WordGraph - Development of a unified graph-theoretical system for acquiring lexico-semantic phenomena

一个词的意义很大程度上取决于它与其他词的关系。该项目的目标是开发直观和灵活的方法，使我们能够在不同的语言环境中使用单词及其关系来建模词汇语义。我们的主要表示形式是图论的，它自然适合于表示（i）单词作为节点和（ii）单词之间的关系作为边。同时，向量空间模型也能够基于语言上下文对词汇语义进行建模。不同的上下文具有不同的属性，因此可以用这两种形式主义中的一种来更好地建模。我们提出了一个双重模型来探索哪种形式主义最适合用来覆盖哪种现象。我们继续我们的工作，通过利用在各种语言背景下编码的词汇知识，使用适当的隐喻（图或向量空间模型）自动词汇习得。我们采用了一种新的方式解决歧义（发病率图）和改进的可扩展性，我们的算法（阈值筛选）。我们定义了一个新的应用程序的图形模型（语义头识别），并将继续工作的自动词典采集。

The meaning of a word crucially depends on the relationships it has with other words. The goal of the project is to develop intuitive and flexible methods that enable us to model lexical semantics using words and their relationships in diverse linguistic contexts. Our main representational formalism is graph-theoretical which is naturally suited to represent (i) words as nodes and (ii) relationships between words as edges.Many linguistic contexts are easily modeled with graphs. In parallel, vector space models are capable of modeling lexical semantics based on linguistic contexts as well. Different contexts have different properties and are therefore better modeled in one or the other of the two formalisms. We propose a dual model to explore which formalism is best used to cover which phenomena.We continue our work on automatic lexicon acquisition by exploiting lexical knowledge encoded in various linguistic contexts using the appropriate metaphor (graph or vector space model). We employ a new way of ambiguity resolution (incidence graphs) and improved scalability of our algorithms (threshold sieving). We define a new application for the graph model (semantic head identification) and will continue work on automatic lexicon acquisition.