PersoNER: Persian named-entity recognition

© 1963-2018 ACL. Named-Entity Recognition (NER) is still a challenging task for languages with low digital resources. The main difficulties arise from the scarcity of annotated corpora and the consequent problematic training of an effective NER pipeline. To abridge this gap, in this paper we target the Persian language that is spoken by a population of over a hundred million people world-wide. We first present and provide ArmanPerosNERCorpus, the first manually-annotated Persian NER corpus. Then, we introduce PersoNER, an NER pipeline for Persian that leverages a word embedding and a sequential max-margin classifier. The experimental results show that the proposed approach is capable of achieving interesting MUC7 and CoNNL scores while outperforming two alternatives based on a CRF and a recurrent neural network

GujiBERT and GujiGPT: Construction of Intelligent Information Processing Foundation Language Models for Ancient Texts

In the context of the rapid development of large language models, we have meticulously trained and introduced the GujiBERT and GujiGPT language models, which are foundational models specifically designed for intelligent information processing of ancient texts. These models have been trained on an extensive dataset that encompasses both simplified and traditional Chinese characters, allowing them to effectively handle various natural language processing tasks related to ancient books, including but not limited to automatic sentence segmentation, punctuation, word segmentation, part-of-speech tagging, entity recognition, and automatic translation. Notably, these models have exhibited exceptional performance across a range of validation tasks using publicly available datasets. Our research findings highlight the efficacy of employing self-supervised methods to further train the models using classical text corpora, thus enhancing their capability to tackle downstream tasks. Moreover, it is worth emphasizing that the choice of font, the scale of the corpus, and the initial model selection all exert significant influence over the ultimate experimental outcomes. To cater to the diverse text processing preferences of researchers in digital humanities and linguistics, we have developed three distinct categories comprising a total of nine model variations. We believe that by sharing these foundational language models specialized in the domain of ancient texts, we can facilitate the intelligent processing and scholarly exploration of ancient literary works and, consequently, contribute to the global dissemination of China's rich and esteemed traditional culture in this new era.Comment: 22pages,0 figur

On the Use of Parsing for Named Entity Recognition

[Abstract] Parsing is a core natural language processing technique that can be used to obtain the structure underlying sentences in human languages. Named entity recognition (NER) is the task of identifying the entities that appear in a text. NER is a challenging natural language processing task that is essential to extract knowledge from texts in multiple domains, ranging from financial to medical. It is intuitive that the structure of a text can be helpful to determine whether or not a certain portion of it is an entity and if so, to establish its concrete limits. However, parsing has been a relatively little-used technique in NER systems, since most of them have chosen to consider shallow approaches to deal with text. In this work, we study the characteristics of NER, a task that is far from being solved despite its long history; we analyze the latest advances in parsing that make its use advisable in NER settings; we review the different approaches to NER that make use of syntactic information; and we propose a new way of using parsing in NER based on casting parsing itself as a sequence labeling task.Xunta de Galicia; ED431C 2020/11Xunta de Galicia; ED431G 2019/01This work has been funded by MINECO, AEI and FEDER of UE through the ANSWER-ASAP project (TIN2017-85160-C2-1-R); and by Xunta de Galicia through a Competitive Reference Group grant (ED431C 2020/11). CITIC, as Research Center of the Galician University System, is funded by the Consellería de Educación, Universidade e Formación Profesional of the Xunta de Galicia through the European Regional Development Fund (ERDF/FEDER) with 80%, the Galicia ERDF 2014-20 Operational Programme, and the remaining 20% from the Secretaría Xeral de Universidades (Ref. ED431G 2019/01). Carlos Gómez-Rodríguez has also received funding from the European Research Council (ERC), under the European Union’s Horizon 2020 research and innovation programme (FASTPARSE, Grant No. 714150)

