Over the past decade, analogies, in the form of word-level analogies, have
played a significant role as an intrinsic measure of evaluating the quality of
word embedding methods such as word2vec. Modern large language models (LLMs),
however, are primarily evaluated on extrinsic measures based on benchmarks such
as GLUE and SuperGLUE, and there are only a few investigations on whether LLMs
can draw analogies between long texts. In this paper, we present ANALOGICAL, a
new benchmark to intrinsically evaluate LLMs across a taxonomy of analogies of
long text with six levels of complexity -- (i) word, (ii) word vs. sentence,
(iii) syntactic, (iv) negation, (v) entailment, and (vi) metaphor. Using
thirteen datasets and three different distance measures, we evaluate the
abilities of eight LLMs in identifying analogical pairs in the semantic vector
space (e.g., "I can speak two languages" should be closer to "I am bilingual"
while "I like chocolate" and "I do not like chocolate" should be orthogonal).
Our evaluation finds that it is increasingly challenging for LLMs to identify
analogies when going up the analogy taxonomy.

提出了一个新的基准测试，名为 ANALOGICAL，在长文本的类比上对大型语言模型（LLMs）进行内在评估。在使用 13 个数据集和 3 种不同距离度量进行评估的过程中，发现当上升类比分类学时，LLMs 越来越难以识别类比。

ANALOGICAL - 大语言模型长文本类比的新基准

ANALOGICAL - A New Benchmark for Analogy of Long Text for Large Language  Models

We suggest a model for metaphor interpretation using word embeddings trained
over a relatively large corpus. Our system handles nominal metaphors, like
"time is money". It generates a ranked list of potential interpretations of
given metaphors. Candidate meanings are drawn from collocations of the topic
("time") and vehicle ("money") components, automatically extracted from a
dependency-parsed corpus. We explore adding candidates derived from word
association norms (common human responses to cues). Our ranking procedure
considers similarity between candidate interpretations and metaphor components,
measured in a semantic vector space. Lastly, a clustering algorithm removes
semantically related duplicates, thereby allowing other candidate
interpretations to attain higher rank. We evaluate using different sets of
annotated metaphors, with encouraging preliminary results.

使用基于语料库的单词嵌入模型，处理词汇隐喻并生成一个有序候选诠释列表。该模型通过从依存解析语料库中自动提取主题（如 “时间”）和载体（如 “金钱”）组件的搭配来获取候选的含义，并通过语义向量空间来衡量候选诠释与隐喻组件之间的相似度。最后，聚类算法去除了语义相关的重复诠释，从而使其他候选诠释具有更高的排名。在不同的语义标注数据集上进行评估，取得了令人鼓舞的初步结果。

利用单词嵌入解释隐喻

Metaphor Interpretation Using Word Embeddings

We propose a supervised algorithm for generating type embeddings in the same
semantic vector space as a given set of entity embeddings. The algorithm is
agnostic to the derivation of the underlying entity embeddings. It does not
require any manual feature engineering, generalizes well to hundreds of types
and achieves near-linear scaling on Big Graphs containing many millions of
triples and instances by virtue of an incremental execution. We demonstrate the
utility of the embeddings on a type recommendation task, outperforming a
non-parametric feature-agnostic baseline while achieving 15x speedup and
near-constant memory usage on a full partition of DBpedia. Using
state-of-the-art visualization, we illustrate the agreement of our
extensionally derived DBpedia type embeddings with the manually curated domain
ontology. Finally, we use the embeddings to probabilistically cluster about 4
million DBpedia instances into 415 types in the DBpedia ontology.

提出了一种监督算法来生成类型嵌入，该算法与给定实体嵌入集在相同的语义向量空间中工作。使用最先进的可视化技术，展示了算法在 DBpedia 本体论中的效果。最终，我们使用这些 embedding 将大约 4 百万个 DBpedia 实例概率聚类到 415 种类型。

使用语义嵌入进行大型图形的监督式打标

Supervised Typing of Big Graphs using Semantic Embeddings

Latent Dirichlet Allocation (LDA) mining thematic structure of documents
plays an important role in nature language processing and machine learning
areas. However, the probability distribution from LDA only describes the
statistical relationship of occurrences in the corpus and usually in practice,
probability is not the best choice for feature representations. Recently,
embedding methods have been proposed to represent words and documents by
learning essential concepts and representations, such as Word2Vec and Doc2Vec.
The embedded representations have shown more effectiveness than LDA-style
representations in many tasks. In this paper, we propose the Topic2Vec approach
which can learn topic representations in the same semantic vector space with
words, as an alternative to probability. The experimental results show that
Topic2Vec achieves interesting and meaningful results.

本文提出 Topic2Vec 方法，通过在语义向量空间中学习话题表示来替代概率的 LDA 表示，实验证明该方法取得了有意义的结果。