We present a computationally-grounded word similarity dataset based on two
well-known Natural Language Processing resources; text corpora and knowledge
bases. This dataset aims to fulfil a gap in psycholinguistic research by
providing a variety of quantifications of semantic similarity in an extensive
set of noun pairs controlled by variables that play a significant role in
lexical processing. The dataset creation has consisted in three steps, 1)
computing four key psycholinguistic features for each noun; concreteness,
frequency, semantic and phonological neighbourhood density; 2) pairing nouns
across these four variables; 3) for each noun pair, assigning three types of
word similarity measurements, computed out of text, Wordnet and hybrid
embeddings. The present dataset includes noun pairs' information in Basque and
European Spanish, but further work intends to extend it to more languages.

本文介绍了一个基于 NLP 资源的语义相似性数据集，用于填补心理语言学研究中的空白，并通过提供大量受词汇处理中起重要作用的变量控制的名词对的语义相似性的各种量化方式。

自然语言处理和心语言学的桥梁：计算基础的巴斯克语和西班牙语语义相似性和关联数据集

Bridging Natural Language Processing and Psycholinguistics:  computationally grounded semantic similarity and relatedness datasets for  Basque and Spanish

Bilingual lexicon induction (BLI) with limited bilingual supervision is a
crucial yet challenging task in multilingual NLP. Current state-of-the-art BLI
methods rely on the induction of cross-lingual word embeddings (CLWEs) to
capture cross-lingual word similarities; such CLWEs are obtained 1) via
traditional static models (e.g., VecMap), or 2) by extracting type-level CLWEs
from multilingual pretrained language models (mPLMs), or 3) through combining
the former two options. In this work, we propose a novel semi-supervised
post-hoc reranking method termed BLICEr (BLI with Cross-Encoder Reranking),
applicable to any precalculated CLWE space, which improves their BLI
capability. The key idea is to 'extract' cross-lingual lexical knowledge from
mPLMs, and then combine it with the original CLWEs. This crucial step is done
via 1) creating a word similarity dataset, comprising positive word pairs
(i.e., true translations) and hard negative pairs induced from the original
CLWE space, and then 2) fine-tuning an mPLM (e.g., mBERT or XLM-R) in a
cross-encoder manner to predict the similarity scores. At inference, we 3)
combine the similarity score from the original CLWE space with the score from
the BLI-tuned cross-encoder. BLICEr establishes new state-of-the-art results on
two standard BLI benchmarks spanning a wide spectrum of diverse languages: it
substantially outperforms a series of strong baselines across the board. We
also validate the robustness of BLICEr with different CLWEs.

本文提出了 BLICEr（BLI with Cross-Encoder Reranking）方法，是一个半监督的后处理重排序方法，用于任意预先计算过的 CLWE 空间，取得了基于两个标准 BLI 基准的新的最先进结果，涵盖了各种不同语言的宽谱。