Semantic annotation of Italian legal texts

2011 ◽  
Vol 3 (1) ◽  
pp. 46-79 ◽  
Author(s):  
Giulia Venturi
Keyword(s):  
Knowledge Representation ◽  
Semantic Processing ◽  
Semantic Annotation ◽  
Semantic Content ◽  
Language Studies ◽  
Reliable Model ◽  
Legal Texts ◽  
Statistical Dependency ◽  
Legal Knowledge Representation ◽  
Dependency Parser

The FrameNet approach to text semantic annotation can be a reliable model to make the linguistic information and semantic content of legal texts explicit. This hypothesis is discussed and empirically demonstrated through a trial of annotating a corpus of Italian legal texts. This study aims to show that FrameNet is particularly appropriate to provide new perspectives for legal language studies and for legal knowledge representation tasks. Moreover, by relying on the output of a statistical dependency parser, the FrameNet-based annotation methodology presented here can be used successfully in the automatic semantic processing of legal texts.

Representation and Display of Digital Images of Cultural Heritage: A Semantic Enrichment Approach

2021 ◽  
Vol 48 (3) ◽  
pp. 231-247
Author(s):  
Xu Tan ◽  
Xiaoxi Luo ◽  
Xiaoguang Wang ◽  
Hongyu Wang ◽  
Xilong Hou
Keyword(s):  
Cultural Heritage ◽  
Digital Images ◽  
Semantic Network ◽  
Semantic Representation ◽  
Semantic Annotation ◽  
Visual Display ◽  
Semantic Content ◽  
Entity Recognition ◽  
Semantic Enrichment ◽  
Fine Grained

Digital images of cultural heritage (CH) contain rich semantic information. However, today’s semantic representations of CH images fail to fully reveal the content entities and context within these vital surrogates. This paper draws on the fields of image research and digital humanities to propose a systematic methodology and a technical route for semantic enrichment of CH digital images. This new methodology systematically applies a series of procedures including: semantic annotation, entity-based enrichment, establishing internal relations, event-centric enrichment, defining hierarchy relations between properties text annotation, and finally, named entity recognition in order to ultimately provide fine-grained contextual semantic content disclosure. The feasibility and advantages of the proposed semantic enrichment methods for semantic representation are demonstrated via a visual display platform for digital images of CH built to represent the Wutai Mountain Map, a typical Dunhuang mural. This study proves that semantic enrichment offers a promising new model for exposing content at a fine-grained level, and establishing a rich semantic network centered on the content of digital images of CH.

Distinguishing between Knowledge Visualization and Knowledge Rrpresentation in Legal Informatics

2009 ◽  
Author(s):  
Vytautas Čyras
Keyword(s):  
Knowledge Representation ◽  
Essential Feature ◽  
Semantic Networks ◽  
Knowledge Visualization ◽  
Management Processes ◽  
Object Based ◽  
Legal Informatics ◽  
The Subject ◽  
Legal Knowledge Representation ◽  
Legal Domain

Knowledge visualization (KV) and knowledge representation (KR) are distinguished, though both are knowledge management processes. Knowledge visualization is subject to humans, whereas knowledge representation – to computers. In computing, knowledge representation leverages reasoning of software agents. Thus, KR is a branch of artificial intelligence. The subject matter of KR is representation methods. They are classified into (1) knowledge level and symbol level representations; (2) procedural and declarative representations; (3) logic-based, rule-based, frame- or object-based representations (supporting inference by inheritance); and (4) semantic networks. In legal informatics, methods of legal knowledge representation (LKR) are dealt with. An essential feature of LKR is the representation of deep knowledge, which is mainly tacit. It is easily understood by professional jurists and hardly by amateurs from outside law. This knowledge comprises the teleology of law and a whole implicit framework of legal system. The paper focuses on (1) identifying key features of KV and KR in the legal domain; and (2) distinguishing between visualization, symbolization, formalisation and mind mapping.

scholarly journals NKRL, a knowledge representation tool for encoding the ‘meaning’ of complex narrative texts

1997 ◽  
Vol 3 (2) ◽  
pp. 231-253 ◽  
Author(s):  
GIAN PIERO ZARRI
Keyword(s):  
Knowledge Representation ◽  
Data Structures ◽  
Semantic Content ◽  
Narrative Texts ◽  
Knowledge Representation Language ◽  
Representation Language ◽  
Original Narrative

In this paper, we describe NKRL (Narrative Knowledge Representation Language), a language designed for representing, in a standardized way, the semantic content (the ‘meaning’) of complex narrative texts. After having introduced informally the four ‘components’ (specialized sub-languages) of NKRL, we will describe (some of) the data structures proper to each of them, trying to show that the NKRL coding retains the main informational elements of the original narrative expressions. We will then focus on an important subset of NKRL, the so-called AECS sub-language, showing in particular that the operators of this sub-language can be used to represent some sorts of ‘plural’ expressions.

scholarly journals Interplay between metrical and semantic processing in French: an N400 study

2019 ◽  
Author(s):  
Noémie te Rietmolen ◽  
Radouane El Yagoubi ◽  
Corine Astésano
Keyword(s):  
Speech Processing ◽  
Semantic Processing ◽  
Semantic Content ◽  
Speech Comprehension ◽  
Parietal Region ◽  
Metrical Structure ◽  
Semantic Congruity ◽  
Stress Processing ◽  
Semantic Conflict ◽  
Stress Patterns

