scholarly journals A Simplified Model for Assembly Precision Information of Complex Products Based on Tolerance Semantic Relations

2018 ◽  
Vol 10 (12) ◽  
pp. 4482 ◽  
Author(s):  
Xiaolin Shi ◽  
Xitian Tian ◽  
Gangfeng Wang ◽  
Min Zhang ◽  
Dongping Zhao
Keyword(s):  
Semantic Relation ◽  
Assembly Sequence Planning ◽  
Semantic Relations ◽  
Practical Significance ◽  
Geometric Feature ◽  
Complex Products ◽  
Design Tolerance ◽  
Structure Knowledge ◽  
Whole Life Cycle ◽  
Assembly Precision

Assembly precision analysis (APA) plays an important role in the whole life cycle of complex products design, manufacturing, assembly and even remanufacturing. Assembly precision information model (APIM) is usually complex since it is affected by many factors, such as design tolerance of parts, assembly process scheme, assembly sequence planning and tolerance of positioning tooling, etc. Therefore, it is of practical significance for APA to reasonably reduce the workload of assembly precision information (API) modeling. A semantic simplification approach for APIM is proposed in this paper, which mainly takes semantic relations between APIM and design tolerance of parts into consideration. Initially, ontology of structure knowledge of APIM is constructed according to a tolerance standard. Furthermore, simplification rules are respectively established by considering two semantic relations: one semantic relation between deviation change direction and deviation accumulation direction and the other semantic relation among multiple geometric characteristics on the same geometric feature. Additionally, by utilizing ontology reasoning function, the simplified semantic APIM is generated. Finally, the effectiveness of the proposed method is demonstrated by a practical example of engine front auxiliary drive equipment. It is expected that our work would lay the foundation for APA of complex products based on actual measured data.

Using multiple Web resources and inference rules to classify Chinese word semantic relation

2018 ◽  
Vol 46 (2) ◽  
pp. 120-126 ◽  
Author(s):  
Shutian Ma ◽  
Yingyi Zhang ◽  
Chengzhi Zhang
Keyword(s):  
Language Processing ◽  
Data Access ◽  
Semantic Relation ◽  
Semantic Relations ◽  
Primary System ◽  
Chinese Word ◽  
Lexical Resources ◽  
Data Set ◽  
Content Type ◽  
External Resources

Purpose The purpose of this paper is to classify Chinese word semantic relations, which are synonyms, antonyms, hyponyms and meronymys. Design/methodology/approach Basically, four simple methods are applied, ontology-based, dictionary-based, pattern-based and morpho-syntactic method. The authors make good use of search engine to build lexical and semantic resources for dictionary-based and pattern-based methods. To improve classification performance with more external resources, they also classify the given word pairs in Chinese and in English at the same time by using machine translation. Findings Experimental results show that the approach achieved an average F1 score of 50.87 per cent, an average accuracy of 70.36 per cent and an average recall of 40.05 per cent over all classification tasks. Synonym and antonym classification achieved high accuracy, i.e. above 90 per cent. Moreover, dictionary-based and pattern-based approaches work effectively on final data set. Originality/value For many natural language processing (NLP) tasks, the step of distinguishing word semantic relation can help to improve system performance, such as information extraction and knowledge graph generation. Currently, common methods for this task rely on large corpora for training or dictionaries and thesauri for inference, where limitation lies in freely data access and keeping built lexical resources up-date. This paper builds a primary system for classifying Chinese word semantic relations by seeking new ways to obtain the external resources efficiently.

Applying Semantic Relations for Automatic Topic Ontology Construction

2018 ◽  
pp. 48-77 ◽  
Author(s):  
Subramaniyaswamy Vairavasundaram ◽  
Logesh R.
Keyword(s):  
Clustering Algorithm ◽  
Relation Extraction ◽  
Semantic Relation ◽  
Semantic Relations ◽  
Construction Process ◽  
Ontology Construction ◽  
Web Documents ◽  
Mapping Algorithm ◽  
Novel Approach ◽  
Improved Performance

The rapid growth of web technologies had created a huge amount of information that is available as web resources on Internet. Authors develop an automatic topic ontology construction process for better topic classification and present a corpus based novel approach to enrich the set of categories in the ODP by automatically identifying concepts and their associated semantic relationships based on external knowledge from Wikipedia and WordNet. The topic ontology construction process relies on concept acquisition and semantic relation extraction. Initially, a topic mapping algorithm is developed to acquire the concepts from Wikipedia based on semantic relations. A semantic similarity clustering algorithm is used to compute similarity to group the set of similar concepts. The semantic relation extraction algorithm derives associated semantic relations between the set of extracted topics from the lexical patterns in WordNet. The performance of the proposed topic ontology is evaluated for the classification of web documents and obtained results depict the improved performance over ODP.

