Generalized inference with multiple semantic role labeling systems (original) (raw)

Semantic role labeling via generalized inference over classifiers

2004

Abstract We present a system submitted to the CoNLL-2004 shared task for semantic role labeling. The system is composed of a set of classifiers and an inference procedure used both to clean the classification results and to ensure structural integrity of the final role labeling. Linguistic information is used to generate features during classification and constraints for the inference process.

Combination strategies for semantic role labeling

2007

This paper introduces and analyzes a battery of inference models for the problem of semantic role labeling: one based on constraint satisfaction, and several strategies that model the inference as a meta-learning problem using discriminative classifiers. These classifiers are developed with a rich set of novel features that encode proposition and sentence-level information. To our knowledge, this is the first work that: (a) performs a thorough analysis of learning-based inference models for semantic role labeling, and (b) compares several inference strategies in this context. We evaluate the proposed inference strategies in the framework of the CoNLL-2005 shared task using only automatically-generated syntactic information. The extensive experimental evaluation and analysis indicates that all the proposed inference strategies are successful −they all outperform the current best results reported in the CoNLL-2005 evaluation exercise− but each of the proposed approaches has its advantages and disadvantages. Several important traits of a state-of-the-art SRL combination strategy emerge from this analysis: (i) individual models should be combined at the granularity of candidate arguments rather than at the granularity of complete solutions; (ii) the best combination strategy uses an inference model based in learning; and (iii) the learning-based inference benefits from max-margin classifiers and global feedback.

Semantic role labeling via integer linear programming inference

2004

Abstract We present a system for the semantic role labeling task. The system combines a machine learning technique with an inference procedure based on integer linear programming that supports the incorporation of linguistic and structural constraints into the decision process. The system is tested on the data provided in CoNLL-2004 shared task on semantic role labeling and achieves very competitive results.

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.

Multi-argument classification for semantic role labeling

2008

This paper describes a Multi-Argument Classification (MAC) approach to Semantic Role Labeling. The goal is to exploit dependencies between semantic roles by simultaneously classifying all arguments as a pattern. Argument identification, as a pre-processing stage, is carried at using the improved Predicate-Argument Recognition Algorithm (PARA) developed by Lin and Smith (2006). Results using standard evaluation metrics show that multiargument classification, achieving 76.60 in F 1 measurement on WSJ 23, outperforms existing systems that use a single parse tree for the CoNLL 2005 shared task data. This paper also describes ways to significantly increase the speed of multi-argument classification, making it suitable for real-time language processing tasks that require semantic role labeling.

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.

A robust combination strategy for semantic role labeling

2005

This paper focuses on semantic role labeling using automatically-generated syntactic information. A simple and robust strategy for system combination is presented, which allows to partially recover from input parsing errors and to significantly boost results of individual systems. This combination scheme is also very flexible since the individual systems are not required to provide any information other than their solution. Extensive experimental evaluation in the CoNLL-2005 shared task framework supports our previous claims. The proposed architecture outperforms the best results reported in that evaluation exercise.

A Dual-Layer Semantic Role Labeling System

Proceedings of ACL-IJCNLP 2015 System Demonstrations, 2015

We describe a well-performed semantic role labeling system that further extracts concepts (smaller semantic expressions) from unstructured natural language sentences language independently. A dual-layer semantic role labeling (SRL) system is built using Chinese Treebank and Propbank data. Contextual information is incorporated while labeling the predicate arguments to achieve better performance. Experimental results show that the proposed approach is superior to CoNLL 2009 best systems and comparable to the state of the art with the advantage that it requires no feature engineering process. Concepts are further extracted according to templates formulated by the labeled semantic roles to serve as features in other NLP tasks to provide semantically related cues and potentially help in related research problems. We also show that it is easy to generate a different language version of this system by actually building an English system which performs satisfactory.

A Joint Model for Semantic Role Labeling

2005

We present a semantic role labeling system submitted to the closed track of the CoNLL-2005 shared task. The system, introduced in (Toutanova et al., 2005), implements a joint model that captures dependencies among arguments of a predicate using log-linear models in a discriminative re-ranking framework. We also describe experiments aimed at increasing the robustness of the system in the presence of syntactic parse errors. Our final system achieves F1-Measures of 76.68 and 78.45 on the development and the WSJ portion of the test set, respectively.

Generalization of Semantic Roles in Automatic Semantic Role Labeling

Journal of Natural Language Processing, 2014

Numerous studies have applied machine-learning approaches to semantic role labeling with the availability of corpora such as FrameNet and PropBank. These corpora define frame-specific semantic roles for each frame, which are problematic for a machinelearning approach because the corpus contains a number of infrequent roles that hinder efficient learning. This paper focuses on the generalization problem of semantic roles in a semantic role labeling task. We compare existing generalization criteria with our novel criteria, and clarify the characteristics of each criterion. We also show that using multiple generalization criteria in a single model improves the performance of a semantic role classification. In experiments on FrameNet, we achieved 19.16% error reduction in terms of total accuracy, and 7.42% in macro-averaged F1. On PropBank, we reduced 24.07% of errors in total accuracy, and 26.39% of errors in the evaluation for unseen verbs.