AbstractFrench accentuation is held to belong to the level of the phrase. Consequently French is considered ‘a language without accent’ with speakers that are ‘deaf to stress’. Recent ERP-studies investigating the French initial accent (IA) however demonstrate listeners to not only discriminate between different stress patterns, but also expect words to be marked with IA early in the process of speech comprehension. Still, as words were presented in isolation, it remains unclear whether the preference applied to the lexical or to the phrasal level. In the current ERP-study, we address this ambiguity and manipulate IA on words embedded in a sentence. Furthermore, we orthogonally manipulate semantic congruity to investigate the interplay between accentuation and later speech processing stages. Results reveal an early fronto-centrally located negative deflection when words are presented without IA, indicating a general dispreference for words presented without IA. Additionally, we found an effect of semantic congruity in the centro-parietal region (the traditional region for N400), which was bigger for words without IA than for words with IA. Furthermore, we observed an interaction between metrical structure and semantic congruity such that ±IA continued to modulate N400 amplitude fronto-centrally, but only in the sentences that were semantically incongruent. The results indicate that presenting word without initial accent hinders semantic conflict resolution. This interpretation is supported by the behavioral data which show that participants were slower and made more errors words had been presented without IA. As participants attended to the semantic content of the sentences, the finding underlines the automaticity of stress processing and indicates that IA may be encoded at a lexical level where it facilitates semantic processing.

Research on the construction of the semantic model for Chinese ancient architectures based on architectural narratives

2020 ◽  
Vol 38 (4) ◽  
pp. 769-784
Author(s):  
Jinju Chen ◽  
Shiyan Ou
Keyword(s):  
Visual Analysis ◽  
Semantic Annotation ◽  
Semantic Content ◽  
Relevant Information ◽  
Semantic Model ◽  
Semantic Retrieval ◽  
Semantic Web Technologies ◽  
Semantic Reasoning ◽  
Content Type ◽  
Unstructured Information

Purpose This paper aims to reorganize the relevant information of Chinese ancient architectures with the use of Semantic Web technologies and thus facilitate its deep discovery and usage. Design/methodology/approach This paper proposes an ontology model for Chinese ancient architectures based on architectural narratives theory. To verify the availability of the ancient architecture ontology, we designed and implemented three experiments, including semantic retrieval based on SPARQL query, semantic reasoning with the use of Jena reasoner and visual analysis based on the Chinese Online Digital Humanities Resources Platform. Findings The proposed ontology provided a solution for the semantic annotation of the unstructured information of Chinese ancient architectures. On this basis, deep knowledge services such as semantic retrieval, semantic reasoning and visual analysis can be provided. Practical implications The proposed semantic model of ancient architectures can effectively improve the organization and access quality of the semantic content of Chinese ancient architectures. Originality/value This paper focuses on the semantic modelling for the unstructured information of Chinese ancient architectures to semantically describe the related entities (e.g. persons, events, places and times) and uncover their relationships, and thus it made contribution to the deep semantic annotations on ancient architectures.

"Narrative" Information Problems

2011 ◽  
pp. 1167-1172
Author(s):  
Gian Piero Zarri
Keyword(s):  
Economic Value ◽  
Real Life ◽  
Semantic Content ◽  
News Stories ◽  
Corporate Memory ◽  
Legal Texts ◽  
Computer Based ◽  
Cultural Heritage Material ◽  
Entertainment Value ◽  
Fictional Story

‘Narrative’ information concerns in general the account of some real-life or fictional story (a ‘narrative’) involving concrete or imaginary ‘personages’. In this article we deal with (multimedia) nonfictional narratives of an economic interest. This means, first, that we are not concerned with all sorts of fictional narratives that have mainly an entertainment value, and represent an imaginary narrator’s account of a story that happened in an imaginary world: a novel is a typical example of fictional narrative. Secondly, our ‘nonfictional narratives’ must have an economic value: they are then typically embodied into corporate memory documents, they concern news stories, normative and legal texts, medical records, intelligence messages, surveillance videos or visitor logs, actuality photos and video fragments for newspapers and magazines, eLearning and multimedia Cultural Heritage material, etc. Because of the ubiquity of these ‘narrative’, ‘dynamic’ resources, it is particularly important to build up computer-based applications able to represent and to exploit in a general, accurate, and effective way the semantic content – i.e., the key ‘meaning’ – of these resources.

Proactive inhibition and semantic organization Relationship with verbal memory in patients with schizophrenia

1996 ◽  
Vol 2 (6) ◽  
pp. 486-493 ◽  
Author(s):  
David A. Kareken ◽  
Paul J. Moberg ◽  
Ruben C. Gur
Keyword(s):  
Verbal Memory ◽  
Semantic Processing ◽  
Verbal Learning ◽  
Semantic Category ◽  
Semantic Content ◽  
Proactive Inhibition ◽  
California Verbal Learning Test ◽  
Retroactive Inhibition ◽  
Intrusion Errors ◽  
Learning Test

AbstractCompared to other cognitive functions in schizophrenia, evidence suggests that verbal memory is particularly impaired. This study used the California Verbal Learning Test (CVLT) to examine proactive inhibition (PI) and semantic processing in verbal memory in 29 patients with schizophrenia and 29 healthy controls. Patients showed significantly less PI, but also did not organize (cluster) their recall according to semantic category. Controls and patients demonstrated small retroactive inhibition (RI) effects regardless of semantic content. Although both groups made similar types and numbers of free recall intrusion errors, patients committed more phonemic and nonshared recognition errors. Results suggest that reduced semantic processing prevented build of PI, and contributes to defective memory in schizophrenia. The anatomic-physiologic abnormalities that underlie these findings may be particularly pronounced in prefrontal and temporal-parietal cortical areas. (JINS, 1996, 2, 486–493.)

Sign in / Sign up

Export Citation Format

Share Document