Understanding Word Embeddings and Sentence Similarity through Word2Vec and Doc2Vec

Word embeddings like Word2Vec and Doc2Vec have become a cornerstone of Natural Language Processing (NLP). This article delves into how these algorithms function, particularly focusing on their output for simple phrases and how they can be used to determine sentence similarity. We will also explore advanced models like doc2vec and Siamese Recurrent Architectures for enhanced accuracy.

Introduction to Word Embeddings

Word embeddings, like Word2Vec, transform textual information into numerical vectors. Each word in the vocabulary is represented by a vector in a high-dimensional space. These vectors capture semantic and syntactic relationships between words based on their context in a given corpus.

For instance, consider a corpus containing phrases like "how are you"? In a 30-dimensional embedding space, the vectors for "how", "are", and "you" will be distinct. This is because each word's vector is influenced by its contextual neighborhood in the training text. The difference in vectors for "how" and "are" will not yield a zero vector, as these words have different neighborhood contexts.

Word2Vec and Vector Space Representation

Word2Vec generates a vector for each word in the vocabulary considering a pre-defined dimensionality (e.g., 30 dimensions). This results in a 30-by-1 vector for each word. If "how", "are", and "you" are vectors v1, v2, and v3, respectively, their differences (v1-v2, v2-v3, v3-v1) will be non-zero because they belong to different neighborhoods in the corpus. This representation is crucial for capturing the semantic meaning of words.

Determining Sentence Similarity

Automatically determining the similarity between sentences is a complex task that requires a robust understanding of word embeddings. Word2Vec, by itself, may not suffice for sentence-level tasks like similarity. Instead, a more sophisticated method, such as doc2vec, can be employed.

Doc2Vec, an extension of Word2Vec, defines a fixed-length vector for each document in the corpus. By averaging the vectors of all words in a sentence, a document vector can be obtained. This average vector provides a general representation of the sentence's semantics, which can be used to compare sentences for similarity.

Advanced Models for Enhanced Accuracy

For higher accuracy in sentence similarity, more advanced methods can be utilized. One such method is the Siamese Recurrent Architecture. This architecture uses two recurrent neural networks (RNNs) that share parameters to process two input sentences in parallel. The final hidden states of both networks are concatenated, and a dense layer is applied to determine the similarity score.

Another option is to use LSTMs (Long Short-Term Memory) with sequence classification. LSTM networks are well-suited for sequence data and can capture long-term dependencies within sentences, making them effective for tasks like sentence similarity.

Learning Models with Keras and sklearn

For practical implementations, frameworks like Keras are particularly useful. Keras provides a high-level API for building deep learning models, making it easier to learn both the embedding layers and the final classification layers from the data. Libraries like gensim can be used for pre-processing and creating embeddings, but Keras can handle the entire model training process more comprehensively.

Machine Learning Mastery’s tutorial on Sequence Classification with LSTM Recurrent Neural Networks in Python with Keras provides a detailed explanation and examples. If you are using more data, it is typically better to learn embeddings and the complete model from the data, resulting in better features and classification.

Broader Applications

While the methods discussed here are often used in English text processing, they are language-independent. The principles and techniques can be applied to any language, making these models versatile and broadly applicable. Following these methods can significantly enhance the performance of NLP tasks like sentiment analysis, text classification, and more.

By leveraging advanced models and frameworks like Keras, NLP practitioners can achieve state-of-the-art results in tasks such as sentence similarity, leading to more accurate and meaningful text analysis.