Semantic Role Labelling without Deep Syntactic Parsing
نویسندگان
چکیده
This article proposes a method of Semantic Role Labelling for languages with no reliable deep syntactic parser and with limited corpora annotated with semantic roles. Reasonable results may be achieved with the help of shallow parsing, provided that features used for training such shallow parsers include both lexical semantic information (here: hypernymy) and syntactic information.
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 کاملJoint A* CCG Parsing and Semantic Role Labelling
Joint models of syntactic and semantic parsing have the potential to improve performance on both tasks—but to date, the best results have been achieved with pipelines. We introduce a joint model using CCG, which is motivated by the close link between CCG syntax and semantics. Semantic roles are recovered by labelling the deep dependency structures produced by the grammar. Furthermore, because C...
متن کاملJoint A∗ CCG Parsing and Semantic Role Labeling
Joint models of syntactic and semantic parsing have the potential to improve performance on both tasks—but to date, the best results have been achieved with pipelines. We introduce a joint model using CCG, which is motivated by the close link between CCG syntax and semantics. Semantic roles are recovered by labelling the deep dependency structures produced by the grammar. Furthermore, because C...
متن کاملAssigning Function Labels to Unparsed Text
In this paper, we propose a novel solution to the problem of assigning function labels to syntactic constituents. This task is a useful intermediate step between syntactic parsing and semantic role labelling. What distinguishes our proposal from other attempts in function or semantic role labelling is that we perform the learning of function labels at the same time as parsing. We reach state-of...
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 کامل