Semantic Role Labeling using Dependency Syntax
نویسندگان
چکیده
This document gives a brief introduction to the topic of Semantic Role Labeling using Dependency Syntax. We also describe a system that has been developed and tested on a corpus from the CoNLL-20081 shared task. We evaluate the system and give a short discussion on further improvements. Our results are reasonably good compared to those reached during the shared task.
منابع مشابه
Dependency Tree Representations of Predicate-Argument Structures
We present a novel annotation framework for representing predicate-argument structures, which uses dependency trees to encode the syntactic and semantic roles of a sentence simultaneously. The main contribution is a semantic role transmission model, which eliminates the structural gap between syntax and shallow semantics, making them compatible. A Chinese semantic treebank was built under the p...
متن کاملSyntax Aware LSTM model for Semantic Role Labeling
In Semantic Role Labeling (SRL) task, the tree structured dependency relation is rich in syntax information, but it is not well handled by existing models. In this paper, we propose Syntax Aware Long Short Time Memory (SA-LSTM). The structure of SA-LSTM changes according to dependency structure of each sentence, so that SA-LSTM can model the whole tree structure of dependency relation in an arc...
متن کاملJoint A∗ CCG Parsing and Semantic Role Labeling
Joint models of syntactic and semantic parsing have the potential to improve performance on both tasks—but to date, the best results have been achieved with pipelines. We introduce a joint model using CCG, which is motivated by the close link between CCG syntax and semantics. Semantic roles are recovered by labelling the deep dependency structures produced by the grammar. Furthermore, because C...
متن کاملThe Dependency-Parsed FrameNet Corpus
When training semantic role labeling systems, the syntax of example sentences is of particular importance. Unfortunately, for the FrameNet annotated sentences, there is no standard parsed version. The integration of the automatic parse of an annotated sentence with its semantic annotation, while conceptually straightforward, is complex in practice. We present a standard dataset that is publicly...
متن کاملA Simple and Accurate Syntax-Agnostic Neural Model for Dependency-based Semantic Role Labeling
We introduce a simple and accurate neural model for dependency-based semantic role labeling. Our model predicts predicate-argument dependencies relying on states of a bidirectional LSTM encoder. The semantic role labeler achieves competitive performance on English, even without any kind of syntactic information and only using local inference. However, when automatically predicted partof-speech ...
متن کامل