Entity Translation Research Articles

Impact of Translation on Biomedical Information Extraction: Experiment on Real-Life Clinical Notes.

Biomedical natural language processing tasks are best performed with English models, and translation tools have undergone major improvements. On the other hand, building annotated biomedical data sets remains a challenge. The aim of our study is to determine whether the use of English tools to extract and normalize French medical concepts based on translations provides comparable performance to that of French models trained on a set of annotated French clinical notes. We compared 2 methods: 1 involving French-language models and 1 involving English-language models. For the native French method, the named entity recognition and normalization steps were performed separately. For the translated English method, after the first translation step, we compared a 2-step method and a terminology-oriented method that performs extraction and normalization at the same time. We used French, English, and bilingual annotated data sets to evaluate all stages (named entity recognition, normalization, and translation) of our algorithms. The native French method outperformed the translated English method, with an overall F1-score of 0.51 (95% CI 0.47-0.55), compared with 0.39 (95% CI 0.34-0.44) and 0.38 (95% CI 0.36-0.40) for the 2 English methods tested. Despite recent improvements in translation models, there is a significant difference in performance between the 2 approaches in favor of the native French method, which is more effective on French medical texts, even with few annotated documents.

The Translation and Validation of Patient-Centered Outcomes Questionnaire Into Arabic: A Chronic Periodontitis Patient-Based Study.

Background The patient-centered outcomes questionnaire (PCOQ) is a self-report questionnaire that aims to assess four fundamental domains (pain, fatigue, distress, and interference) on an 11-point numerical rating scale from 0 to 10 in chronic diseases. The implementation of this tool will help assess chronic diseases; hence, this study aimed to translate the PCOQ to a sample of periodontitis patients. Methodology This study went through the content validity index. Arabic PCOQ used Cronbach's alpha for reliability with 300 participants. From July to August 2023, patients with periodontitis visiting an outpatient dental center in Riyadhwere invited to participate in the study. The language, content, and structure of the questionnaire were appropriate, and with forward and backward translation, external entity translation was implemented. Results Regarding the participants' gender, the number of males was higher at 61%. Regarding age distribution, 50.7% of the participants were between 30 and 50 years old. A clarity score of 95.2% and a representativeness score of 97.3% were reported in the content validity analysis. The Cronbach's alpha of the Arabic PCOQ questionnaire was 0.85, and the subscales ranged between 0.68 and 0.93. Conclusions The translated version of the Arabic PCOQ is a valid tool to be used in Arab countries. Nonetheless, this instrument can provide insights for healthcare professionals, policymakers, and managers to improve patient satisfaction and healthcare system delivery.

Named entity translation method based on machine translation lexicon

In the context of the rapid development of computer technology, communication between various languages has become increasingly important. Among the research methods of named entities, the research on named entity translation methods based on machine translation is one of the research hotspots. Named entity translation is to realize the switching between entities in two languages, which can be used by browsers, translators, etc., and can greatly reduce the cost of communication between people from all over the world. Due to the immaturity of the existing translation model technology and the lack of integration, the translation of bilingual named entities with unique composition is very challenging. Based on this, this paper proposes a fusion method of bilingual entity class named entity translation based on chunk symmetry strategy and English–Chinese transliteration model based on machine learning strategy. According to the bilingual corpus, a more standard candidate entity translation pair is generated through bilingual named entity alignment. The transliteration model is used to reorder and correct the candidate translation results, so as to achieve the correct selection of translation pairs. Experiments show that the model based on the translation of named entity translations based on chunks and transliteration models based on machine learning strategies not only effectively solves the problem of difficulty in word ordering and selection in bilingual translation, but also makes extraction to a certain extent. The accuracy of the translation results has been improved.

Constructing Uyghur Commonsense Knowledge Base by Knowledge Projection

Although considerable effort has been devoted to building commonsense knowledge bases (CKB), it is still not available for many low-resource languages such as Uyghur because of expensive construction cost. Focusing on this issue, we proposed a cross-lingual knowledge-projection method to construct an Uyghur CKB by projecting ConceptNet’s Chinese facts into Uyghur. We used a Chinese–Uyghur bilingual dictionary to get high-quality entity translation in facts and employed a back-translation method to eliminate the entity-translation ambiguity. Moreover, to tackle the inner relation ambiguity in translated facts, we made a hand-crafted rule to convert the structured facts into natural-language phrases and built the Chinese–Uyghur lingual phrases based on the similarity of phrases that corresponded to the bilingual semantic similarity scoring model. Experimental results show that the accuracy of our semantic similarity scoring model reached 94.75% for our task, and they successfully project 55,872 Chinese facts into Uyghur as well as obtain 67,375 Uyghur facts within a very short period.