Compilation of Malay criminological terms from online news

A Malay language corpus has been established by the Institute of Language and Literature (Dewan Bahasa dan Pustaka, DBP in Malaysia). Most of the past research on the Malay language corpus has focused on the description, lexicography and translation of the Malay language. However, in the existing literature, there is no list of Malay words that categorizes crime terminologies. This study aims to fill that linguistic gap. First, we aggregated the most frequently used crime terminology words from Malaysian online news sources. Five hundred crime-related words were compiled. No automatic machines were in the initial process, but they were subsequently used to verify the data. Four human coders were used to validate the data and ensure the originality of the semantic understanding of the Malay text. Finally, major crime terminologies were outlined from a set of keywords to serve as taggers in our solution. The ultimate goal of this study is to provide a corpus for forensic linguistics, police investigations, and general crime research. This study has established the first corpus of a criminological text in the Malay language

A Survey on Arabic Named Entity Recognition: Past, Recent Advances, and Future Trends

As more and more Arabic texts emerged on the Internet, extracting important information from these Arabic texts is especially useful. As a fundamental technology, Named entity recognition (NER) serves as the core component in information extraction technology, while also playing a critical role in many other Natural Language Processing (NLP) systems, such as question answering and knowledge graph building. In this paper, we provide a comprehensive review of the development of Arabic NER, especially the recent advances in deep learning and pre-trained language model. Specifically, we first introduce the background of Arabic NER, including the characteristics of Arabic and existing resources for Arabic NER. Then, we systematically review the development of Arabic NER methods. Traditional Arabic NER systems focus on feature engineering and designing domain-specific rules. In recent years, deep learning methods achieve significant progress by representing texts via continuous vector representations. With the growth of pre-trained language model, Arabic NER yields better performance. Finally, we conclude the method gap between Arabic NER and NER methods from other languages, which helps outline future directions for Arabic NER.Comment: Accepted by IEEE TKD

A Named Entity Recognition Method Enhanced with Lexicon Information and Text Local Feature

At present, Named Entity Recognition (NER) is one of the fundamental tasks for extracting knowledge from traditional Chinese medicine (TCM) texts. The variability of the length of TCM entities and the characteristics of the language of TCM texts lead to ambiguity of TCM entity boundaries. In addition, better extracting and exploiting local features of text can improve the accuracy of named entity recognition. In this paper, we proposed a TCM NER model with lexicon information and text local feature enhancement of text. In this model, a lexicon is introduced to encode the characters in the text to obtain the context-sensitive global semantic representation of the text. The convolutional neural network (CNN) and gate joined collaborative attention network are used to form a text local feature extraction module to capture the important semantic features of local text. Experiments were conducted on two TCM domain datasets and the F1 values are 91.13% and 90.21% respectively

FindVehicle and VehicleFinder: A NER dataset for natural language-based vehicle retrieval and a keyword-based cross-modal vehicle retrieval system

Natural language (NL) based vehicle retrieval is a task aiming to retrieve a vehicle that is most consistent with a given NL query from among all candidate vehicles. Because NL query can be easily obtained, such a task has a promising prospect in building an interactive intelligent traffic system (ITS). Current solutions mainly focus on extracting both text and image features and mapping them to the same latent space to compare the similarity. However, existing methods usually use dependency analysis or semantic role-labelling techniques to find keywords related to vehicle attributes. These techniques may require a lot of pre-processing and post-processing work, and also suffer from extracting the wrong keyword when the NL query is complex. To tackle these problems and simplify, we borrow the idea from named entity recognition (NER) and construct FindVehicle, a NER dataset in the traffic domain. It has 42.3k labelled NL descriptions of vehicle tracks, containing information such as the location, orientation, type and colour of the vehicle. FindVehicle also adopts both overlapping entities and fine-grained entities to meet further requirements. To verify its effectiveness, we propose a baseline NL-based vehicle retrieval model called VehicleFinder. Our experiment shows that by using text encoders pre-trained by FindVehicle, VehicleFinder achieves 87.7\% precision and 89.4\% recall when retrieving a target vehicle by text command on our homemade dataset based on UA-DETRAC. The time cost of VehicleFinder is 279.35 ms on one ARM v8.2 CPU and 93.72 ms on one RTX A4000 GPU, which is much faster than the Transformer-based system. The dataset is open-source via the link https://github.com/GuanRunwei/FindVehicle, and the implementation can be found via the link https://github.com/GuanRunwei/VehicleFinder-CTIM