Fine-Grained Named Entity Typing over Distantly Supervised Data Based on Refined Representations

Fine-Grained Named Entity Typing (FG-NET) is a key component in Natural Language Processing (NLP). It aims at classifying an entity mention into a wide range of entity types. Due to a large number of entity types, distant supervision is used to collect training data for this task, which noisily assigns type labels to entity mentions irrespective of the context. In order to alleviate the noisy labels, existing approaches on FGNET analyze the entity mentions entirely independent of each other and assign type labels solely based on mention sentence-specific context. This is inadequate for highly overlapping and noisy type labels as it hinders information passing across sentence boundaries. For this, we propose an edge-weighted attentive graph convolution network that refines the noisy mention representations by attending over corpus-level contextual clues prior to the end classification. Experimental evaluation shows that the proposed model outperforms the existing research by a relative score of upto 10.2% and 8.3% for macro f1 and micro f1 respectively

The Market for Heritage: Evidence from eBay using Natural Language Processing

The trade in antiquities and cultural objects has proven difficult to understand and yet is highly dynamic. Currently, there are few computational tools that allow researchers to understand the nature of the legal market, which can also potentially provide insights into the illegal market such as types of objects traded and countries trading antiquities. Online sales in antiquities and cultural objects are often unstructured data; relevant cultural affiliations, types, and materials for objects are important for distinguishing what might sell, but these data are rarely organized in a format that makes the quantification of sales a simple process. Additionally, sale locations and the total value of sales are relevant to understanding the focus and size of the market. These data all provide potentially useful insights into how the market in antiquities and cultural objects is developing. Based on this need, this work presents the results of a machine learning approach using natural language processing and dictionary-based searches that investigates relatively low-end but high sales volume objects sold on eBay’s US site, where sales are often international, between October 2018 and May 2019. The use of named entity recognition, using a conditional random field approach, classifies objects based on the cultures in which they come from, what type of objects they are, and what the objects are made of. The results indicate that objects from the UK, affiliated with the Roman period, mostly constituting jewelery, and made of metals sell the most. Other important countries for selling ancient and cultural objects include the United States, Thailand, Germany, and Cyprus. Some countries appear to focus on specific types of objects, such as Egypt being a leader in selling Islamic cultural objects. Overall, the approach and tool used demonstrate that it is possible to monitor the online antiquities and cultural objects market while potentially gaining useful insights into the market. The tool developed is provided as part of this work so that it can be applied for other cases and online sites, where it can be applied in real time or using historical data

Named Entity Recognition in Chinese Clinical Text

Objective: Named entity recognition (NER) is one of the fundamental tasks in natural language processing (NLP). In the medical domain, there have been a number of studies on NER in English clinical notes; however, very limited NER research has been done on clinical notes written in Chinese. The goal of this study is to develop corpora, methods, and systems for NER in Chinese clinical text. Materials and methods: To study entities in Chinese clinical text, we started with building annotated clinical corpora in Chinese. We developed an NER annotation guideline in Chinese by extending the one used in the 2010 i2b2 NLP challenge. We randomly selected 400 admission notes and 400 discharge summaries from Peking Union Medical College Hospital (PUMCH) in China. For each note, four types of entities including clinical problems, procedures, labs, and medications were annotated according to the developed guideline. In addition, an annotation tool was developed to assist two MD students to annotate Chinese clinical documents. A comparison of entity distribution between Chinese and English clinical notes (646 English and 400 Chinese discharge summaries) was performed using the annotated corpora, to identify the important features for NER. In the NER study, two-thirds of the 400 notes were used for training the NER systems and one-third were used for testing. We investigated the effects of different types of features including bag-of-characters, word segmentation, part-of-speech, and section information, with different machine learning (ML) algorithms including Conditional Random Fields (CRF), Support Vector Machines (SVM), Maximum Entropy (ME), and Structural Support Vector Machines (SSVM) on the Chinese clinical NER task. All classifiers were trained on the training dataset, evaluated on the test set, and microaveraged precision, recall, and F-measure were reported. Results: Our evaluation on the independent test set showed that most types of features were beneficial to Chinese NER systems, although the improvements were limited. By combining word segmentation and section information, the system achieved the highest performance, indicating that these two types of features are complementary to each other. When the same types of optimized features were used, CRF and SSVM outperformed SVM and ME. More specifically, SSVM reached the highest performance among the four algorithms, with F-measures of 93.51% and 90.01% for admission notes and discharge summaries respectively. Conclusions: In this study, we created large annotated datasets of Chinese admission notes and discharge summaries and then systematically evaluated different types of features (e.g., syntactic, semantic, and segmentation information) and four ML algorithms including CRF, SVM, SSVM, and ME for clinical NER in Chinese. To the best of our knowledge, this is one of the earliest comprehensive effort in Chinese clinical NER research and we believe it will provide valuable insights to NLP research in Chinese clinical text. Our results suggest that both word segmentation and section information improves NER in Chinese clinical text, and SSVM, a recent sequential labelling algorithm, outperformed CRF and other classification algorithms. Our best system achieved F-measures of 90.01% and 93.52% on Chinese discharge summaries and admission notes, respectively, indicating a promising start on Chinese NLP research

A Named Entity Recognition System Applied to Arabic Text in the Medical Domain

Currently, 30-35% of the global population uses the Internet. Furthermore, there is a rapidly increasing number of non-English language internet users, accompanied by an also increasing amount of unstructured text online. One area replete with underexploited online text is the Arabic medical domain, and one method that can be used to extract valuable data from Arabic medical texts is Named Entity Recognition (NER). NER is the process by which a system can automatically detect and categorise Named Entities (NE). NER has numerous applications in many domains, and medical texts are no exception. NER applied to the medical domain could assist in detection of patterns in medical records, allowing doctors to make better diagnoses and treatment decisions, enabling medical staff to quickly assess a patient's records and ensuring that patients are informed about their data, as just a few examples. However, all these applications would require a very high level of accuracy. To improve the accuracy of NER in this domain, new approaches need to be developed that are tailored to the types of named entities to be extracted and categorised. In an effort to solve this problem, this research applied Bayesian Belief Networks (BBN) to the process. BBN, a probabilistic model for prediction of random variables and their dependencies, can be used to detect and predict entities. The aim of this research is to apply BBN to the NER task to extract relevant medical entities such as disease names, symptoms, treatment methods, and diagnosis methods from modern Arabic texts in the medical domain. To achieve this aim, a new corpus related to the medical domain has been built and annotated. Our BBN approach achieved a 96.60% precision, 90.79% recall, and 93.60% F-measure for the disease entity, while for the treatment method entity, it achieved 69.33%, 70.99%, and 70.15% for precision, recall, and F-measure, respectively. For the diagnosis method and symptom categories, our system achieved 84.91% and 71.34%, respectively, for precision, 53.36% and 49.34%, respectively, for recall, and 65.53% and 58.33%, for F-measure, respectively. Our BBN strategy achieved good accuracy for NEs in the categories of disease and treatment method. However, the average word length of the other two NE categories observed, diagnosis method and symptom, may have had a negative effect on their accuracy. Overall, the application of BBN to Arabic medical NER is successful, but more development is needed to improve accuracy to a standard at which the results can be applied to real medical systems