Assessment of Stability and Discrimination Capacity of Radiomic Features on Apparent Diffusion Coefficient Images

The objectives of the study are to develop a new way to assess stability and discrimination capacity of radiomic features without the need of test-retest or multiple delineations and to use information obtained to perform a preliminary feature selection. Apparent diffusion coefficient (ADC) maps were computed from diffusion-weighted magnetic resonance images (DW-MRI) of two groups of patients: 18 with soft tissue sarcomas (STS) and 18 with oropharyngeal cancers (OPC). Sixty-nine radiomic features were computed, using three different histogram discretizations (16, 32, and 64 bins). Geometrical transformations (translations) of increasing entity were applied to the regions of interest (ROIs), and the intra-class correlation coefficient (ICC) was used to compare the features computed on the original and modified ROIs. The distribution of ICC values for minimal and maximal entity translations (ICC10 and ICC100, respectively) was used to adjust thresholds of ICC (ICCmin and ICCmax) used to discriminate between good, unstable (ICC10 < ICCmin), and non-discriminative features (ICC100 > ICCmax). Fifty-four and 59 radiomic features passed the stability-based selection for all the three histogram discretizations for the OPC and STS datasets, respectively. The excluded features were similar across the different histogram discretizations (Jaccard’s index 0.77 ± 0.13 and 0.9 ± 0.1 for OPC and STS, respectively) but different between datasets (Jaccard’s index 0.19 ± 0.02). The results suggest that the observed radiomic features are mainly stable and discriminative, but the stability depends on the region of the body under observation. The method provides a way to assess stability without the need of test-retest or multiple delineations.

Stability assessment of first order statistics features computed on ADC maps in soft-tissue sarcoma.

Radiomics extracts a large number of features from medical images to perform a quantitative characterization. Aim of this study was to assess radiomic features stability and relevance. Apparent diffusion coefficient (ADC) maps were computed from diffusion-weighted magnetic resonance images (DW-MRI) of 18 patients diagnosed with soft-tissue sarcomas (STSs). Thirty-seven intensity-based features were computed on the regions of interest (ROIs). First, ROIs of the images were subjected to translations and rotations in specific ranges. The 37 features computed on the original and transformed ROIs were compared in terms of percentage of variations. The intra-class correlation coefficient (ICC) was computed. To be accepted, a feature should satisfy the following conditions: the ICC after a minimum entity transformation is > 0.6 and the ICC after a maximum entity translation is <; 0.4. In total, 31 features out of 37 were accepted by the algorithm. This stability analysis can be used as a first step in the features selection process.

Machine Translation: Phrase-Based, Rule-Based and Neural Approaches with Linguistic Evaluation

AbstractIn this article we present a novel linguistically driven evaluation method and apply it to the main approaches of Machine Translation (Rule-based, Phrase-based, Neural) to gain insights into their strengths and weaknesses in much more detail than provided by current evaluation schemes. Translating between two languages requires substantial modelling of knowledge about the two languages, about translation, and about the world. Using English-German IT-domain translation as a case-study, we also enhance the Phrase-based system by exploiting parallel treebanks for syntax-aware phrase extraction and by interfacing with Linked Open Data (LOD) for extracting named entity translations in a post decoding framework.

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.

Overcoming Asymmetry in Entity Graphs

This paper studies the problem of mining named entity translations by aligning comparable corpora. Current state-of-the-art approaches mine a translation pair by aligning an entity graph in one language to another based on node similarity or propagated similarity of related entities. However, they, building on the assumption of “symmetry”, quickly deteriorate on “weakly” comparable corpora with some asymmetry. In this paper, we pursue two directions for overcoming relation and entity asymmetry respectively. The first approach starts from weakly comparable corpora (for high recall) then ensures precision by selective propagation only to entities of symmetric relations. The second approach starts from parallel corpora (for high precision) then enhances recall by extending the translation matrix based on node similarity and contextual similarity. Our experimental results on English-Chinese corpora show that both approaches are effective and complementary. Our combined approach outperforms the best-performing baseline in terms of F1-score by up to 0.28.

