Informed Named Entity Recognition Decoding for Generative Language Models

Ever-larger language models with ever-increasing capabilities are by now well-established text processing tools. Alas, information extraction tasks such as named entity recognition are still largely unaffected by this progress as they are primarily based on the previous generation of encoder-only transformer models. Here, we propose a simple yet effective approach, Informed Named Entity Recognition Decoding (iNERD), which treats named entity recognition as a generative process. It leverages the language understanding capabilities of recent generative models in a future-proof manner and employs an informed decoding scheme incorporating the restricted nature of information extraction into open-ended text generation, improving performance and eliminating any risk of hallucinations. We coarse-tune our model on a merged named entity corpus to strengthen its performance, evaluate five generative language models on eight named entity recognition datasets, and achieve remarkable results, especially in an environment with an unknown entity class set, demonstrating the adaptability of the approach.Comment: 12 pages, 2 figures, 4 table

An Enhanced Malay Named Entity Recognition Using Clustering and Classification Approach For Crime Textual Data Analysis

Named Entity Recognition (NER) is one of the tasks undertaken in the information extraction. NER is used for extracting and classifying words or entities that belong to the proper noun category in text data such as the person's name, location, organization, date, etc. As seen in today's generation, social media such as web pages, blogs, Facebook, Twitter, Instagram and online newspapers are among the major contributors to information extraction. These resources contain various types of unstructured data such as text. However, the amount of works done to process this type of data is limited for Malay Named Entity Recognition (MNER). The deficiency on Malay textual analytic has led to difficulties in extracting information for decision making. This research aims to present a Malay Named Entity Recognition technique that focuses on crime data analysis in the Malay language that extracted from Polis Diraja Malaysia (PDRM) news web page. This Malay Named Entity Recognition (MNER) technique is proposed by using multi-staged of clustering and classification methods. The methods are Fuzzy C-Means and K-Nearest Neighbors Algorithm. The methods involve multi-layer features extraction to recognize entities such as person name, location, organization, date and crime type. This multi-staged technique is obtained 95.24% accuracy in the process of recognizing named entities for text analysis, particularly in Malay. The proposed technique can improve the accuracy performance on named entity recognition of crime data based on the suitability selected features for the Malay language

Bayesian Information Extraction Network

Dynamic Bayesian networks (DBNs) offer an elegant way to integrate various aspects of language in one model. Many existing algorithms developed for learning and inference in DBNs are applicable to probabilistic language modeling. To demonstrate the potential of DBNs for natural language processing, we employ a DBN in an information extraction task. We show how to assemble wealth of emerging linguistic instruments for shallow parsing, syntactic and semantic tagging, morphological decomposition, named entity recognition etc. in order to incrementally build a robust information extraction system. Our method outperforms previously published results on an established benchmark domain.Comment: 6 page

Exploring Named Entity Recognition and Relation Extraction for ontology and medical records integration

The available natural language data in electronic health records is of noteworthy interest to health research and development. Nevertheless, their manual analysis is not feasible and poses a challenge to accessing valuable information in these records. This paper presents an approach to automatically extract information from these unstructured medical records using Domain Entity Recognition and Relation Extraction, structuring the results through a domain ontology. We developed our work in the oncology domain, an attention-demanding field. The main contribution of this work lies in integrating multiple resources in a complete methodology to accomplish this task. We developed a new entity and relation annotated dataset of medical evolutions in Brazilian Portuguese, containing 1622 documents, 146,769 entities, and 111,716 relations. We attained 78.24 % accuracy for entity and relation extraction in the exams domain. Healthcare specialists evaluated the approach regarding entity recognition and relation extraction positively and considered the methodology valuable to health professionals

An integrated architecture for shallow and deep processing

We present an architecture for the integration of shallow and deep NLP components which is aimed at flexible combination of different language technologies for a range of practical current and future applications. In particular, we describe the integration of a high-level HPSG parsing system with different high-performance shallow components, ranging from named entity recognition to chunk parsing and shallow clause recognition. The NLP components enrich a representation of natural language text with layers of new XML meta-information using a single shared data structure, called the text chart. We describe details of the integration methods, and show how information extraction and language checking applications for realworld German text benefit from a deep grammatical analysis

BIBLIOMETRIC ANALYSIS OF NAMED ENTITY RECOGNITION FOR CHEMOINFORMATICS AND BIOMEDICAL INFORMATION EXTRACTION OF OVARIAN CANCER

With the massive amount of data that has been generated in the form of unstructured text documents, Biomedical Named Entity Recognition (BioNER) is becoming increasingly important in the field of biomedical research. Since currently there does not exist any automatic archiving of the obtained results, a lot of this information remains hidden in the textual details and is not easily accessible for further analysis. Hence, text mining methods and natural language processing techniques are used for the extraction of information from such publications.Named entity recognition, is a subtask that comes under information extraction that focuses on finding and categorizing specific entities in text. In this paper, bibliometric analysis of named entity recognition of ovarian cancer is carried out using information about publications from Scopus. The most productive journals, countries and authors are determined. The most frequently cited article and its citation history has been described. Also bibliometric maps based on citation network among countries are constructed. This study can assist people in the medical field to get a comprehensive understanding of the study of BioNER. It can also be utilized for reference works, for the research and application of the BioNER visualization methods