Semantic role labeling via tree kernel joint inference (original) (raw)
Related papers
Multi-lingual dependency parsing at NAIST
Proceedings of the Tenth …, 2006
CoNLL has turned ten! With a mix of pride and amazement over how time flies, we now celebrate the tenth time that ACL's special interest group on natural language learning, SIGNLL, holds its yearly conference.
A Global Joint Model for Semantic Role Labeling
Computational Linguistics, 2008
We present a model for semantic role labeling that effectively captures the linguistic intuition that a semantic argument frame is a joint structure, with strong dependencies among the arguments. We show how to incorporate these strong dependencies in a statistical joint model with a rich set of features over multiple argument phrases. The proposed model substantially outperforms a similar state-of-the-art local model that does not include dependencies among different arguments.
The necessity of syntactic parsing for semantic role labeling
International Joint Conference on …, 2005
We provide an experimental study of the role of syntactic parsing in semantic role labeling. Our conclusions demonstrate that syntactic parse information is clearly most relevant in the very first stage -the pruning stage. In addition, the quality of the pruning stage cannot be determined solely based on its recall and precision. Instead it depends on the characteristics of the output candidates that make downstream problems easier or harder. Motivated by this observation, we suggest an effective and simple approach of combining different semantic role labeling systems through joint inference, which significantly improves the performance.
Introduction to the CoNLL-2005 shared task
Proceedings of the Ninth Conference on Computational Natural Language Learning - CONLL '05, 2005
In this paper we describe the CoNLL-2005 shared task on Semantic Role Labeling. We introduce the specification and goals of the task, describe the data sets and evaluation methods, and present a general overview of the 19 systems that have contributed to the task, providing a comparative description and results.
Hierarchical semantic role labeling
Proceedings of the Ninth Conference on Computational Natural Language Learning - CONLL '05, 2005
The 2005 Conference on Computational Natural Language Learning (CoNLL-2005) is the ninth in a series of meetings organized by SIGNLL, the ACL special interest group on natural language learning. This year's CoNLL will be held in Ann Arbor, Michigan, on June 29 and 30, in conjunction with the ACL 2005 conference.
Introduction to the CoNLL-2004 shared task: Semantic role labeling
Proceedings of CoNLL, 2004
In this paper we describe the CoNLL-2004 shared task: semantic role labeling. We introduce the specification and goal of the task, describe the data sets and evaluation methods, and present a general overview of the systems that have contributed to the task, providing comparative description.
The effect of syntactic representation on semantic role labeling
Proceedings of the 22nd International Conference on Computational Linguistics - COLING '08, 2008
Almost all automatic semantic role labeling (SRL) systems rely on a preliminary parsing step that derives a syntactic structure from the sentence being analyzed. This makes the choice of syntactic representation an essential design decision. In this paper, we study the influence of syntactic representation on the performance of SRL systems. Specifically, we compare constituent-based and dependencybased representations for SRL of English in the FrameNet paradigm. Contrary to previous claims, our results demonstrate that the systems based on dependencies perform roughly as well as those based on constituents: For the argument classification task, dependencybased systems perform slightly higher on average, while the opposite holds for the argument identification task. This is remarkable because dependency parsers are still in their infancy while constituent parsing is more mature. Furthermore, the results show that dependency-based semantic role classifiers rely less on lexicalized features, which makes them more robust to domain changes and makes them learn more efficiently with respect to the amount of training data.
The importance of syntactic parsing and inference in semantic role labeling
2008
We present a general framework for semantic role labeling. The framework combines a machine-learning technique with an integer linear programming-based inference procedure, which incorporates linguistic and structural constraints into a global decision process. Within this framework, we study the role of syntactic parsing information in semantic role labeling. We show that full syntactic parsing information is, by far, most relevant in identifying the argument, especially, in the very first stage-the pruning stage.
Semantic Role Labeling: An Introduction to the Special Issue
Computational Linguistics, 2008
Semantic role labeling, the computational identification and labeling of arguments in text, has become a leading task in computational linguistics today. Although the issues for this task have been studied for decades, the availability of large resources and the development of statistical machine learning methods have heightened the amount of effort in this field. This special issue presents selected and representative work in the field. This overview describes linguistic background of the problem, the movement from linguistic theories to computational practice, the major resources that are being used, an overview of steps taken in computational systems, and a description of the key issues and results in semantic role labeling (as revealed in several international evaluations). We assess weaknesses in semantic role labeling and identify important challenges facing the field. Overall, the opportunities and the potential for useful further research in semantic role labeling are considerable.
Proceedings of the Thirteenth Conference on Computational Natural Language Learning Shared Task - CoNLL '09, 2009
For the 11th straight year, the Conference on Computational Natural Language Learning has been accompanied by a shared task whose purpose is to promote natural language processing applications and evaluate them in a standard setting. In 2009, the shared task was dedicated to the joint parsing of syntactic and semantic dependencies in multiple langauges. This shared task combines the shared tasks of the previous five years under a unique dependency-based formalism similar to the 2008 task. In this paper, we define the shared task, describe how the data sets were created, report and analyze the results and summarize the approaches of the participating systems.