Модели встраивания

method ID: embeddingmodels

Related Papers

Compositional Learning of Embeddings for Relation Paths in Knowledge Base and Text

Authors: nan

Publication: TIPSTER (2016)

DOI: nan

Modeling relation paths has offered sig- nificant gains in embedding models for knowledge base (KB) completion. How- ever, enumerating paths between two en- tities is very expensive, and existing ap- …...

40378

Investigating Language Universal and Specific Properties in Word Embeddings

Authors: nan

Publication: TIPSTER (2016)

DOI: nan

Recently, many NLP tasks have benefit- ed from distributed word representation. However, it remains unknown whether embedding models are really immune to the typological diversity of languages, despite the language-independent …...

40375

CoDEx: A Comprehensive Knowledge Graph Completion Benchmark

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractWe present CoDEx, a set of knowledge graph completion datasets extracted from Wikidata and Wikipedia that improve upon existing knowledge graph completion benchmarks in scope and level of difficulty. In …...

29782

Toward Qualitative Evaluation of Embeddings for Arabic Sentiment Analysis

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractIn this paper, we propose several protocols to evaluate specific embeddings for Arabic sentiment analysis (SA) task. In fact, Arabic language is characterized by its agglutination and morphological richness contributing …...

14288

Efficient Hyper-parameter Search for Knowledge Graph Embedding

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractWhile hyper-parameters (HPs) are important for knowledge graph (KG) learning, existing methods fail to search them efficiently. To solve this problem, we first analyze the properties of different HPs and …...

39183

To Copy Rather Than Memorize: A Vertical Learning Paradigm for Knowledge Graph Completion

Authors: nan

Publication: TIPSTER (2023)

DOI: nan

AbstractEmbedding models have shown great power in knowledge graph completion (KGC) task. By learning structural constraints for each training triple, these methods implicitly memorize intrinsic relation rules to infer missing …...

38512