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SEAGLE: A platform for comparative evaluation of semantic encoders for information retrieval
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Specializing distributional vectors of all words for lexical entailment
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How to (properly) evaluate cross-lingual word embeddings: On strong baselines, comparative analyses, and some misconceptions
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Cross-lingual semantic specialization via lexical relation induction
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66 |
Generalized tuning of distributional word vectors for monolingual and cross-lingual lexical entailment
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67 |
SenZi: A sentiment analysis lexicon for the latinised Arabic (Arabizi)
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68 |
Informing unsupervised pretraining with external linguistic knowledge
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Do we really need fully unsupervised cross-lingual embeddings?
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Are we consistently biased? Multidimensional analysis of biases in distributional word vectors
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Unsupervised Cross-Lingual Information Retrieval using Monolingual Data Only ...
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Unsupervised Cross-Lingual Information Retrieval Using Monolingual Data Only ...
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Adversarial Propagation and Zero-Shot Cross-Lingual Transfer of Word Vector Specialization ...
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Post-Specialisation: Retrofitting Vectors of Words Unseen in Lexical Resources ...
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A Resource-Light Method for Cross-Lingual Semantic Textual Similarity ...
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Abstract:
Recognizing semantically similar sentences or paragraphs across languages is beneficial for many tasks, ranging from cross-lingual information retrieval and plagiarism detection to machine translation. Recently proposed methods for predicting cross-lingual semantic similarity of short texts, however, make use of tools and resources (e.g., machine translation systems, syntactic parsers or named entity recognition) that for many languages (or language pairs) do not exist. In contrast, we propose an unsupervised and a very resource-light approach for measuring semantic similarity between texts in different languages. To operate in the bilingual (or multilingual) space, we project continuous word vectors (i.e., word embeddings) from one language to the vector space of the other language via the linear translation model. We then align words according to the similarity of their vectors in the bilingual embedding space and investigate different unsupervised measures of semantic similarity exploiting bilingual ... : Accepted for publication in Knowledge-Based Systems journal ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/1801.06436 https://dx.doi.org/10.48550/arxiv.1801.06436
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Post-Specialisation: Retrofitting Vectors of Words Unseen in Lexical Resources ...
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Unsupervised Cross-Lingual Information Retrieval Using Monolingual Data Only
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ArguminSci: a tool for analyzing argumentation and rhetorical aspects in scientific writing
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Investigating the role of argumentation in the rhetorical analysis of scientific publications with neural multi-task learning models
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