Bootstrapping of Semantic Relation Extraction for a Morphologically Rich Language

2019 ◽  
Vol 15 (1) ◽  
pp. 119-149
Author(s):  
Balaji Jagan ◽  
Ranjani Parthasarathi ◽  
T V Geetha
Keyword(s):  
Relation Extraction ◽  
Semantic Relation ◽  
Semantic Relations ◽  
Semantic Constraints ◽  
Order Language ◽  
Fixed Order ◽  
Universal Networking Language ◽  
Rule Based Approach ◽  
Domain Independent ◽  
Free Word

This article focuses on the use of a bootstrapping approach for the extraction of semantic relations that exist between two different concepts in a Tamil text. The proposed system, bootstrapping approach to semantic UNL relation extraction (BASURE) extracts generic relations that exist between different components of a sentence by exploiting the morphological richness of Tamil. Tamil is essentially a partially free word order language which means that semantic relations that exist between the concepts can occur anywhere in the sentence not necessarily in a fixed order. Here, the authors use Universal Networking Language (UNL), an Interlingua framework, to represent the word-based features and aim to define UNL semantic relations that exist between any two constituents in a sentence. The morphological suffix, lexical category and UNL semantic constraints associated with a word are defined as tuples of the pattern used for bootstrapping. Most systems define the initial set of seed patterns manually. However, this article uses a rule-based approach to obtain word-based features that form tuples of the patterns. A bootstrapping approach is then applied to extract all possible instances from the corpus and to generate new patterns. Here, the authors also introduce the use of UNL ontology to discover the semantic similarity between semantic tuples of the pattern, hence, to learn new patterns from the text corpus in an iterative manner. The use of UNL Ontology makes this approach general and domain independent. The results obtained are evaluated and compared with existing approaches and it has been shown that this approach is generic, can extract all sentence based semantic UNL relations and significantly increases the performance of the generic semantic relation extraction system.

Research on the Profile Error Evaluation and Visualization of Free-Form Surface

2010 ◽  
Vol 43 ◽  
pp. 560-564 ◽  
Author(s):  
Na Su ◽  
Hui Guo
Keyword(s):  
Minimum Distance ◽  
Practical Significance ◽  
Free Form ◽  
Geometric Feature ◽  
Error Evaluation ◽  
Profile Error ◽  
Swarm Optimization ◽  
B Spline ◽  
Free Form Surface ◽  
The Mathematical Model

To solve the problem of evaluating profile error of surface, theoretical surface was built by interpolating design points at the method of bicubic Non-Uniform Rational B-Spline(NURBS). Measuring points were gained by laser measurement, and the mathematical model was built for computing the error. The particle swarm optimization (PSO) was applied to compute the minimum distance from measuring points to design surface, which can evaluate profile error of surface accurately. At the same time, MATLAB software was used to realize visualization of profile error evaluation of free-form surface. Experiments show that the proposed optimization can obtain precise result, the method is feasible, visualization makes geometric feature observed more intuitive and there is important practical significance.

scholarly journals What's in a compound?

