利用语义词汇资源增强现代监督式词义消歧模型
Enhancing Modern Supervised Word Sense Disambiguation Models by Semantic Lexical Resources
AI总结：
本文利用WordNet和WordNet Domains为现代监督式词义消歧模型引入语义特征并扩充训练数据，提出多层架构，在统一评估框架中取得与最先进水平相当的结果。
摘要：
当前，用于词义消歧（WSD）的监督式模型在最流行的基准测试中取得了最先进的结果。尽管近期引入了词嵌入和循环神经网络来设计强大的上下文相关特征，但利用语义词汇资源（SLRs）改进WSD模型的兴趣大多局限于基于知识的方法。在本文中，我们利用两种流行的SLRs——WordNet和WordNet Domains——来增强“现代”监督式WSD模型。我们提出了一种将语义特征有效引入分类器的方法，并考虑利用SLR结构来扩充训练数据。我们研究了不同类型语义特征的效果，考察它们与通过词嵌入混合或循环神经网络编码的局部上下文之间的交互，并将所提出的模型扩展为一种新颖的面向WSD的多层架构。在最近的统一评估框架（Raganato等人，2017）中进行的详细实验比较表明，所提出的方法得到的监督式模型与最先进水平相比具有优势。
英文摘要：
Supervised models for Word Sense Disambiguation (WSD) currently yield to state-of-the-art results in the most popular benchmarks. Despite the recent introduction of Word Embeddings and Recurrent Neural Networks to design powerful context-related features, the interest in improving WSD models using Semantic Lexical Resources (SLRs) is mostly restricted to knowledge-based approaches. In this paper, we enhance "modern" supervised WSD models exploiting two popular SLRs: WordNet and WordNet Domains. We propose an effective way to introduce semantic features into the classifiers, and we consider using the SLR structure to augment the training data. We study the effect of different types of semantic features, investigating their interaction with local contexts encoded by means of mixtures of Word Embeddings or Recurrent Neural Networks, and we extend the proposed model into a novel multi-layer architecture for WSD. A detailed experimental comparison in the recent Unified Evaluation Framework (Raganato et al., 2017) shows that the proposed approach leads to supervised models that compare favourably with the state-of-the art.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
