Integrative Semantic Dependency Parsing via Efficient Large-scale Feature Selection
نویسندگان
چکیده
منابع مشابه
Integrative Semantic Dependency Parsing via Efficient Large-scale Feature Selection
Semantic parsing, i.e., the automatic derivation of meaning representation such as an instantiated predicate-argument structure for a sentence, plays a critical role in deep processing of natural language. Unlike all other top systems of semantic dependency parsing that have to rely on a pipeline framework to chain up a series of submodels each specialized for a specific subtask, the one presen...
متن کاملSemantic Dependency Parsing of NomBank and PropBank: An Efficient Integrated Approach via a Large-scale Feature Selection
We present an integrated dependencybased semantic role labeling system for English from both NomBank and PropBank. By introducing assistant argument labels and considering much more feature templates, two optimal feature template sets are obtained through an effective feature selection procedure and help construct a high performance single SRL system. From the evaluations on the date set of CoN...
متن کاملDynamic Feature Selection for Dependency Parsing
Feature computation and exhaustive search have significantly restricted the speed of graph-based dependency parsing. We propose a faster framework of dynamic feature selection, where features are added sequentially as needed, edges are pruned early, and decisions are made online for each sentence. We model this as a sequential decision-making problem and solve it by imitation learning technique...
متن کاملDependency Parsing with Efficient Feature Extraction
The fastest parsers currently can parse an average sentence in up to 2.5ms, a considerable improvement, since most of the older accuracy-oriented parsers parse only few sentences per second. It is generally accepted that the complexity of a parsing algorithm is decisive for the performance of a parser. However, we show that the most time consuming part of processing is feature extraction and th...
متن کاملSemantic Dependency Parsing via Book Embedding
We model a dependency graph as a book, a particular kind of topological space, for semantic dependency parsing. The spine of the book is made up of a sequence of words, and each page contains a subset of noncrossing arcs. To build a semantic graph for a given sentence, we design new Maximum Subgraph algorithms to generate noncrossing graphs on each page, and a Lagrangian Relaxation-based algori...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Artificial Intelligence Research
سال: 2013
ISSN: 1076-9757
DOI: 10.1613/jair.3717