Capturing Semantic Similarity for Entity Linking with Convolutional Neural Networks (1604.00734v1)

Abstract: A key challenge in entity linking is making effective use of contextual information to disambiguate mentions that might refer to different entities in different contexts. We present a model that uses convolutional neural networks to capture semantic correspondence between a mention's context and a proposed target entity. These convolutional networks operate at multiple granularities to exploit various kinds of topic information, and their rich parameterization gives them the capacity to learn which n-grams characterize different topics. We combine these networks with a sparse linear model to achieve state-of-the-art performance on multiple entity linking datasets, outperforming the prior systems of Durrett and Klein (2014) and Nguyen et al. (2014).

Capturing Semantic Similarity for Entity Linking with Convolutional Neural Networks

The paper "Capturing Semantic Similarity for Entity Linking with Convolutional Neural Networks" addresses the challenge of entity linking wherein ambiguous mentions in texts must be accurately linked to the correct entities. Traditional methods often employ heuristics like tf-idf, which can be imprecise, capturing contextual information in a coarse manner. This research proposes a method using convolutional neural networks (CNNs) to quantify the semantic similarity between the context surrounding a text mention and the potential target entities, thereby enhancing disambiguation accuracy.

Model Architecture

The proposed model employs CNNs to encapsulate semantic similarities between the mention's context and its entity links. The CNNs are designed to operate at different granularities, encompassing mention-level, context-level, and document-level features to form detailed semantic topic vectors. These vectors are then compared using cosine similarity to evaluate potential links. The convolutional features are integrated with a pre-existing sparse linear model, yielding state-of-the-art results on multiple datasets, outperforming previous models from Durrett et al. (2014) and Nguyen et al. (2015).

Experimental Results

The model's performance was benchmarked across several datasets, including ACE, CoNLL-YAGO, WP, and Wikipedia articles. The combination of CNN-derived features with sparse linear models demonstrated robust performance improvements. Notably, the full system encompassing both CNNs and sparse features achieved significant gains over systems using only one of these feature types. When CNN features were analyzed in isolation, they substantially outperformed the sparse feature models especially when leveraging multiple context granularities, underscoring the CNN's nuanced representation of semantic similarity.

Strong Numerical Results

The numerical results delineate the merit of the model: it achieves accurate entity linking by simultaneously capturing distinct semantic aspects via various CNN granularities. Performance improvements were observed across diverse texts, demonstrating the model's generalizability. Interestingly, the combination of fine-grained features (mention-title pairs) with coarse-grained (document-topic vectors) was particularly effective, highlighting the benefits of integrative semantic analysis for disambiguation tasks.

Implications and Future Directions

The utility of CNNs in capturing semantic nuance broadens the prospects for entity linking in natural language processing. The model's architecture promises enhancements not only in accuracy but also in computational efficiency by avoiding heuristic measures. The leveraging of word embeddings derived from domain-relevant corpora like Wikipedia further optimizes semantic comprehension.

Future research may delve into adaptive mechanisms where CNN parameters could be fine-tuned dynamically based on domain-specific requirements, or extending the framework to address multilingual challenges in entity linking. Additionally, exploring deeper neural architectures, or integrating with knowledge graphs could further enhance the system's capabilities in complex disambiguation tasks.

In conclusion, the paper significantly contributes to the field by demonstrating that convolutional neural networks can effectively refine entity linking tasks, illustrating a promising convergence of deep learning techniques with practical natural language processing applications.