Automatic Construction of Fine-Grained Paraphrase Corpora System Using Language Inference Model

Paraphrase detection and generation are important natural language processing (NLP) tasks. Yet the term paraphrase is broad enough to include many fine-grained relations. This leads to different tolerance levels of semantic divergence in the positive paraphrase class among publicly available paraphrase datasets. Such variation can affect the generalisability of paraphrase classification models. It may also impact the predictability of paraphrase generation models. This paper presents a new model which can use few corpora of fine-grained paraphrase relations to construct automatically using language inference models. The fine-grained sentence level paraphrase relations are defined based on word and phrase level counterparts. We demonstrate that the fine-grained labels from our proposed system can make it possible to generate paraphrases at desirable semantic level. The new labels could also contribute to general sentence embedding techniques.

Knowledge extract and ontology construction method of assembly process text

The actual product assembly process mainly relies on manual assembly by workers, and the personal experience of workers is difficult to effectively reuse. Ontology as a knowledge management and expression tool is gradually applied in the field of assembly. However, the manual construction of the ontology is time-consuming and labor-intensive, and the automatic construction of the ontology requires a large number of corpora for training, both of which are difficult to obtain a good assembly case ontology. This paper proposes a method in which automatically extracts relevant knowledge from case assembly process files to generates case database and integrates ontology framework of assembly domain to construct ontology. It shows that the accuracy can be guaranteed on the basis of the rapid construction of case ontology. The feasibility of this method is proved by a practical case.

Low-Quality Error Detection for Noisy Knowledge Graphs

The automatic construction of knowledge graphs (KGs) from multiple data sources has received increasing attention. The automatic construction process inevitably brings considerable noise, especially in the construction of KGs from unstructured text. The noise in a KG can be divided into two categories: factual noise and low-quality noise. Factual noise refers to plausible triples that meet the requirements of ontology constraints. For example, the plausible triple <New_York, IsCapitalOf, America> satisfies the constraints that the head entity “New_York” is a city and the tail entity “America” belongs to a country. Low-quality noise denotes the obvious errors commonly created in information extraction processes. This study focuses on entity type errors. Most existing approaches concentrate on refining an existing KG, assuming that the type information of most entities or the ontology information in the KG is known in advance. However, such methods may not be suitable at the start of a KG's construction. Therefore, the authors propose an effective framework to eliminate entity type errors. The experimental results demonstrate the effectiveness of the proposed method.

English Sociological Terminology Based On Functional Equivalence Theory

Objectives: In the practice of translating English, there are often situations that may lead to missing words. In this case, a computer technology is needed to improve the translation studies of sociological terms in English. This time, based on the characteristics of Internet network data, intelligent robot information is extracted. Methods: According to the knowledge ontology constructed this time, based on the functional equivalence theory, a method based on the automatic construction of the ontology library in the party building domain is proposed. Results: In order to verify the proposed method algorithm, the example study of some sociological terms conceptual terms above the interactive encyclopedia is studied by the ontology created by encyclopedia resources, such as father/sub-relationship, class and instance relationship and attribute relationship, and a total of 72474 relationships are obtained through the final statistical study. Conclusion: From the overall analysis, it can be seen that the sociological terminology research of English computer network based on functional equivalence theory can achieve a good classification effect.