scholarly journals Semantic as well as referential relevance facilitates the processing of referring expressions.

2021 ◽  
Author(s):  
Catherine Davies ◽  
Anna Richardson
Keyword(s):  
Experimental Design ◽  
Processing Speed ◽  
Semantic Information ◽  
Semantic Representation ◽  
Noun Phrases ◽  
Referring Expressions ◽  
Adult Participants ◽  
Semantic Relevance ◽  
Redundancy Hypothesis ◽  
Affect Processing

A range of studies investigating how overspecified referring expressions (e.g., the stripy cup to describe a single cup) affect referent identification have found it to slow identification, speed it up, or yield no effect on processing speed. To date, these studies have all used adjectives that are semantically arbitrary within the sentential context.In addition to the standard ‘informativeness’ design that manipulates the presence of contrast sets, we controlled the semantic relevance of adjectives in discourse to reveal whether overspecifying adjectives would affect processing when relevant to the context (fed the hungry rabbit) compared to when they are not (tickled the hungry rabbit). Using a self-paced reading paradigm with a sample of adult participants (N=31), we found that overspecified noun phrases were read more slowly than those that distinguished a member of a contrast set. Importantly, this penalty was mitigated when adjectives were semantically relevant.Contrary to classical approaches, we show that modifiers do not necessarily presuppose a set, and that referential and semantic information is integrated rapidly in pragmatic processing. Our data support Fukumura and van Gompel’s (2017) meaning-based redundancy hypothesis, which predicts that it is the specific semantic representation of the overspecifying adjective that determines whether a penalty is incurred, rather than generic Gricean expectations. We extend this account using a novel experimental design.

scholarly journals The Semantics of Adjective Noun Phrases in the Human Brain

2016 ◽  
Author(s):  
Alona Fyshe ◽  
Gustavo Sudre ◽  
Leila Wehbe ◽  
Nicole Rafidi ◽  
Tom M. Mitchell
Keyword(s):  
Human Brain ◽  
Information Flow ◽  
Semantic Information ◽  
Semantic Representation ◽  
Noun Phrases ◽  
Brain Regions ◽  
Higher Order ◽  
Neural Representation ◽  
The Brain ◽  
Over Time

AbstractAs a person reads, the brain performs complex operations to create higher order semantic representations from individual words. While these steps are effortless for competent readers, we are only beginning to understand how the brain performs these actions. Here, we explore semantic composition using magnetoencephalography (MEG) recordings of people reading adjective-noun phrases presented one word at a time. We track the neural representation of semantic information over time, through different brain regions. Our results reveal two novel findings: 1) a neural representation of the adjective is present during noun presentation, but this neural representation is different from that observed during adjective presentation 2) the neural representation of adjective semantics observed during adjective reading is reactivated after phrase reading, with remarkable consistency. We also note that while the semantic representation of the adjective during the reading of the adjective is very distributed, the later representations are concentrated largely to temporal and frontal areas previously associated with composition. Taken together, these results paint a picture of information flow in the brain as phrases are read and understood.

scholarly journals Phonetics and Ambiguity Comprehension Gated Attention Network for Humor Recognition

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Xiaochao Fan ◽  
Hongfei Lin ◽  
Liang Yang ◽  
Yufeng Diao ◽  
Chen Shen ◽  
...  
Keyword(s):  
Neural Network ◽  
Semantic Information ◽  
Semantic Representation ◽  
Research Attention ◽  
Attention Network ◽  
Phonetic Information ◽  
The Neural Network ◽  
Ambiguous Words ◽  
Public Datasets

Humor refers to the quality of being amusing. With the development of artificial intelligence, humor recognition is attracting a lot of research attention. Although phonetics and ambiguity have been introduced by previous studies, existing recognition methods still lack suitable feature design for neural networks. In this paper, we illustrate that phonetics structure and ambiguity associated with confusing words need to be learned for their own representations via the neural network. Then, we propose the Phonetics and Ambiguity Comprehension Gated Attention network (PACGA) to learn phonetic structures and semantic representation for humor recognition. The PACGA model can well represent phonetic information and semantic information with ambiguous words, which is of great benefit to humor recognition. Experimental results on two public datasets demonstrate the effectiveness of our model.

scholarly journals Semantic Contextual Search Based on Conceptual Graphs over Encrypted Cloud

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Zhenghong Wang ◽  
Zhangjie Fu ◽  
Xingming Sun
Keyword(s):  
Semantic Information ◽  
Real Data ◽  
Context Information ◽  
Conceptual Graphs ◽  
Encryption Scheme ◽  
Semantic Extension ◽  
Computing Paradigm ◽  
Conceptual Graph ◽  
Semantic Relevance ◽  
Original Scheme