2011 ◽  
Vol 47 (2) ◽  
pp. 481-507 ◽  
Author(s):  
ANDREW SPENCER
Keyword(s):  
Fixed Number ◽  
Head Noun ◽  
Semantic Relation ◽  
Semantic Relations ◽  
Noun Compounds ◽  
General Semantic ◽  
The Way

The Oxford Handbook of Compoundingsurveys a variety of theoretical and descriptive issues, presenting overviews of compounding in a number of frameworks and sketches of compounding in a number of languages. Much of the book deals with Germanic noun–noun compounding. I take up some of the theoretical questions raised surrounding such constructions, in particular, the notion of attributive modification in noun-headed compounds. I focus on two issues. The first is the semantic relation between the head noun and its nominal modifier. Several authors repeat the argument that there is a small(-ish) fixed number of general semantic relations in noun–noun compounds (‘Lees's solution’), but I argue that the correct way to look at such compounds is what I call ‘Downing's solution’, in which we assume that the relation is specified pragmatically, and hence could be any relation at all. The second issue is the way that adjectives modify nouns inside compounds. Although there are languages in which compounded adjectives modify just as they do in phrases (Chukchee, Arleplog Swedish), in general the adjective has a classifier role and not that of a compositional attributive modifier. Thus, even if an English (or German) adjective–noun compound looks compositional, it isn't.

scholarly journals Some new insights into the semantics of English N+N compounds

2021 ◽  
Author(s):  
◽  
Elizaveta Tarasova
Keyword(s):  
Semantic Content ◽  
Semantic Relation ◽  
Semantic Relations ◽  
Cognitive Grammar ◽  
Corpus Study ◽  
Printed Media ◽  
Morphological Productivity ◽  
Quantitative And Qualitative Methods ◽  
N Compounds ◽  
The Way

<p>This thesis focuses on English N+N compounds and the primary purpose of the study is to investigate the way in which compounded structures acquire their meaning and to check the way in which the semantics of each of the constituents contributes to the overall meaning of the structure. The way in which such contributions are made should be inferable from the linguistic analysis of the structure and meaning of compounds. In order to do this, the thesis looks first at the morphological productivity of the constituents comprising a compound. The second aim is to identify whether the productivity of a compound constituent on the morphological level coincides with the productivity of the semantic relation realised in the constituent family. The discussion of the results obtained from a corpus study provides plausible explanations for the regularities noted in the course of the analysis by using some of the relevant principles from the complex of approaches including the Construction Grammar and Cognitive Grammar approaches. Examples of compounds were collected from the printed media (NZ broadsheets) and the BNC. The analysis of the data used both quantitative and qualitative methods. The quantitative analysis of the data confirms two hypotheses: (1) that a constituent is more productive in just one of the positions (modifier or head), and (2) the more productive a constituent is, the more likely it is to realise a single semantic relation in a constituent family. The qualitative analysis involves consideration of the semantic content of the concepts in each constituent in order to see how this content is reflected in the semantic relations realised by a constituent. It is discovered that the semantic content of the head is a stronger predictor of the relation realised in a compound than that of the modifier. The study is important in order to better understand the factors that govern the formation of compounds and the patterns that speakers use in the process of coining complex lexical items ...</p>

scholarly journals SynoExtractor: A Novel Pipeline for Arabic Synonym Extraction Using Word2Vec Word Embeddings

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Rawan N. Al-Matham ◽  
Hend S. Al-Khalifa
Keyword(s):  
Language Processing ◽  
Question Answering ◽  
Semantic Relation ◽  
Semantic Relations ◽  
Word Embeddings ◽  
Average Precision ◽  
King Saud University ◽  
Similar Word ◽  
Linguistic Rules ◽  
Classical Arabic

Automatic synonym extraction plays an important role in many natural language processing systems, such as those involving information retrieval and question answering. Recently, research has focused on extracting semantic relations from word embeddings since they capture relatedness and similarity between words. However, using word embeddings alone poses problems for synonym extraction because it cannot determine whether the relation between words is synonymy or some other semantic relation. In this paper, we present a novel solution for this problem by proposing the SynoExtractor pipeline, which can be used to filter similar word embeddings to retain synonyms based on specified linguistic rules. Our experiments were conducted using KSUCCA and Gigaword embeddings and trained with CBOW and SG models. We evaluated automatically extracted synonyms by comparing them with Alma’any Arabic synonym thesauri. We also arranged for a manual evaluation by two Arabic linguists. The results of experiments we conducted show that using the SynoExtractor pipeline enhances the precision of synonym extraction compared to using the cosine similarity measure alone. SynoExtractor obtained a 0.605 mean average precision (MAP) for the King Saud University Corpus of Classical Arabic with 21% improvement over the baseline and a 0.748 MAP for the Gigaword corpus with 25% improvement. SynoExtractor outperformed the Sketch Engine thesaurus for synonym extraction by 32% in terms of MAP. Our work shows promising results for synonym extraction suggesting that our method can also be used with other languages.

