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Semantic Relation Classification via Convolutional Neural Networks with Simple Negative Sampling
Kun Xu, Yansong Feng, Songfang Huang, Dongyan Zhao
TL;DR
Relation extraction models can be distracted by irrelevant clauses when nominals are distant, motivating representations focused on their shortest dependency path. The paper uses a CNN over these paths and negative sampling for subject–object directionality, achieving better reported performance than prior systems on SemEval-2010 Task 8.
Problem
Neural relation-extraction models often suffer from irrelevant information when subjects and objects are separated by longer distances, while subject–object directionality also requires explicit handling.
Method
A convolutional neural network learns relation representations from shortest dependency paths, while reversed object-to-subject paths provide negative samples for learning subject–object assignments.
Results
The model outperforms state-of-the-art systems on SemEval-2010 Task 8, while negative sampling improves F1 by 2.1% without lexical features and 1.9% with them.
Takeaways & Limitations
Shortest dependency paths capture syntactic features for relation extraction, and simple negative samples help the model make correct subject–object assignments.
Takeaways & Limitations
The paper omits detailed objective-function formulas for space and assumes the shortest path describes the relationship between two nominals in a sentence.
Abstract
from arXiv · showhide
Syntactic features play an essential role in identifying relationship in a sentence. Previous neural network models often suffer from irrelevant information introduced when subjects and objects are in a long distance. In this paper, we propose to learn more robust relation representations from the shortest dependency path through a convolution neural network. We further propose a straightforward negative sampling strategy to improve the assignment of subjects and objects. Experimental results show that our method outperforms the state-of-the-art methods on the SemEval-2010 Task 8 dataset.
1 Introduction
The paper targets relation extraction with syntactic representations that avoid irrelevant clauses and explicitly model subject–object directionality. It uses shortest dependency paths and negative sampling to improve relation classification.
- Relation extraction identifies the relationship between two nominals in a sentence, commonly using syntactic and other linguistic features.
- Neural models can incorporate irrelevant subsequences or clauses when subjects and objects are far apart, hurting extraction performance.
- The proposed CNN learns relation representations from the simple dependency path between subjects and objects, excluding unrelated chunks or clauses.
- Negative sampling models relation directionality by using the shortest dependency path from the object to the subject as a negative sample.
- The model outperforms state-of-the-art methods on the SemEval-2010 Task 8 dataset.
2 The Shortest Path Hypothesis
The shortest dependency path is assumed to describe the relationship between two nominals, while predicate order on that path indicates subject–object assignments.
- For two nominals in one sentence, the shortest dependency path is assumed to describe their relationship.
- If the nominals share a predicate, the path passes through it; across predicate–argument structures, it passes through predicates sharing consecutive arguments.
- The order of predicates on the path indicates the proper assignments of subjects and objects.
3 A Convolutional Neural Network Model
The CNN converts shortest dependency paths into fixed-size syntactic representations by embedding path nodes, extracting local features, pooling globally, and classifying relations.
- The model inputs words, dependency edge directions, and dependency labels from the shortest path between subject and object.
- Each path node is mapped to a vector through an embedding lookup table.
- Convolution: Fixed-size windows concatenate node vectors to form local feature matrices around each dependency-path node.
- Convolution: A linear transformation extracts local contextual features, and max pooling selects useful features into a global vector independent of path length.
- Objective Function and Learning: The softmax classifier predicts a K-class relation distribution using cross-entropy error and regularization over the training data.
- Objective Function and Learning: Model parameters are learned through backpropagation using stochastic gradient descent with AdaGrad.
4 Negative Sampling
The model uses negative sampling to learn correct subject–object assignments, treating reversed assignments as informative negative examples. Development experiments show that assignment information materially improves performance.
- Pilot Experiments: The third pilot experiment serves as an upper bound because subject–object assignments are available during both training and testing.
- Negative Sampling: A dependency path may correspond to multiple relations between two nominals.
- Negative Sampling: The paper omits detailed formulas because of space limitations.
- Negative Sampling: Adding assignment information during training significantly improves the model, demonstrating that subject–object assignment is crucial to performance.
- Negative Sampling: The proposed strategy treats the opposite subject–object assignment as a negative example for the same relationship.The reversed dependency path differs from the correct path, providing information for distinguishing subject and object roles.
5 Experimental Evaluation
The model is evaluated on SemEval-2010 Task 8 against established systems and negative-sampling alternatives. It outperforms prior approaches, benefits from dependency labels, and gains further from reversed-path negative sampling and lexical features.
- Dataset and Setup: 10,717 annotated examples comprise SemEval-2010 Task 8, with 8,000 training instances, 2,717 test instances, and 2,182 validation samples.
- Dataset and Setup: The model predicts both opposite subject–object dependency paths and selects other only when both predictions are other.
- Main Results: Table 2 compares the proposed models with SVM, RNN, MV-RNN, and CNN on SemEval-2010 Task 8.
- Negative Sampling: Table 3 compares different negative-sampling methods on the development set.
- Main Results: The vanilla depLCNN+NS outperforms prior best systems without extra lexical features, while similar lexical features improve it by 1.6%.
- Main Results: depCNN and depLCNN outperform MVRNN and CNN by at least 2.2%, and dependency labels make depLCNN stronger than depCNN.The results indicate that dependency-label information provides more discriminative information for relation extraction.
- Negative Sampling: Negative sampling improves F1 by 2.1% without lexical features and by 1.9% with extra lexical features.
- Negative Sampling: Reversed dependency paths achieve 85.4% F1, improving random negative samples by 1.9% with a similar number of examples.The reversed paths provide clues for learning correct subject–object assignments.
6 Conclusion
The paper combines shortest-dependency-path convolutional representations with simple negative sampling for relation extraction. Experiments show significant gains over state-of-the-art systems and effective capture of syntactic features.
- Conclusion: The model learns relation representations from shortest dependency paths using a convolutional neural network.
- Conclusion: Simple negative sampling helps the model make correct subject–object assignments within relationships.
- Conclusion: Experimental results show significant improvements over state-of-the-art systems and effective capture of syntactic features.