41 |
Manual Clustering and Spatial Arrangement of Verbs for Multilingual Evaluation and Typology Analysis ...
|
|
|
|
BASE
|
|
Show details
|
|
42 |
A Closer Look at Few-Shot Crosslingual Transfer: The Choice of Shots Matters ...
|
|
|
|
BASE
|
|
Show details
|
|
43 |
Verb Knowledge Injection for Multilingual Event Processing ...
|
|
|
|
BASE
|
|
Show details
|
|
44 |
Multi-SimLex: A Large-Scale Evaluation of Multilingual and Cross-Lingual Lexical Semantic Similarity ...
|
|
|
|
BASE
|
|
Show details
|
|
45 |
Probing Pretrained Language Models for Lexical Semantics ...
|
|
|
|
BASE
|
|
Show details
|
|
46 |
The Secret is in the Spectra: Predicting Cross-lingual Task Performance with Spectral Similarity Measures ...
|
|
|
|
BASE
|
|
Show details
|
|
47 |
SemEval-2020 Task 2: Predicting Multilingual and Cross-Lingual (Graded) Lexical Entailment ...
|
|
|
|
BASE
|
|
Show details
|
|
48 |
Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity ...
|
|
|
|
BASE
|
|
Show details
|
|
49 |
Cross-lingual semantic specialization via lexical relation induction ...
|
|
|
|
BASE
|
|
Show details
|
|
50 |
Adversarial propagation and zero-shot cross-lingual transfer of word vector specialization ...
|
|
|
|
BASE
|
|
Show details
|
|
51 |
Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity ...
|
|
|
|
BASE
|
|
Show details
|
|
52 |
SemEval-2020 Task 2: Predicting Multilingual and Cross-Lingual (Graded) Lexical Entailment ...
|
|
|
|
BASE
|
|
Show details
|
|
53 |
Do we really need fully unsupervised cross-lingual embeddings? ...
|
|
|
|
BASE
|
|
Show details
|
|
54 |
Multi-SimLex: A Large-Scale Evaluation of Multilingual and Cross-Lingual Lexical Semantic Similarity ...
|
|
|
|
BASE
|
|
Show details
|
|
55 |
Probing Pretrained Language Models for Lexical Semantics ...
|
|
|
|
BASE
|
|
Show details
|
|
56 |
Classification-Based Self-Learning for Weakly Supervised Bilingual Lexicon Induction ...
|
|
|
|
BASE
|
|
Show details
|
|
57 |
On the relation between linguistic typology and (limitations of) multilingual language modeling ...
|
|
|
|
BASE
|
|
Show details
|
|
58 |
Improving Bilingual Lexicon Induction with Unsupervised Post-Processing of Monolingual Word Vector Spaces ...
|
|
|
|
BASE
|
|
Show details
|
|
59 |
The Secret is in the Spectra: Predicting Cross-Lingual Task Performance with Spectral Similarity Measures ...
|
|
|
|
BASE
|
|
Show details
|
|
60 |
Spatial multi-arrangement for clustering and multi-way similarity dataset construction ...
|
|
|
|
Abstract:
We present a novel methodology for fast bottom-up creation of large-scale semantic similarity resources to support development and evaluation of NLP systems. Our work targets verb similarity, but the methodology is equally applicable to other parts of speech. Our approach circumvents the bottleneck of slow and expensive manual development of lexical resources by leveraging semantic intuitions of native speakers and adapting a spatial multi-arrangement approach from cognitive neuroscience, used before only with visual stimuli, to lexical stimuli. Our approach critically obtains judgments of word similarity in the context of a set of related words, rather than of word pairs in isolation. We also handle lexical ambiguity as a natural consequence of a two-phase process where verbs are placed in broad semantic classes prior to the fine-grained spatial similarity judgments. Our proposed design produces a large-scale verb resource comprising 17 relatedness-based classes and a verb similarity dataset containing ...
|
|
URL: https://www.repository.cam.ac.uk/handle/1810/306834 https://dx.doi.org/10.17863/cam.53925
|
|
BASE
|
|
Hide details
|
|
|
|