scholarly journals THE ANALYSIS OF CLAUSE COMPLEX OF ANALYTICAL EXPOSITION TEXTS WRITTEN BY THE ENGLISH TEACHERS OF SMKN 2 PALANGKA RAYA

2021 ◽  
Vol 4 (2) ◽  
pp. 24
Author(s):  
Charly Kurniawan ◽  
Maria Arina Luardini ◽  
Elanneri Karani
Keyword(s):  
Learning Process ◽  
Systemic Functional Linguistics ◽  
Descriptive Study ◽  
English Teachers ◽  
Functional Approach ◽  
Semantic Relation ◽  
Semantic Relations ◽  
Teaching English ◽  
Teaching Learning ◽  
Clause Complex

This study was conducted to investigate the analysis of clause complex of analytical exposition text written by the English teachers of SMKN 2 Palangka Raya. Considering that teachers have important role in education field, in which teachers have a function as the model in teaching learning process, especially in teaching English and it is the consideration that the subjects of the study will be the English teachers. The study design of this study was descriptive qualitative. By means of a descriptive study under Systemic Functional Linguistics theory as suggested by Halliday & Matthiessen (2014), the writers employ the analysis of taxis systems which cover elaboration, extension, and enhancement. Besides, logico-semantic relation is also analyzed which covers projection and expansion The data will be analyzed through systemic functional approach in which the analysis of the data will be based the clauses and its taxis (protasis and hypotaxis) along with its logico-semantic relations from text by the teacher.

scholarly journals I believe that he didn’t do it and I don’t believe that he did it. The influence of context on the semantic-communicative relations between sentence negation and performative negation

2018 ◽  
Vol 54 (1) ◽  
pp. 61-76
Author(s):  
Józef Maciuszek
Keyword(s):  
Subject Matter ◽  
Speech Acts ◽  
Contextual Factors ◽  
Empirical Studies ◽  
Semantic Relation ◽  
Semantic Relations ◽  
Situational Context ◽  
Negative Comment ◽  
The Subject ◽  
Negative Expectations

Abstract The subject matter of the paper is an analysis of the semantic relations between sentence negation, performative negation, and declarations in reference to utterances which speech acts theory gives the label of representatives. Apart from linguistic-semantic analyses, empirical studies have been conducted on the manner in which sentence negation and performative negation are processed. The results of Study I demonstrate that the semantic relation between sentence negation and performative negation changes depend on the type of comment (positive vs. negative), and contextual factors (type of expectations towards events being commented on). As it turned out, when the situational context suggests a negative comment by the sender, participants offer similar interpretations of utterances with sentence negation and performative negation. In Study II the participants assessed the likelihood of the occurrence of the facts spoken of by a sender who uses sentence negation or performative negation. In a context suggesting positive utterances by the sender, a clear difference emerged between sentence negation and performative negation. This difference was not present in respect of negative expectations. The results achieved confirm the assumptions of the model of conversational inference regarding the influence of context on interpretation of a message. The recorded results indicate the semantic relations between declarations, sentence negation, and performative negation, which change depending on the affective significance of the message and contextual factors.

Returning Meaning to Semantic Relations

1983 ◽  
Vol 48 (1) ◽  
pp. 25-36 ◽  
Author(s):  
Laurence B. Leonard ◽  
Karen F. Steckol ◽  
Kathy M. Panther
Keyword(s):  
Detailed Analysis ◽  
Clinical Setting ◽  
Clinical Information ◽  
Spontaneous Speech ◽  
Semantic Relation ◽  
Semantic Relations ◽  
Word Combination

Two approaches for analyzing the meanings reflected in children's early word combinations are illustrated for possible application in the clinical setting. The first approach is interpretive in nature and involves detailed analysis of spontaneous speech. The second approach uses individualized probes that are designed to isolate the semantic factors involved in children's word combination patterns. Although both approaches have limitations, it is argued that each provides significantly more clinical information than assigning a child's utterances to predetermined semantic relation categories.

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