability and effectiveness. BERT utilizes a deep bidirectional transformer architecture to produce contextualized word embeddings that are aggregated into sentence representations. SentenceBERT modifies BERT to optimize it for sentence-level tasks by fine-tuning with natural language inference data, which enhances the ability to compare sentence embeddings via cosine similarity. This optimization boosts BERT’s efficiency
and effectiveness in applications such as semantic similarity assessment and information retrieval.
Furthermore, the development of Large Language Models (LLMs) has improved text embedding generation. OpenAI, for instance, offers several GPT-based embedding models through its API services, including the “text-embedding-3-small” and the more robust “text-embedding-3-large” model (OpenAI, 2024). These models have demonstrated great capabilities across a diverse set of tasks, including semantic search, clustering, and recommendation systems.
TextSEM supports the integration of both SentenceBERT models and OpenAI APIs for generating text embeddings. However, the high dimensionality of these embeddings poses challenges for direct SEM model estimation. To mitigate this, TextSEM employs Principal Component Analysis (PCA) to reduce dimensionality, allowing users to tailor the reduced dimensions to their specific requirements.
Our online app can directly embed text into vectors and save the vectors as an R data set.
