The compositionality degree of multiword expressions indicates to what extent
the meaning of a phrase can be derived from the meaning of its constituents and
their grammatical relations. Prediction of (non)-compositionality is a task
that has been frequently addressed with distributional semantic models. We
introduce a novel technique to blend hierarchical information with
distributional information for predicting compositionality. In particular, we
use hypernymy information of the multiword and its constituents encoded in the
form of the recently introduced Poincar\'e embeddings in addition to the
distributional information to detect compositionality for noun phrases. Using a
weighted average of the distributional similarity and a Poincar\'e similarity
function, we obtain consistent and substantial, statistically significant
improvement across three gold standard datasets over state-of-the-art models
based on distributional information only. Unlike traditional approaches that
solely use an unsupervised setting, we have also framed the problem as a
supervised task, obtaining comparable improvements. Further, we publicly
release our Poincar\'e embeddings, which are trained on the output of
handcrafted lexical-syntactic patterns on a large corpus.

论文介绍了一种利用多层次信息和分布式信息的新技术，以检测名词短语的组合度，同时采用有监督的方法，并且使用了 Poincaré 嵌入，可以获得显著的效果提升。

使用 Poincaré 嵌入预测名词词组的组合性

On the Compositionality Prediction of Noun Phrases using Poincaré  Embeddings

Recognizing various semantic relations between terms is beneficial for many
NLP tasks. While path-based and distributional information sources are
considered complementary for this task, the superior results the latter showed
recently suggested that the former's contribution might have become obsolete.
We follow the recent success of an integrated neural method for hypernymy
detection (Shwartz et al., 2016) and extend it to recognize multiple relations.
The empirical results show that this method is effective in the multiclass
setting as well. We further show that the path-based information source always
contributes to the classification, and analyze the cases in which it mostly
complements the distributional information.

本文介绍了一种综合神经方法以识别多个语义关系和判断上位词检测，结果表明基于路径的信息源对分类的贡献不可或缺且常常与分布式信息互补。

基于路径和分布信息的词汇语义关系识别

Path-based vs. Distributional Information in Recognizing Lexical  Semantic Relations

This paper presents a new model for word sense disambiguation formulated in
terms of evolutionary game theory, where each word to be disambiguated is
represented as a node on a graph whose edges represent word relations and
senses are represented as classes. The words simultaneously update their class
membership preferences according to the senses that neighboring words are
likely to choose. We use distributional information to weigh the influence that
each word has on the decisions of the others and semantic similarity
information to measure the strength of compatibility among the choices. With
this information we can formulate the word sense disambiguation problem as a
constraint satisfaction problem and solve it using tools derived from game
theory, maintaining the textual coherence. The model is based on two ideas:
similar words should be assigned to similar classes and the meaning of a word
does not depend on all the words in a text but just on some of them. The paper
provides an in-depth motivation of the idea of modeling the word sense
disambiguation problem in terms of game theory, which is illustrated by an
example. The conclusion presents an extensive analysis on the combination of
similarity measures to use in the framework and a comparison with
state-of-the-art systems. The results show that our model outperforms
state-of-the-art algorithms and can be applied to different tasks and in
different scenarios.

本文提出了一种基于演化博弈论的词义消歧模型，用于解决语义学中的词义消歧问题，该模型通过构建一个基于图的模型，通过博弈理论的方法来维持文本的连贯性和一致性，结果表明该方法优于现有算法并可用于不同的任务和场景。

一种博弈论方法解决词义消歧

A Game-Theoretic Approach to Word Sense Disambiguation

Vector space word representations are learned from distributional information
of words in large corpora. Although such statistics are semantically
informative, they disregard the valuable information that is contained in
semantic lexicons such as WordNet, FrameNet, and the Paraphrase Database. This
paper proposes a method for refining vector space representations using
relational information from semantic lexicons by encouraging linked words to
have similar vector representations, and it makes no assumptions about how the
input vectors were constructed. Evaluated on a battery of standard lexical
semantic evaluation tasks in several languages, we obtain substantial
improvements starting with a variety of word vector models. Our refinement
method outperforms prior techniques for incorporating semantic lexicons into
the word vector training algorithms.

本文提出了一种基于语义词典的关系信息来改进词向量表示的方法，并在多种语言的标准词汇语义评估任务中获得了显著的改进，其方法超越了以往词向量训练算法中融入语义词典的技术。