Classifying Relations by Ranking with Convolutional Neural Networks (1504.06580v2)

Abstract: Relation classification is an important semantic processing task for which state-ofthe-art systems still rely on costly handcrafted features. In this work we tackle the relation classification task using a convolutional neural network that performs classification by ranking (CR-CNN). We propose a new pairwise ranking loss function that makes it easy to reduce the impact of artificial classes. We perform experiments using the the SemEval-2010 Task 8 dataset, which is designed for the task of classifying the relationship between two nominals marked in a sentence. Using CRCNN, we outperform the state-of-the-art for this dataset and achieve a F1 of 84.1 without using any costly handcrafted features. Additionally, our experimental results show that: (1) our approach is more effective than CNN followed by a softmax classifier; (2) omitting the representation of the artificial class Other improves both precision and recall; and (3) using only word embeddings as input features is enough to achieve state-of-the-art results if we consider only the text between the two target nominals.

• The paper introduces a CR-CNN that uses a pairwise ranking loss to circumvent the need for expensive handcrafted features.
• Empirical evaluation on the SemEval-2010 Task 8 dataset achieved an F1 score of 84.1, outperforming previous state-of-the-art models.
• The study demonstrates that omitting the artificial 'Other' class enhances precision and recall, paving the way for scalable NLP solutions.

Classifying Relations by Ranking with Convolutional Neural Networks

The paper "Classifying Relations by Ranking with Convolutional Neural Networks" presents a novel approach to the task of relation classification in NLP. The authors propose a method utilizing a Classification by Ranking Convolutional Neural Network (CR-CNN), which aims to circumvent the reliance on expensive handcrafted features often used in state-of-the-art systems.

Key Contributions

1. CR-CNN Architecture:
   • The CR-CNN is designed to learn distributed vector representations for relation classes. The network evaluates a given text segment, generating a score for each class by comparing the text representation to class embeddings.
   • A novel pairwise ranking loss function is introduced to attenuate the impact of artificial classes, such as the class "Other" in the SemEval-2010 Task 8 dataset.
2. Empirical Results:
   • The proposed approach is benchmarked against the SemEval-2010 Task 8 dataset, achieving an F1 score of 84.1, surpassing previous state-of-the-art results without the use of handcrafted features. This is noteworthy as it signifies the efficacy of using pure word embeddings as input features.
3. Comparative Analysis:
   • The CR-CNN demonstrates superior performance over CNN models followed by a softmax classifier. It was observed that omitting the representation of the "Other" class enhances both precision and recall.

Experimental Setup and Findings

• Dataset Utilization:

The SemEval-2010 Task 8 dataset provided a platform for evaluating relation classification performance. The paper emphasizes that not using external resources like WordNet or dependency parsers is sufficient to achieve competent results.

• Word Embeddings:

Pre-trained word embeddings were utilized as the input features, leveraging unsupervised learning from extensive corpus data. The network effectively uses these embeddings to discern semantic relationships without supplementary lexical resources.

• Training Efficiency:

CR-CNN employs a pairwise ranking loss function that facilitates training by updating two classes per example, beneficial for tasks with extensive class sets. This approach aids in efficiently handling the artificial "Other" class, focusing on optimizing for core relation classes.

Theoretical and Practical Implications

The research highlights that with carefully designed neural architectures and loss functions, the dependency on traditional features can be significantly reduced. This positions CR-CNN as a viable model for contexts where generating handcrafted features is impractical. Additionally, the method's capacity to handle artificial classes differently could inspire comparable techniques in related tasks, promoting the broader application of neural models in NLP.

Future Directions

The findings suggest potential expansions in several directions:

• Exploring varied architectures and embedding techniques may yield further improvements in extracting relational semantics.
• Applications in multilingual settings where handcrafted resource availability varies drastically could benefit from this automated feature inference.
• Further analysis could investigate scalability and performance in larger and more diverse datasets.

In conclusion, the paper contributes meaningful insights and advancements in the field of relation classification. CR-CNN, with its ranking-based approach and inventive treatment of artificial classes, marks a step towards automating semantic understanding tasks in NLP.