Learning Semantic Knowledge from Wikipedia: Learning Concept Hierarchies from Document Categories

Author: Mingda Chen. Table of Links Abstract Acknowledgements 1 INTRODUCTION 1.1 Overview 1.2 Contributions 2 BACKGROUND 2.1 Self-Supervised Language Pretraining 2.2 Naturally-Occurring Data Structures 2.3 Sentence Variational Autoencoder 2.4 Summary 3 IMPROVING SELF-SUPERVISION FOR LANGUAGE PRETRAINING 3.1 Improving Language Representation Learning via Sentence Ordering Prediction 3.2 Improving In-Context Few-Shot Learning via Self-Supervised Training 3.

gives the best performance averaging over 8 tasks for both BERT and RoBERTa. We perform an in-depth analysis of approaches to handling the Wikipedia category graph and the effects of pretraining with showing more significant gains for the task requiring higher-level conceptual knowledge; uses category pairs in which one is a hyponym of the other, it is more closely related to work in extracting hyponymhypernym pairs from text . Pavlick et al. automatically generate a large-scale phrase pair dataset with several relationships by training classifiers on a relatively small amount of human-annotated data. However, most of this prior work uses raw text or raw text combined with either annotated data or curated resources like WordNet.

The Multi-Genre Natural Language Inference dataset is a human-annotated multi-domain NLI dataset. to form input data pairs. We include this dataset for more finegrained evaluation. Since there is no standard development or testing set for this dataset, we randomly sample 60%/20%/20% as our train/dev/test sets. Break. Glockner et al. constructed a challenging NLI dataset called “Break” using external knowledge bases such as WordNet. Since sentence pairs in the dataset only differ by one or two words, similar to a pair of adversarial examples, it has broken many NLI systems.

. All three datasets are constructed from their corresponding parent-child relationship pairs. Neutral pairs are first randomly sampled from non-ancestor-descendant relationships and top ranked pairs according to cosine similarities of ELMo embeddings are kept. We also ensure these datasets are balanced among the three classes. Code and data are available at https://github.com/ZeweiChu/WikiNLI. 4.3.4 Experimental Results The results are summarized in Table 4.10.

can lead to much more substantial gains than the other two resources. Although BERT-large + . We note that BERT-large + Wikidata, and WordNet, we list the top 20 most frequent words in these three resources in Table 4.11. Interestingly, WordNet contains mostly abstract words, such as “unit”, “family”, and “person”, while Wikidata contains many domain-specific words, such as “protein” and “gene”. In contrast,

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