Generative and Discriminative Learning in Semantic Role Labeling for Italian
نویسنده
چکیده
In this paper, we present a Semantic Role Labeling tool for Italian language for the FLaIT competition at Evalita 2011. This tool presents an hybrid approach to resolve the different sub-tasks that composed the SRL task. We apply a discriminative model for the boundary detection task based on lexical and syntactical features. A distributional approach to modeling lexical semantic information, instead, for the Argument Classification sub-task is applied in a semi-supervised perspective. The combination of these models achieved interesting results in the FLaIT competition.
منابع مشابه
Discriminative Models for Semi-Supervised Natural Language Learning
An interesting question surrounding semisupervised learning for NLP is: should we use discriminative models or generative models? Despite the fact that generative models have been frequently employed in a semi-supervised setting since the early days of the statistical revolution in NLP, we advocate the use of discriminative models. The ability of discriminative models to handle complex, high-di...
متن کاملDiscriminative vs. Generative Approaches in Semantic Role Labeling
This paper describes the two algorithms we developed for the CoNLL 2008 Shared Task “Joint learning of syntactic and semantic dependencies”. Both algorithms start parsing the sentence using the same syntactic parser. The first algorithm uses machine learning methods to identify the semantic dependencies in four stages: identification and labeling of predicates, identification and labeling of ar...
متن کاملبرچسبزنی خودکار نقشهای معنایی در جملات فارسی به کمک درختهای وابستگی
Automatic identification of words with semantic roles (such as Agent, Patient, Source, etc.) in sentences and attaching correct semantic roles to them, may lead to improvement in many natural language processing tasks including information extraction, question answering, text summarization and machine translation. Semantic role labeling systems usually take advantage of syntactic parsing and th...
متن کاملبرچسبزنی نقش معنایی جملات فارسی با رویکرد یادگیری مبتنی بر حافظه
Abstract Extracting semantic roles is one of the major steps in representing text meaning. It refers to finding the semantic relations between a predicate and syntactic constituents in a sentence. In this paper we present a semantic role labeling system for Persian, using memory-based learning model and standard features. Our proposed system implements a two-phase architecture to first identify...
متن کاملCombining Generative/Discriminative Learning for Automatic Image Annotation and Retrieval
In order to bridge the semantic gap exists in image retrieval, this paper propose an approach combining generative and discriminative learning to accomplish the task of automatic image annotation and retrieval. We firstly present continuous probabilistic latent semantic analysis (PLSA) to model continuous quantity. Furthermore, we propose a hybrid framework which employs continuous PLSA to mode...
متن کامل