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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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69 |
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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Post-Specialisation: Retrofitting Vectors of Words Unseen in Lexical Resources ...
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Abstract:
Word vector specialisation (also known as retrofitting) is a portable, light-weight approach to fine-tuning arbitrary distributional word vector spaces by injecting external knowledge from rich lexical resources such as WordNet. By design, these post-processing methods only update the vectors of words occurring in external lexicons, leaving the representations of all unseen words intact. In this paper, we show that constraint-driven vector space specialisation can be extended to unseen words. We propose a novel post-specialisation method that: a) preserves the useful linguistic knowledge for seen words; while b) propagating this external signal to unseen words in order to improve their vector representations as well. Our post-specialisation approach explicits a non-linear specialisation function in the form of a deep neural network by learning to predict specialised vectors from their original distributional counterparts. The learned function is then used to specialise vectors of unseen words. This approach, ... : https://www.aclweb.org/anthology/N18-1048 ...
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URL: https://www.repository.cam.ac.uk/handle/1810/294076 https://dx.doi.org/10.17863/cam.41176
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77 |
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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