Rehabilitation of Count-based Models for Word Vector Representations

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Enabling autonomous driving (AD) can be considered one of the biggest challenges in today?s technology. AD is a complex task accomplished by several functionalities, with environment perception being one of its core functions. Environment perception is usu ...

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There has recently been much interest in extending vector-based word representations to multiple languages, such that words can be compared across languages. In this paper, we shift the focus from words to documents and introduce a method for embedding doc ...

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Pre-trained word vectors are ubiquitous in Natural Language Processing applications. In this paper, we show how training word embeddings jointly with bigram and even trigram embeddings, results in improved unigram embeddings. We claim that training word em ...

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Rehabilitation of Count-based Models for Word Vector Representations

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The Vulnerability of Semantic Segmentation Networks to Adversarial Attacks in Autonomous Driving: Enhancing Extensive Environment Sensing

Concept Discovery for The Interpretation of Landscape Scenicness

Multi-scale sequential network for semantic text segmentation and localization

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On the Limitations of Cross-lingual Encoders as Exposed by Reference-Free Machine Translation Evaluation

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Approaching Ontology Alignment through Representation Learning to Bridge the Semantic Gap in Engineering Applications

Crosslingual Document Embedding as Reduced-Rank Ridge Regression

Aligning Multilingual Word Embeddings for Cross-Modal Retrieval Task

Better Word Embeddings by Disentangling Contextual n-Gram Information

Learning to Create Sentence Semantic Relation Graphs for Multi-Document Summarization