Semantic Labeling of Compound Nominalization in Chinese
نویسندگان
چکیده
This paper discusses the semantic interpretation of compound nominalizations in Chinese. We propose four coarse-grained semantic roles of the noun modifier and use a Maximum Entropy Model to label such relations in a compound nominalization. The feature functions used for the model are web-based statistics acquired via role related paraphrase patterns, which are formed by a set of word instances of prepositions, support verbs, feature nouns and aspect markers. By applying a sub-linear transformation and discretization of the raw statistics, a rate of approximately 77% is obtained for classification of the four semantic relations.
منابع مشابه
Extracting Knowledge from Text with PIKES
In this demonstration we showcase PIKES, a Semantic Role Labeling (SRL)-powered approach for Knowledge Extraction. PIKES implements a rule-based strategy that reinterprets SRL output in light of other linguistic analyses, such as dependency parsing and co-reference resolution, thus properly capturing and formalizing in RDF important linguistic aspects such as argument nominalization, frame-fram...
متن کاملVHR Semantic Labeling by Random Forest Classification and Fusion of Spectral and Spatial Features on Google Earth Engine
Semantic labeling is an active field in remote sensing applications. Although handling high detailed objects in Very High Resolution (VHR) optical image and VHR Digital Surface Model (DSM) is a challenging task, it can improve the accuracy of semantic labeling methods. In this paper, a semantic labeling method is proposed by fusion of optical and normalized DSM data. Spectral and spatial featur...
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 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 کاملNominalization in College English Writing
Nominalization is a very universal phenomenon in English written language as well as other languages. It has compact relations with written texts that it becomes one of important components in the formal style writing such as technical writing or legal writing. However, this does not mean that we should use as many nominalizations as possible in formal written texts. Overuse of nominalization i...
متن کامل