An Analysis of the Performances of the CasEN Named Entities Recognition System in the Ester2 Evaluation Campaign

8 pagesIn this paper, we present a detailed and critical analysis of the behaviour of the CasEN named entity recognition system during the French Ester2 evaluation campaign. In this project, CasEN has been confronted with the task of detecting and categorizing named entities in manual and automatic transcriptions of radio broadcastings. At first, we give a general presentation of the Ester2 campaign. Then, we describe our system, based on transducers. Next, we depict how systems were evaluated during this campaign and we report the main official results. Afterwards, we investigate in details the influence of some annotation biases which have significantly affected the estimation of the performances of systems. At last, we conduct an in-depth analysis of the effective errors of the CasEN system, providing us with some useful indications about phenomena that gave rise to errors (e.g. metonymy, encapsulation, detection of right boundaries) and are as many challenges for named entity recognition systems

Application of Named Entity Recognition via Twitter on SpaCy in Indonesian (Case Study : Power Failure in the Special Region of Yogyakarta)

SpaCy is a tool that can efficiently handle Natural Language Processing (NLP) problems, one of which is Named Entity Recognition (NER). NER is used to extract and identify named entities in a text. However, so far SpaCy has not officially released the NER model pre-train for Indonesian. On the other hand, based on the 2019 PLN statistical report, the Province of D.I. Yogyakarta is a province that often experiences power failure and many complaints from the public are found on Twitter related to power failure that occur in the province. This is because there is no research on extracting information related to electrical disturbances and research on NER using SpaCy in Indonesian is still rare. So in this study, information extraction related to power failure in the Province of D.I. will be carried out. Yogyakarta via twitter using Indonesian SpaCy. This study produces good performance results with 95.52% precision calculation, 93.27% recall, and 94.38% f1-score. Then, mapping is carried out based on the location entities contained in tweets related to electrical disturbances. From this process, it was found that the highest number of locations mentioned in the tweet related to power failure came from Sleman Regency, while the lowest number came from Gunung Kidul Regency. Then, the month that experienced the most power failure was March 2020, while the month that experienced the least amount of electricity was July 2020

Band gap information extraction from materials science literature – a pilot study

Purpose The purpose of this paper is to present a preliminary work on extracting band gap information of materials from academic papers. With increasing demand for renewable energy, band gap information will help material scientists design and implement novel photovoltaic (PV) cells. Design/methodology/approach The authors collected 1.44 million titles and abstracts of scholarly articles related to materials science, and then filtered the collection to 11,939 articles that potentially contain relevant information about materials and their band gap values. ChemDataExtractor was extended to extract information about PV materials and their band gap information. Evaluation was performed on randomly sampled information records of 415 papers. Findings The findings of this study show that the current system is able to correctly extract information for 51.32% articles, with partially correct extraction for 36.62% articles and incorrect for 12.04%. The authors have also identified the errors belonging to three main categories pertaining to chemical entity identification, band gap information and interdependency resolution. Future work will focus on addressing these errors to improve the performance of the system. Originality/value The authors did not find any literature to date on band gap information extraction from academic text using automated methods. This work is unique and original. Band gap information is of importance to materials scientists in applications such as solar cells, light emitting diodes and laser diodes

Named entity recognition for quranic text using rule based approaches

The variety and difference between domains for textual data require customization in the Natural Language Processing component especially in Named Entity Recognition where different domains contain several types of entities. The current NER model is deemed not fit to accurately extract entities from Quranic text due to its unique content. This paper describes the building of a rule-based Named Entity Recognition method to extract the entities that exist in the English translation to the meaning of the Quranic text and its performance evaluation. Named entity tagging, a common task in-text annotation, in which entities (nouns) in the unstructured text are identified and assigned a class. A few rules are built to extract several types of entities such as the name of prophets and people, creation, location, time, and the various names of God. The rules are built mainly using regular expressions and gazetteers. The rules that have been built result in high precision and recall as well as a satisfactory F-score of over 90%. The results from this experiment can be used as annotation in building a machine learning model to extract entities from the same type of domain specifically on the Quranic text or generally in the Islamic domain text

Satellite Workshop On Language, Artificial Intelligence and Computer Science for Natural Language Processing Applications (LAICS-NLP): Discovery of Meaning from Text

This paper proposes a novel method to disambiguate important words from a collection of documents. The hypothesis that underlies this approach is that there is a minimal set of senses that are significant in characterizing a context. We extend Yarowsky’s one sense per discourse [13] further to a collection of related documents rather than a single document. We perform distributed clustering on a set of features representing each of the top ten categories of documents in the Reuters-21578 dataset. Groups of terms that have a similar term distributional pattern across documents were identified. WordNet-based similarity measurement was then computed for terms within each cluster. An aggregation of the associations in WordNet that was employed to ascertain term similarity within clusters has provided a means of identifying clusters’ root senses