Ilya Shnayderman


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Learning Concept Abstractness Using Weak Supervision
Ella Rabinovich | Benjamin Sznajder | Artem Spector | Ilya Shnayderman | Ranit Aharonov | David Konopnicki | Noam Slonim
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We introduce a weakly supervised approach for inferring the property of abstractness of words and expressions in the complete absence of labeled data. Exploiting only minimal linguistic clues and the contextual usage of a concept as manifested in textual data, we train sufficiently powerful classifiers, obtaining high correlation with human labels. The results imply the applicability of this approach to additional properties of concepts, additional languages, and resource-scarce scenarios.

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Learning Thematic Similarity Metric from Article Sections Using Triplet Networks
Liat Ein Dor | Yosi Mass | Alon Halfon | Elad Venezian | Ilya Shnayderman | Ranit Aharonov | Noam Slonim
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

In this paper we suggest to leverage the partition of articles into sections, in order to learn thematic similarity metric between sentences. We assume that a sentence is thematically closer to sentences within its section than to sentences from other sections. Based on this assumption, we use Wikipedia articles to automatically create a large dataset of weakly labeled sentence triplets, composed of a pivot sentence, one sentence from the same section and one from another section. We train a triplet network to embed sentences from the same section closer. To test the performance of the learned embeddings, we create and release a sentence clustering benchmark. We show that the triplet network learns useful thematic metrics, that significantly outperform state-of-the-art semantic similarity methods and multipurpose embeddings on the task of thematic clustering of sentences. We also show that the learned embeddings perform well on the task of sentence semantic similarity prediction.