Towards Comparability of Linguistic Graph Banks for Semantic Parsing
نویسندگان
چکیده
We announce a new language resource for research on semantic parsing, a large, carefully curated collection of semantic dependency graphs representing multiple linguistic traditions. This resource is called SDP 2016 and provides an update and extension to previous versions used as Semantic Dependency Parsing target representations in the 2014 and 2015 Semantic Evaluation Exercises (SemEval). For a common core of English text, this third edition comprises semantic dependency graphs from four distinct frameworks, packaged in a unified abstract format and aligned at the sentence and token levels. SDP 2016 is the first general release of this resource and available for licensing from the Linguistic Data Consortium from May 2016. The data is accompanied by an open-source SDP utility toolkit and system results from previous contrastive parsing evaluations against these target representations.
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 کاملSemantic construction with graph grammars
We introduce s-graph grammars, a new grammar formalism for computing graph-based semantic representations. Semantically annotated corpora which use graphs as semantic representations have recently become available, and there have been a number of data-driven systems for semantic parsing that can be trained on these corpora. However, it is hard to map the linguistic assumptions of these systems ...
متن کاملJoint learning of dependency parsing and semantic role labeling
When natural language processing tasks overlap in their linguistic input space, they can be technically merged. Applying machine learning algorithms to the new joint task and comparing the results of joint learning with disjoint learning of the original tasks may bring to light the linguistic relatedness of the two tasks. We present a joint learning experiment with dependency parsing and semant...
متن کاملAntelop: an industrial platform for linguistic processing
The Antelope linguistic platform, inspired by Meaning-Text Theory, targets the syntactic and semantic analysis of texts, and can handle large corpora. Antelope integrates several pre-existing (parsing) components as well as broad-coverage linguistic data originating from various sources. Efforts towards integration of all components nonetheless make for a homogeneous platform. Our direct contri...
متن کاملSemi-Supervised and Latent-Variable Models of Natural Language Semantics
This thesis focuses on robust analysis of natural language semantics. A primary bottleneck for semantic processing of text lies in the scarcity of high-quality and large amounts of annotated data that provide complete information about the semantic structure of natural language expressions. In this dissertation, we study statistical models tailored to solve problems in computational semantics, ...
متن کامل