Collectivist semantics

2021 ◽  
pp. 303-319
Author(s):  
Dick Crouch ◽  
Aikaterini-Lida Kalouli
Keyword(s):  
Knowledge Representation ◽  
Natural Language ◽  
Semantic Representation ◽  
Formal Semantics ◽  
Fine Grained ◽  
Computational Implementation ◽  
Graphical Knowledge

The Graphical Knowledge Representation was introduced as a graph-based semantic representation for natural language. Although its computational implementation has already been presented, a formal account for the semantics behind the representation is still missing. This chapter seeks to fill this gap by proposing a formal semantics for the representation. The main proposal of the semantics is to take concepts, and not individuals, as primitive and thus achieve a fine-grained intensional semantics. This chapter explores how such a collectivist semantics can come about and how it can be employed within the Graphical Knowledge Representation.

scholarly journals Multi-Point Semantic Representation for Intent Classification

2020 ◽  
Vol 34 (05) ◽  
pp. 9531-9538
Author(s):  
Jinghan Zhang ◽  
Yuxiao Ye ◽  
Yue Zhang ◽  
Likun Qiu ◽  
Bin Fu ◽  
...  
Keyword(s):  
Natural Language ◽  
State Of The Art ◽  
Semantic Representation ◽  
Coarse Grained ◽  
Semantic Parsing ◽  
Language Understanding ◽  
Attention Model ◽  
Object Condition ◽  
Fine Grained ◽  
Query Type

Detecting user intents from utterances is the basis of natural language understanding (NLU) task. To understand the meaning of utterances, some work focuses on fully representing utterances via semantic parsing in which annotation cost is labor-intentsive. While some researchers simply view this as intent classification or frequently asked questions (FAQs) retrieval, they do not leverage the shared utterances among different intents. We propose a simple and novel multi-point semantic representation framework with relatively low annotation cost to leverage the fine-grained factor information, decomposing queries into four factors, i.e., topic, predicate, object/condition, query type. Besides, we propose a compositional intent bi-attention model under multi-task learning with three kinds of attention mechanisms among queries, labels and factors, which jointly combines coarse-grained intent and fine-grained factor information. Extensive experiments show that our framework and model significantly outperform several state-of-the-art approaches with an improvement of 1.35%-2.47% in terms of accuracy.

scholarly journals Representation and Presentation of Culinary Tradition as Cultural Heritage

Heritage ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 612-640
Author(s):  
Nikolaos Partarakis ◽  
Danai Kaplanidi ◽  
Paraskevi Doulgeraki ◽  
Effie Karuzaki ◽  
Argyro Petraki ◽  
...  
Keyword(s):  
Social Sciences ◽  
Knowledge Representation ◽  
Cultural Heritage ◽  
Systematic Approach ◽  
Semantic Representation ◽  
Ethnographic Research ◽  
Abstract Form ◽  
Web Based ◽  
Social Sciences And Humanities ◽  
Authoring Environment

This paper presents a knowledge representation framework and provides tools to allow the representation and presentation of the tangible and intangible dimensions of culinary tradition as cultural heritage including the socio-historic context of its evolution. The representation framework adheres to and extends the knowledge representation standards for the Cultural Heritage (CH) domain while providing a widely accessible web-based authoring environment to facilitate the representation activities. In strong collaboration with social sciences and humanities, this work allows the exploitation of ethnographic research outcomes by providing a systematic approach for the representation of culinary tradition in the form of recipes, both in an abstract form for their preservation and in a semantic representation of their execution captured on-site during ethnographic research.

scholarly journals Datives with psych nouns and adjectives in Basque

2020 ◽  
Vol 54 (3) ◽  
pp. 647-696
Author(s):  
Beatriz Fernández ◽  
Fernando Zúñiga ◽  
Ane Berro
Keyword(s):  
Natural Language ◽  
Linguistic Theory ◽  
Psychological State ◽  
Formal Expression ◽  
Fine Grained ◽  
Psych Verbs ◽  
Other Regarding ◽  
Psychological Verbs

Abstract This paper explores the formal expression of two Basque dative argument types in combination with psych nouns and adjectives, in intransitive and transitive clauses: (i) those that express the experiencer, and (ii) those that express the stimulus of the psychological state denoted by the psych noun and adjective. In the intransitive structure involving a dative experiencer (DatExpIS), the stimulus is in the absolutive case, and the intransitive copula izan ‘be’ shows both dative and absolutive agreement. This construction basically corresponds to those built upon the piacere type of psychological verbs typified in (Belletti, Adriana & Luigi Rizzi. 1988. Psych-verbs and θ-theory. Natural Language and Linguistic Theory 6. 291–352) three-way classification of Italian psych verbs. In the intransitive structure involving a dative stimulus (DatStimIS), the experiencer is marked by absolutive case, and the same intransitive copula shows both absolutive and dative agreement (with the latter corresponding to the dative stimulus and not to the experiencer). We show that the behavior of the dative argument in the two constructions is just the opposite of each other regarding a number of morphosyntactic tests, including agreement, constituency, hierarchy and selection. Additionally, we explore two parallel transitive constructions that involve either a dative experiencer and an ergative stimulus (DatExpTS) or a dative stimulus and an ergative experiencer (DatStimTS), which employ the transitive copula *edun ‘have’. Considering these configurations, we propose an extended and more fine-grained typology of psych predicates.

Agile sensor tasking for CoIST using natural language knowledge representation and reasoning

2014 ◽  
Author(s):  
David Braines ◽  
Geeth de Mel ◽  
Chris Gwilliams ◽  
Christos Parizas ◽  
Diego Pizzocaro ◽  
...  
Keyword(s):  
Knowledge Representation ◽  
Natural Language ◽  
Knowledge Representation And Reasoning ◽  
Language Knowledge ◽  
Sensor Tasking
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