KDSL: a Knowledge-Driven Supervised Learning Framework for Word Sense Disambiguation

  title={KDSL: a Knowledge-Driven Supervised Learning Framework for Word Sense Disambiguation},
  author={Shi Yin and Yi Zhou and Chenguang Li and Shangfei Wang and Jianmin Ji and Xiaoping Chen and Ruili Wang},
  journal={2019 International Joint Conference on Neural Networks (IJCNN)},
  • Shi Yin, Yi Zhou, +4 authors Ruili Wang
  • Published 28 August 2018
  • Computer Science
  • 2019 International Joint Conference on Neural Networks (IJCNN)
We propose KDSL, a new word sense disambiguation (WSD) framework that utilizes knowledge to automatically generate sense-labeled data for supervised learning. First, from WordNet, we automatically construct a semantic knowledge base called DisDict, which provides refined feature words that highlight the differences among word senses, i.e., synsets. Second, we automatically generate new sense-labeled data by DisDict from unlabeled corpora. Third, these generated data, together with manually… Expand


One Million Sense-Tagged Instances for Word Sense Disambiguation and Induction
It is shown that the open source IMS WSD system trained on the dataset achieves stateof-the-art results in standard disambiguation tasks and a recent word sense induction task, outperforming several task submissions and strong baselines. Expand
Semi-supervised Word Sense Disambiguation with Neural Models
This paper studies WSD with a sequence learning neural net, LSTM, to better capture the sequential and syntactic patterns of the text and demonstrates state-of-the-art results, especially on verbs. Expand
Word Sense Disambiguation: A Unified Evaluation Framework and Empirical Comparison
A unified evaluation framework is developed and the results show that supervised systems clearly outperform knowledge-based models in Word Sense Disambiguation, and a linear classifier trained on conventional local features still proves to be a hard baseline to beat. Expand
A Unified Model for Word Sense Representation and Disambiguation
A unified model for joint word sense representation and disambiguation, which will assign distinct representations for each word sense and improves the performance of contextual word similarity compared to existing WSR methods, outperforms state-of-the-art supervised methods on domainspecific WSD, and achieves competitive performance on coarse-grained all-words WSD. Expand
It Makes Sense: A Wide-Coverage Word Sense Disambiguation System for Free Text
The flexible framework of IMS allows users to integrate different preprocessing tools, additional features, and different classifiers, and it achieves state-of-the-art results on several SensEval and SemEval tasks. Expand
An Empirical Evaluation of Knowledge Sources and Learning Algorithms for Word Sense Disambiguation
Evaluated knowledge sources include the part-of-speech of neighboring words, single words in the surrounding context, local collocations, and syntactic relations, and the SVM, Naive Bayes, AdaBoost, and decision tree algorithms. Expand
Neural Sequence Learning Models for Word Sense Disambiguation
This work proposes and studies in depth a series of end-to-end neural architectures directly tailored to the task, from bidirectional Long Short-Term Memory to encoder-decoder models, and shows that sequence learning enables more versatile all-words models that consistently lead to state-of-the-art results, even against word experts with engineered features. Expand
An Enhanced Lesk Word Sense Disambiguation Algorithm through a Distributional Semantic Model
A new Word Sense Disambiguation (WSD) algorithm which extends two well-known variations of the Lesk WSD method which relies on the use of a word similarity function defined on a distributional semantic space to compute the gloss-context overlap. Expand
FastSense: An Efficient Word Sense Disambiguation Classifier
The objective of fastSense is to introduce an efficient neural network-based tool for word sense disambiguation that can process huge amounts of data quickly and also surpasses state-of-the-art tools in terms of F-measure. Expand
Word Sense Disambiguation using a Bidirectional LSTM
A clean, yet effective, model for word sense disambiguation that leverage a bidirectional long short-term memory network which is shared between all words and achieves statistically equivalent results to the best state-of-the-art systems. Expand