Currently, searchable encryption becomes the focus topic with the emerging cloud computing paradigm. The existing research schemes are mainly semantic extensions of multiple keywords. However, the semantic information carried by the keywords is limited and does not respond well to the content of the document. And when the original scheme constructs the conceptual graph, it ignores the context information of the topic sentence, which leads to errors in the semantic extension. In this paper, we define and construct semantic search encryption scheme for context-based conceptual graph (ESSEC). We make contextual contact with the central key attributes in the topic sentence and extend its semantic information, so as to improve the accuracy of the retrieval and semantic relevance. Finally, experiments based on real data show that the scheme is effective and feasible.

scholarly journals Incorporating Topological Representation in 3D City Models

2019 ◽  
Vol 8 (8) ◽  
pp. 347 ◽  
Author(s):  
Stelios Vitalis ◽  
Ken Ohori ◽  
Jantien Stoter
Keyword(s):  
Data Structure ◽  
Data Model ◽  
Semantic Information ◽  
Semantic Representation ◽  
Geometric Analysis ◽  
Computationally Efficient ◽  
Real World Data ◽  
3D City Models ◽  
Map Data ◽  
City Models

3D city models are being extensively used in applications such as evacuation scenarios and energy consumption estimation. The main standard for 3D city models is the CityGML data model which can be encoded through the CityJSON data format. CityGML and CityJSON use polygonal modelling in order to represent geometries. True topological data structures have proven to be more computationally efficient for geometric analysis compared to polygonal modelling. In a previous study, we have introduced a method to topologically reconstruct CityGML models while maintaining the semantic information of the dataset, based solely on the combinatorial map (C-Map) data structure. As a result of the limitations of C-Map’s semantic representation mechanism, the resulting datasets could suffer either from semantic information loss or the redundant repetition of them. In this article, we propose a solution for a more efficient representation of geometry, topology and semantics by incorporating the C-Map data structure into the CityGML data model and implementing a CityJSON extension to encode the C-Map data. In addition, we provide an algorithm for the topological reconstruction of CityJSON datasets to append them according to this extension. Finally, we apply our methodology to three open datasets in order to validate our approach when applied to real-world data. Our results show that the proposed CityJSON extension can represent all geometric information of a city model in a lossless way, providing additional topological information for the objects of the model.

Topic-Continuity and Topic-Shift Effects in Spanish Discourse

2016 ◽  
Vol 8 (1) ◽  
pp. 1-35
Author(s):  
Iker Zulaica-Hernández
Keyword(s):  
Noun Phrases ◽  
Direct Object ◽  
Personal Pronouns ◽  
Referring Expressions ◽  
Discourse Coherence ◽  
Narrow View ◽  
Topic Shift ◽  
Syntactic Prominence ◽  
Previous Sentence

Differences in use among referring expressions are usually explained on the basis of the cognitive accessibility of their antecedents, where antecedent accessibility has been operationalized differently in the literature; i.e. as a grammatical role, as syntactic prominence or as antecedent distance. On these grounds, it has been proposed that personal pronouns prefer topical antecedents whereas demonstratives prefer non-topical antecedents. This paper investigates the referring properties of Spanish demonstratives and direct object personal pronouns with the aim to unveil their differences and similarities. My analysis shows that these two expressions are very similar referentially when a narrow view of discourse context is considered. However, important differences show up when a broader notion of context is thrown into the picture; i.e. contexts that extend beyond the immediate previous sentence and beyond the immediate local topic of discourse. Based on my corpus evidence and on previous research on the pragmatic interpretation of referring expressions, I claim that direct object personal pronouns and demonstrative noun phrases crucially differ in the way they contribute to discourse coherence; the former playing the role of topic continuity markers and the latter focalising referents that reintroduce suspended or declining topics and marking (sub)-topic shifts in the discourse.

