Learning for Semantic Parsing
نویسنده
چکیده
Semantic parsing is the task of mapping a natural language sentence into a complete, formal meaning representation. Over the past decade, we have developed a number of machine learning methods for inducing semantic parsers by training on a corpus of sentences paired with their meaning representations in a specified formal language. We have demonstrated these methods on the automated construction of naturallanguage interfaces to databases and robot command languages. This paper reviews our prior work on this topic and discusses directions for future research.
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 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 کاملOnline Structured Learning for Semantic Parsing with Synchronous and λ−Synchronous Context Free Grammars
We formulate semantic parsing as a parsing problem on a synchronous context free grammar (SCFG) which is automatically built on the corpus of natural language sentences and the representation of semantic outputs. We then present an online learning framework for estimating the synchronous SCFG grammar. In addition, our online learning methods for semantic parsing problems are also extended to de...
متن کاملMachine Learning for Semantic Parsing in Review
Spoken Language Understanding (SLU) and more specifically, semantic parsing is an indispensable task in each speech-enabled application. In this survey, we review the current research on SLU and semantic parsing with emphasis on machine learning techniques used for these tasks. Observing the current trends in semantic parsing, we conclude our discussion by suggesting some of the most promising ...
متن کاملTransfer Learning for Neural Semantic Parsing
The goal of semantic parsing is to map natural language to a machine interpretable meaning representation language (MRL). One of the constraints that limits full exploration of deep learning technologies for semantic parsing is the lack of sufficient annotation training data. In this paper, we propose using sequence-to-sequence in a multi-task setup for semantic parsing with a focus on transfer...
متن کاملEmpirically-motivated Generalizations of CCG Semantic Parsing Learning Algorithms
Learning algorithms for semantic parsing have improved drastically over the past decade, as steady improvements on benchmark datasets have shown. In this paper we investigate whether they can generalize to a novel biomedical dataset that differs in important respects from the traditional geography and air travel benchmark datasets. Empirical results for two state-of-the-art PCCG semantic parser...
متن کامل