Hierarchical Network Of Concepts Research Articles

English Long Sentence Segmentation and Translation Optimization of Professional Literature Based on Hierarchical Network of Concepts

The structure of English long sentences is generally complicated, with complicated logical levels, many parallel elements, many modifiers and conjunctions, and long post attributives. Moreover, pronouns in English long sentences often need to be judged according to the context. Complex English long sentences are very common in English and even run through the whole English article. The analysis results of these complex long sentences will seriously affect the quality and readability of machine translation. In this paper, HNC (hierarchical network of concepts) method is improved to realize the segmentation of long sentences, so as to simplify sentence patterns. According to the characteristics of professional literature, this paper puts forward a translation optimization method of professional literature combining HNC with statistics, which greatly improves the recognition efficiency of unknown words by extracting professional terms. The results show that the word segmentation method based on HNC and statistics proposed in this paper has achieved a good word segmentation effect in the open test environment, with an accuracy rate of 93.38% and a recall rate of 94.51%. The conclusion shows that our method can make full use of the knowledge of the source tree database, thereby improving the accuracy of the syntactic model on the target tree database.

Quantitative Semantic Analysis and Comprehension by Cognitive Machine Learning

Knowledge learning is the sixth and the most fundamental category of machine learning mimicking the brain. It is recognized that the semantic space of machine knowledge is a hierarchical concept network (HCN), which can be rigorously represented by formal concepts in concept algebra and semantic algebra. This paper presents theories and algorithms of hierarchical concept classification by quantitative semantic analysis based on machine learning. Semantic equivalence between formal concepts is rigorously measured by an Algorithm of Concept Equivalence Analysis (ACEA). The semantic hierarchy among formal concepts is quantitatively determined by an Algorithm of Relational Semantic Classification (ARSC). Experiments applying Algorithms ACEA and ARSC on a set of formal concepts have been successfully conducted, which demonstrate a deep machine understanding of formal concepts and quantitative relations in the hierarchical semantic space by machine learning beyond human empirical perspectives.

English Semantic Feature Processing and Sentence Structure Analysis Based on Hierarchical Network of Concepts

Current English-Chinese translation machine can’t understand the source sentence fully. The theory of hierarchical network of concepts is proposed for resolving the problem through methods including function, translator, effect, relation and state. English semantic feature plays an important role in analysis of sentence comprehension. In our study, we discuss the core structure of English semantic respectively and put forward the corresponding of source sentence compared with online machine translation machines .The experimental results can provide technical support for English -Chinese machine translation. Effect chain and judge is called generalized function effect chain, it is not only the basis of element concept classification, but also the foundation of sentence semantic classification.

A Semantic Parsing Model Applied into Search Engine

t is a tough work to select accurate web pages for search engine. This paper applied a semantic parsing model into search engine by comparing the conceptual structures between users input and text. The words, sentences and sentence groups can be mapped into conceptual symbols, semantic categories, and contextual elements based on the theory of Hierarchical Network of Concepts. The approach will improve the intelligence and flexibility of the search engine in future.

Sentence Structure Analysis and English Semantic Feature Processing Based on Hierarchical Network of Concepts

Sentence Structure Analysis and English Semantic Feature Processing Based on Hierarchical Network of Concepts

Semantic MMT Model Based on Hierarchical Network of Concepts in Chinese-English MT

To study the generation of the semantic tree of Chinese sentence in Chinese-English Machine translation (MT), a new semantic-analysis model of Chinese multiple-branched and multiple-labeled tree (MMT) based on the hierarchical network of concepts (HNC) is proposed. Supported by word and rule knowledge-base of HNC, the model executed the semantic analysis using static and dynamic labels as a complex feature of MMT instead of a single feature of phrase structure grammar, and generated a HNC-MMT semantic tree for deep understanding of the semantic of Chinese sentence. Based on the semantic tree generated, the model can realize structure conversion of semantic chunks, and utilize a hybrid strategy of the statistical and rule-based to translate. Experiment shows one of the most important tasks of semantic analysis of Chinese sentence, the global eigen-chunk recognition, achieves accuracy above 85%, verifying the effectiveness. The model has been applied to system development of MT based on HNC.

A Chinese-English patent machine translation system based on the theory of hierarchical network of concepts

Compared with ordinary text, patent text often has more complex sentence structure and more ambiguity of multiple verbs. To deal with these problems, this paper presents a rule-based Chinese-English patent machine translation (MT) system based on the theory of hierarchical network of concepts (HNC). In this system, the whole procedure are divided into three main parts, the semantic analysis of the source language, the transitional transformation from the source language to the target language and the generation of the target language. The knowledge base and the rule set are obtained from manually analyzing the semantic features of a training set which contains more than 6 000 Chinese patent sentences, and a specific method of evaluation is provided during the experiment.