Improving the Quality of English-Hindi Name Entity Translation

Machine Translation is a novel step in overcoming the language barrier. The results of the research in this area has started to show its results with some good machine translators being available. In the context of English-Hindi language pair, due to bad name entity translations, the quality of translation deteriorates. In this paper we have addressed this issue where we show that only a single approach is not effective in dealing with this issue. We need to devise mechanism and address this problem in a multi pronged approach. General Terms Natural Language Processing, Machine Translation

Improving the Quality of Machine Translation through Proper Transliteration of Name Entities

Machine Translation is the study of system that translates the given input text in source language to the output text in the target language. The source language and the target language are Natural Languages. Machine Translation is a very difficult problem; especially name entity translation has always been a challenge for the machine translators because of different spelling variations in translation of name entities. In this paper we focus on improving the quality of our machine translated output. For this, at first we recognize the name entities and then transliterate them. We have calculated the phoneme based N-Gram Probabilities for all the name entities. Using these probabilities we are transliterating our name entities from English to Punjabi through syllabification in which we divide the word in to syllables.

Entity Translation Mining from Comparable Corpora: Combining Graph Mapping with Corpus Latent Features

This paper addresses the problem of mining named entity translations from comparable corpora, specifically, mining English and Chinese named entity translation. We first observe that existing approaches use one or more of the following named entity similarity metrics: entity, entity context, and relationship. Motivated by this observation, we propose a new holistic approach by 1) combining all similarity types used and 2) additionally considering relationship context similarity between pairs of named entities, a missing quadrant in the taxonomy of similarity metrics. We abstract the named entity translation problem as the matching of two named entity graphs extracted from the comparable corpora. Specifically, named entity graphs are first constructed from comparable corpora to extract relationship between named entities. Entity similarity and entity context similarity are then calculated from every pair of bilingual named entities. A reinforcing method is utilized to reflect relationship similarity and relationship context similarity between named entities. We also discover features lost in the graph extraction process and integrate this into our framework. According to our experimental results, our holistic graph-based approach and its enhancement using corpus latent features are highly effective and our framework significantly outperforms previous approaches.

Efficient Entity Translation Mining

This article studies the problem of mining entity translation, specifically, mining English and Chinese name pairs. Existing efforts can be categorized into (a) transliteration-based approaches that leverage phonetic similarity and (b) corpus-based approaches that exploit bilingual cooccurrences. These approaches suffer from inaccuracy and scarcity, respectively. In clear contrast, we use under-leveraged resources of monolingual entity cooccurrences crawled from entity search engines, which are represented as two entity-relationship graphs extracted from two language corpora, respectively. Our problem is then abstracted as finding correct mappings across two graphs. To achieve this goal, we propose a holistic approach to exploiting both transliteration similarity and monolingual cooccurrences. This approach, which builds upon monolingual corpora, complements existing corpus-based work requiring scarce resources of parallel or comparable corpus while significantly boosting the accuracy of transliteration-based work. In addition, by parallelizing the mapping process on multicore architectures, we speed up the computation by more than 10 times per unit accuracy. We validated the effectiveness and efficiency of our proposed approach using real-life datasets.

Web-based pattern learning for named entity translation in Korean–Chinese cross-language information retrieval

Named entity (NE) translation plays an important role in many applications, such as information retrieval and machine translation. In this paper, we focus on translating NEs from Korean to Chinese in order to improve Korean–Chinese cross-language information retrieval (KCIR). The ideographic nature of Chinese makes NE translation difficult because one syllable may map to several Chinese characters. We propose a hybrid NE translation system. First, we integrate two online databases to extend the coverage of our bilingual dictionaries. We use Wikipedia as a translation tool based on the inter-language links between the Korean edition and the Chinese or English editions. We also use Naver.com’s people search engine to find a query name’s Chinese or English translation. The second component of our system is able to learn Korean–Chinese (K–C), Korean–English (K–E), and English–Chinese (E–C) translation patterns from the web. These patterns can be used to extract K–C, K–E and E–C pairs from Google snippets. We found KCIR performance using this hybrid configuration over five times better than that a dictionary-based configuration using only Naver people search. Mean average precision was as high as 0.3385 and recall reached 0.7578. Our method can handle Chinese, Japanese, Korean, and non-CJK NE translation and improve performance of KCIR substantially.

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.

Named entity translation matching and learning

This article introduces a named entity matching model that makes use of both semantic and phonetic evidence. The matching of semantic and phonetic information is captured by a unified framework via a bipartite graph model. By considering various technical challenges of the problem, including order insensitivity and partial matching, this approach is less rigid than existing approaches and highly robust. One major component is a phonetic matching model which exploits similarity at the phoneme level. Two learning algorithms for learning the similarity information of basic phonemic matching units based on training examples are investigated. By applying the proposed named entity matching model, a mining system is developed for discovering new named entity translations from daily Web news. The system is able to discover new name translations that cannot be found in the existing bilingual dictionary.