In order to improve the performance of Tibetan natural language processing applications such as machine translation, sentiment analysis and other tasks, this article proposes a neural network-based method for syntactic dependence. Part corpus Qinghai Normal University’s part-of-speech tag set is marked by corresponding mapping relationship transformed into annotated national standard set. At sa...

Dependency parsing is a crucial step towards deep language understanding and, therefore, widely demanded by numerous Natural Language Processing applications. In particular, left-to-right and top-down transition-based algorithms that rely on Pointer Networks are among the most accurate approaches for performing dependency parsing. Additionally, it has been observed algorithm Networks’ sequentia...

In order to realize the full potential of dependency-based syntactic parsing, it is desirable to allow non-projective dependency structures. We show how a datadriven deterministic dependency parser, in itself restricted to projective structures, can be combined with graph transformation techniques to produce non-projective structures. Experiments using data from the Prague Dependency Treebank s...

In dependency parsing, much effort is devoted to the development of new methods of language modeling and better feature settings. Less attention is paid to actual linguistic data and how appropriate they are for automatic parsing: linguistic data can be too complex for a given parser, morphological tags may not reflect well syntactic properties of words, a detailed, complex annotation scheme ma...

Transition-based models for dependency parsing use a factorization defined in terms of a transition system, or abstract state machine. In this lecture, I will introduce the arc-eager and arcstandard transition systems for dependency parsing (§1) and discuss two different approaches to learning and decoding with these models: greedy classifier-based parsing (§2) and beam search and structured le...

Statistical dependency parsers have quickly gained popularity in the last decade by providing a good trade-off between parsing accuracy and parsing speed. Such parsers usually rely on handcrafted symbolic features and linear discriminative classifiers to make attachment choices. Recent work replaces these with dense word embeddings and neural nets with great success for parsing English and Chin...

We present an arc-factored statistical model for semantic dependency parsing, as defined by the SemEval 2014 Shared Task 8 on Broad-Coverage Semantic Dependency Parsing. Our entry in the open track placed second in the competition.

Dependency parsers are critical components within many NLP systems. However, currently available dependency parsers each exhibit at least one of several weaknesses, including high running time, limited accuracy, vague dependency labels, and lack of nonprojectivity support. Furthermore, no commonly used parser provides additional shallow semantic interpretation, such as preposition sense disambi...