Automatic Semantic Role Labeling

نویسندگان

  • Wen-tau Yih
  • Kristina Toutanova
چکیده

The goal of semantic role labeling is to map sentences to domain-independent semantic representations, which abstract away from syntactic structure and are important for deep NLP tasks such as question answering, textual entailment, and complex information extraction. Semantic role labeling has recently received significant interest in the natural language processing community. In this tutorial, we will first describe the problem and history of semantic role labeling, and introduce existing corpora and other related tasks. Next, we will provide a detailed survey of state-of-the-art machine learning approaches to building a semantic role labeling system. Finally, we will conclude the tutorial by discussing directions for improving semantic role labeling systems and their application to other natural language problems.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

برچسب‌زنی 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 کامل

برچسب‌زنی 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 کامل

XARA: An XML- and Rule-based Semantic Role Labeler

XARA is a rule-based PropBank labeler for Alpino XML files, written in Java. I used XARA in my research on semantic role labeling in a Dutch corpus to bootstrap a dependency treebank with semantic roles. Rules in XARA are based on XPath expressions, which makes it a versatile tool that is applicable to other treebanks as well. In addition to automatic role annotation, XARA is able to extract tr...

متن کامل

Labeling Chinese Predicates with Semantic Roles

In this article we report work on Chinese semantic role labeling, taking advantage of two recently completed corpora, the Chinese PropBank, a semantically annotated corpus of Chinese verbs, and the Chinese Nombank, a companion corpus that annotates the predicate–argument structure of nominalized predicates. Because the semantic role labels are assigned to the constituents in a parse tree, we fi...

متن کامل

Issues In Synchronizing The English Treebank And PropBank

The PropBank primarily adds semantic role labels to the syntactic constituents in the parsed trees of the Treebank. The goal is for automatic semantic role labeling to be able to use the domain of locality of a predicate in order to find its arguments. In principle, this is exactly what is wanted, but in practice the PropBank annotators often make choices that do not actually conform to the Tre...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2006