Chinese Organization Name Research Articles

Research on Organization Name Matching Based on Word Vector

Organization names usually exist in the form of abbreviations, aliases, etc. in daily use, which brings great difficulties to online search and matching in the Internet era. Through extensive analysis of Chinese organization name data and research on word embedding technology based on neural network, this paper proposes a BERT model based on organization name field data training to achieve organization name matching. The experimental results show that the accuracy is improved by 17% compared with the method based on edit distance.

Recognizing Chinese Organization Names Based on Deep Learning: A Recurrent Network Model

Recognizing Chinese Organization Names Based on Deep Learning: A Recurrent Network Model

Improving Translation of Organization Names Combining Translation Model and Web Mining

Named entity (NE) translation is a fundamental task in machine translation (MT) and cross-language information retrieval (CLIR). Furthermore, Organization name (ON) translation is the most complex among all the NEs. A novel system for translating ONs from Chinese to English, with a translation model and web resources, is proposed. Firstly, we built a translation model with Chunk. Then query expansion was adopted with the translation model and term-subject co-occurrence. Thirdly, we extracted the Chinese Organization names with English sentences using the method of frequency shifting and adjacency information to find English fragments as translation candidates. Finally, we found the best translation by computing the trustworthiness of all candidates. The experimental results showed that the approach returned a better performance than machine translation-based systems.

Chinese Organization Name Recognition in User Query Log

Chinese Organization Name Recognition in User Query Log

A Practical Chinese-English Organization Name Translation Method Based on Web Assistant

In those traditional organization name translation methods, researchers usually assumed that for every organization name to be translated, its correct translation would exist somewhere on the web. And some researchers further assumed that both the organization names to be translated and their correct translations would exist somewhere on some mix-language web pages. Thus these researchers think it is appropriate to translate organization names with some web mining based methods. However, the correctness of these assumptions has never been verified. In this paper, we focus on this issue and experimentally verify the correctness of these two assumptions. And from our experimental results, we find out several useful distribution characteristics of the organization names that appears on the web. Based on these distribution characteristics, a practical Chinese-English organization name translation method is proposed. Experimental results show that our method is effective. It can improve the inclusion rate of correct translations for those Chinese organization names whose correct translations really exist on the web, and it can also improve the BLEU score and accuracy for those Chinese organization names whose correct translations rarely occur on the web.

Closing the Loop in Webpage Understanding

The two most important tasks in information extraction from the Web are webpage structure understanding and natural language sentences processing. However, little work has been done toward an integrated statistical model for understanding webpage structures and processing natural language sentences within the HTML elements. Our recent work on webpage understanding introduces a joint model of Hierarchical Conditional Random Fields (HCRFs) and extended Semi-Markov Conditional Random Fields (Semi-CRFs) to leverage the page structure understanding results in free text segmentation and labeling. In this top-down integration model, the decision of the HCRF model could guide the decision making of the Semi-CRF model. However, the drawback of the top-down integration strategy is also apparent, i.e., the decision of the Semi-CRF model could not be used by the HCRF model to guide its decision making. This paper proposed a novel framework called WebNLP, which enables bidirectional integration of page structure understanding and text understanding in an iterative manner. We have applied the proposed framework to local business entity extraction and Chinese person and organization name extraction. Experiments show that the WebNLP framework achieved significantly better performance than existing methods.

Web based translation of Chinese organization name

A web-based translation method for Chinese organization name is proposed. After analyzing the structure of Chinese organization name, the methods of bilingual query formulation and maximum entropy based translation re-ranking are suggested to retrieve the English translation from the web via public search engine. The experiments on Chinese university names demonstrate the validness of this approach.

A Structure-Based Model for Chinese Organization Name Translation

Named entity (NE) translation is a fundamental task in multilingual natural language processing. The performance of a machine translation system depends heavily on precise translation of the inclusive NEs. Furthermore, organization name (ON) is the most complex NE for translation among all the NEs. In this article, the structure formulation of ONs is investigated and a hierarchical structure-based ON translation model for Chinese-to-English translation system is presented. First, the model performs ON chunking; then both the translation of words within chunks and the process of chunk-reordering are achieved by synchronous context-free grammar (CFG). The CFG rules are extracted from bilingual ON pairs in a training program. The main contributions of this article are: (1) defining appropriate chunk-units for analyzing the internal structure of Chinese ONs; (2) making the chunk-based ON translation feasible and flexible via a hierarchical CFG derivation; and (3) proposing a training architecture to automatically learn the synchronous CFG for constructing ONs with chunk-units from aligned bilingual ON pairs. The experiments show that the proposed approach translates the Chinese ONs into English with an accuracy of 93.75% and significantly improves the performance of a baseline statistical machine translation (SMT) system.