Sparse Overcomplete Word Vector Representations (1506.02004v1)

Abstract: Current distributed representations of words show little resemblance to theories of lexical semantics. The former are dense and uninterpretable, the latter largely based on familiar, discrete classes (e.g., supersenses) and relations (e.g., synonymy and hypernymy). We propose methods that transform word vectors into sparse (and optionally binary) vectors. The resulting representations are more similar to the interpretable features typically used in NLP, though they are discovered automatically from raw corpora. Because the vectors are highly sparse, they are computationally easy to work with. Most importantly, we find that they outperform the original vectors on benchmark tasks.

• The paper presents a novel sparse coding approach that converts dense word vectors into overcomplete, interpretable representations.
• It introduces an alternative binary representation through nonnegative constraints and binarization for simplified NLP integration.
• The transformed vectors achieve superior performance, with gains of approximately 4–4.8 points on key NLP tasks like sentiment analysis and text classification.

Sparse Overcomplete Word Vector Representations

The article addresses the challenge of creating word vector representations that are both computationally manageable and interpretable, aligning more closely with traditional lexical semantic theories. The authors present a novel method for transforming dense word vectors into sparse and optionally binary representations. This transformation not only enhances interpretability but also improves performance on benchmark NLP tasks.

Key Contributions

1. Sparse Overcomplete Vectors: The primary contribution is a sparse coding method that generates word vectors with increased sparsity and dimensionality, referred to as "overcomplete" vectors. These representations are computationally efficient and align more closely with categorical attributes associated with lexical semantics.
2. Binary Representation: An alternative transformation method proposed by the authors yields binary vectors, further simplifying interpretability and usability in NLP tasks.
3. Benchmark Performance: The sparse transformations exhibit superior performance on multiple qualitative tasks, such as sentiment analysis, question classification, and domain-specific text classification, compared to initial dense representations.
4. Human Interpretability: Sparse vectors significantly improve human interpretability, as demonstrated through a word intrusion test, suggesting that the dimensions of these vectors are more semantically coherent.

Methodological Approach

The authors implement two methodological approaches for transforming word vectors:

• Method A involves sparse coding, which transforms dense vectors into longer sparse representations using an optimization framework that balances reconstruction loss, sparsity, and stability penalties.
• Method B introduces nonnegative constraints on vectors followed by binarization of non-zero values, resulting in sparse, binary word vectors.

For optimizing the transformation, online adaptive gradient descent mechanisms, such as AdaGrad, are employed, allowing efficient parameter updates and scalability for large vocabulary sizes.

Results and Evaluation

The transformed representations were evaluated across several tasks, including word similarity and diverse NLP classification tasks. Sparse overcomplete vectors generally outperformed their initial dense counterparts by a margin, often demonstrating a 4.2-point increase on average performance across the tasks. Furthermore, the binary representations showed an average performance gain of 4.8 points over baseline methods, indicating the effectiveness of binarized features for NLP applications.

Implications for Future Research

The results suggest broad implications for theoretical and practical advancements in NLP. The approach bridges the gap between traditional lexical semantics and computational methods, providing a platform for building more interpretable models that facilitate both automated processing and human analysis. Sparse, binary vectors especially hold promise for integration within statistical NLP models, where interpretability and error analysis are crucial.

Looking forward, these methods could be extended to applications requiring robust semantic interpretation, such as machine translation and semantic search. Additionally, the authors' methodology could inspire further work in intersecting linguistic theories with state-of-the-art distributional semantics.

In conclusion, the paper successfully introduces a method for transforming word vectors into a format that maintains the informational richness necessary for NLP tasks while aligning with traditional semantic theories, providing a foundation for enhancing both interpretative clarity and computational performance in future AI research.