Breaking Sticks and Ambiguities with Adaptive Skip-gram (1502.07257v2)

Abstract: Recently proposed Skip-gram model is a powerful method for learning high-dimensional word representations that capture rich semantic relationships between words. However, Skip-gram as well as most prior work on learning word representations does not take into account word ambiguity and maintain only single representation per word. Although a number of Skip-gram modifications were proposed to overcome this limitation and learn multi-prototype word representations, they either require a known number of word meanings or learn them using greedy heuristic approaches. In this paper we propose the Adaptive Skip-gram model which is a nonparametric Bayesian extension of Skip-gram capable to automatically learn the required number of representations for all words at desired semantic resolution. We derive efficient online variational learning algorithm for the model and empirically demonstrate its efficiency on word-sense induction task.

• The paper introduces Adaptive Skip-gram (AdaGram), a nonparametric Bayesian extension of Skip-gram that automatically learns multiple vector representations for each word based on its context to handle ambiguity.
• The model demonstrates superior empirical performance on word sense induction tasks compared to existing multi-sense models, effectively capturing diverse word meanings more aligned with human understanding.
• AdaGram automatically adapts the number of word meanings, providing richer semantic granularity for NLP applications like sentiment analysis, named entity recognition, and machine translation.

A Critical Analysis of "Breaking Sticks and Ambiguities with Adaptive Skip-gram"

The paper "Breaking Sticks and Ambiguities with Adaptive Skip-gram" presents a sophisticated extension to the conventional Skip-gram model for learning word representations in NLP. The primary focus of this research is addressing the limitations of the original Skip-gram model in dealing with word ambiguity by introducing a nonparametric Bayesian framework called Adaptive Skip-gram, or AdaGram. This model aims to dynamically allocate multiple prototypes for a given word to encapsulate its diverse semantic meanings depending on contextual usage, thus improving upon the original model which limits words to a single representation.

The Adaptive Skip-gram Model

The problem of word ambiguity—where words can have multiple meanings, such as polysemy and homonymy—is crucial in NLP applications but is inadequately addressed by models like Continuous Bag of Words (CBOW) and Skip-gram. Both these models traditionally associate a single, fixed high-dimensional vector representation with each word, which can lead to mixed or dominant representations that obscure less frequent meanings. AdaGram departs from this approach by leveraging the constructive definition of the Dirichlet process (DP) through stick-breaking representation, which allows it to determine the necessary number of prototypes for each word in an automatic yet flexible manner.

Methodological Approaches

AdaGram incorporates a variational learning algorithm for efficient online training, which maintains significant aspects of the Skip-gram model, including computational efficiency, while integrating the ability to learn multiple prototypes. This is achieved by modeling meanings as latent variables and employing DP for dynamic prototype allocation. The variational inference allows for learning representations in a scalable manner, avoiding the overhead seen in methods requiring offline clustering of contexts.

Empirical Evaluation

The paper provides robust empirical evidence demonstrating the efficacy of AdaGram over existing models in various tasks. It highlights AdaGram's superior performance on word sense induction (WSI) tasks compared to related approaches such as Multi-sense Skip-gram (MSSG) and its nonparametric variant (NP MSSG). AdaGram showed significant improvements in Adjusted Rand Index (ARI) evaluation metrics on diverse datasets, reflecting its enhanced ability to capture word meanings more closely aligned with human semantic understanding.

Furthermore, the research introduced a new dataset, Wikipedia Word-sense Induction (WWSI), which serves as a larger and automatically curated benchmark for evaluating WSI capabilities.

Theoretical and Practical Implications

AdaGram's introduction marks a substantive enhancement in the representation learning framework for NLP tasks. The ability to automatically discover and adapt the number of word meanings according to available data introduces greater semantic granularity in NLP applications—a feature that could be pivotal in tasks such as sentiment analysis, named entity recognition, and machine translation.

Moreover, the Bayes nonparametric approach underpinning AdaGram ensures that the complexity of learned word representations scales with data availability, offering adaptability and robustness for extensive real-world applications.

Future Developments

Given AdaGram's success, future research could explore integration of AdaGram with other neural architectures and potentially extend its nonparametric foundations to broader aspects of NLP beyond word representation, such as sentence or document representation. Developing adaptive mechanisms for different data distributions and languages also stands as a fertile area for further innovation.

In conclusion, the Adaptive Skip-gram represents a significant advancement in the field of representation learning. Its methodological rigour and empirical results put forth a compelling argument for its adoption in sophisticated NLP pipelines confronting the challenges of semantic ambiguity.