Несупервизорное парсинг зависимостей

We present new training methods that aim to mitigate local optima and slow convergence in unsupervised training by using additional im- perfect objectives. In its simplest form, lateen EM alternates …...

Unsupervised dependency parsing aims to learn a dependency grammar from text anno- tated with only POS tags. Various features and inductive biases are often used to incorpo- rate prior knowledge …...

AbstractUnsupervised dependency parsing aims to learn a dependency parser from unannotated sentences. Existing work focuses on either learning generative models using the expectation-maximization algorithm and its variants, or learning discriminative …...

AbstractUnsupervised dependency parsing, which tries to discover linguistic dependency structures from unannotated data, is a very challenging task. Almost all previous work on this task focuses on learning generative models. …...

AbstractSyntactic dependency parsing is an important task in natural language processing. Unsupervised dependency parsing aims to learn a dependency parser from sentences that have no annotation of their correct parse …...

AbstractMost of the unsupervised dependency parsers are based on first-order probabilistic generative models that only consider local parent-child information. Inspired by second-order supervised dependency parsing, we proposed a second-order extension …...

AbstractThere are two major classes of natural language grammars — the dependency grammar that models one-to-one correspondences between words and the constituency grammar that models the assembly of one or …...