The production of referring expressions in oral narratives of Chinese–English bilingual speakers and monolingual peers

2012 ◽  
Vol 29 (1) ◽  
pp. 41-55 ◽  
Author(s):  
Liang Chen ◽  
Jianghua Lei
Keyword(s):  
Narrative Analysis ◽  
Picture Book ◽  
Noun Phrases ◽  
English Speakers ◽  
Bilingual Children ◽  
Referring Expressions ◽  
Bilingual Speakers ◽  
English Bilingual ◽  
Discourse Functions ◽  
Chinese And English

This study evaluates the extent to which the production of referring expressions such as noun phrases and pronouns to fulfill various discourse functions in narratives of Chinese–English bilingual children matches that of their monolingual peers in each of the two languages. Spoken narratives in English and Chinese were elicited from 30 9-year-old participants from each of the three groups: Chinese–English bilinguals and their monolingual peers in each of the two languages using the wordless picture book Frog, Where Are You? (Mayer, 1969). Narrative analysis focused on the referring expressions that are used to introduce, re-introduce, and maintain reference to story characters in the narratives. Results show that (1) monolingual Chinese and English speakers differed significantly in the preferred referring expressions for the discourse functions; (2) the Chinese–English bilinguals differed from their monolingual peers in the distribution of referring expressions for referent introduction in English and re-introduction in Chinese; and (3) bilinguals resembled their monolingual peers in their differentiated use of referring expressions for referent maintenance in each of the two languages. These results suggest that the patterns of production of referring expressions in discourse by bilingual speakers may be unique, and fall in between those by their monolingual peers in each of the languages.

Ontology-Based Workflow Semantic Representation and Modeling Method

2010 ◽  
Vol 129-131 ◽  
pp. 50-54
Author(s):  
Wei Ping Shao ◽  
Chun Yan Wang ◽  
Yong Ping Hao ◽  
Peng Fei Zeng ◽  
Xiao Lei Xu
Keyword(s):  
Semantic Information ◽  
Semantic Representation ◽  
Modeling Method ◽  
Workflow Model ◽  
Structure Information ◽  
Ontology Modeling ◽  
Ontology Model ◽  
Representation Method

An ontology-based workflow (workflow-ontology) representation method was proposed after analyzing that not only structure information but also semantic information were needed in a workflow model. Workflow-ontology concepts were composed by class and subclass of the workflow. Concepts’ properties including their values and characteristics were redefined, and then, workflow-ontology modeling method was put forward based on the ontology expresses and definitions above. With the example of applying in products examined and approved workflows, the corresponding workflow-ontology model (WFO) was built.

scholarly journals Path-based knowledge reasoning with textual semantic information for medical knowledge graph completion

2021 ◽  
Vol 21 (S9) ◽  
Author(s):  
Yinyu Lan ◽  
Shizhu He ◽  
Kang Liu ◽  
Xiangrong Zeng ◽  
Shengping Liu ◽  
...  
Keyword(s):  
Semantic Information ◽  
State Of The Art ◽  
Semantic Representation ◽  
Medical Knowledge ◽  
The State ◽  
Language Models ◽  
Knowledge Graph ◽  
Knowledge Reasoning ◽  
Numerical Computing ◽  
Knowledge Graphs

Abstract Background Knowledge graphs (KGs), especially medical knowledge graphs, are often significantly incomplete, so it necessitating a demand for medical knowledge graph completion (MedKGC). MedKGC can find new facts based on the existed knowledge in the KGs. The path-based knowledge reasoning algorithm is one of the most important approaches to this task. This type of method has received great attention in recent years because of its high performance and interpretability. In fact, traditional methods such as path ranking algorithm take the paths between an entity pair as atomic features. However, the medical KGs are very sparse, which makes it difficult to model effective semantic representation for extremely sparse path features. The sparsity in the medical KGs is mainly reflected in the long-tailed distribution of entities and paths. Previous methods merely consider the context structure in the paths of knowledge graph and ignore the textual semantics of the symbols in the path. Therefore, their performance cannot be further improved due to the two aspects of entity sparseness and path sparseness. Methods To address the above issues, this paper proposes two novel path-based reasoning methods to solve the sparsity issues of entity and path respectively, which adopts the textual semantic information of entities and paths for MedKGC. By using the pre-trained model BERT, combining the textual semantic representations of the entities and the relationships, we model the task of symbolic reasoning in the medical KG as a numerical computing issue in textual semantic representation. Results Experiments results on the publicly authoritative Chinese symptom knowledge graph demonstrated that the proposed method is significantly better than the state-of-the-art path-based knowledge graph reasoning methods, and the average performance is improved by 5.83% for all relations. Conclusions In this paper, we propose two new knowledge graph reasoning algorithms, which adopt textual semantic information of entities and paths and can effectively alleviate the sparsity problem of entities and paths in the MedKGC. As far as we know, it is the first method to use pre-trained language models and text path representations for medical knowledge reasoning. Our method can complete the impaired symptom knowledge graph in an interpretable way, and it outperforms the state-of-the-art path-based reasoning methods.

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