End-to-End Entity Detection with Proposer and Regressor

Named entity recognition is a traditional task in natural language processing. In particular, nested entity recognition receives extensive attention for the widespread existence of the nesting scenario. The latest research migrates the well-established paradigm of set prediction in object detection to cope with entity nesting. However, the manual creation of query vectors, which fail to adapt to the rich semantic information in the context, limits these approaches. An end-to-end entity detection approach with proposer and regressor is presented in this paper to tackle the issues. First, the proposer utilizes the feature pyramid network to generate high-quality entity proposals. Then, the regressor refines the proposals for generating the final prediction. The model adopts encoder-only architecture and thus obtains the advantages of the richness of query semantics, high precision of entity localization, and easiness of model training. Moreover, we introduce the novel spatially modulated attention and progressive refinement for further improvement. Extensive experiments demonstrate that our model achieves advanced performance in flat and nested NER, achieving a new state-of-the-art F1 score of 80.74 on the GENIA dataset and 72.38 on the WeiboNER dataset

Automatic rule learning exploiting morphological features for named entity recognition in Turkish

Named entity recognition (NER) is one of the basic tasks in automatic extraction of information from natural language texts. In this paper, we describe an automatic rule learning method that exploits different features of the input text to identify the named entities located in the natural language texts. Moreover, we explore the use of morphological features for extracting named entities from Turkish texts. We believe that the developed system can also be used for other agglutinative languages. The paper also provides a comprehensive overview of the field by reviewing the NER research literature. We conducted our experiments on the TurkIE dataset, a corpus of articles collected from different Turkish newspapers. Our method achieved an average F-score of 91.08% on the dataset. The results of the comparative experiments demonstrate that the developed technique is successfully applicable to the task of automatic NER and exploiting morphological features can significantly improve the NER from Turkish, an agglutinative language. © The Author(s) 2011

The Hmong Medical Corpus: a biomedical corpus for a minority language

Biomedical communication is an area that increasingly benefits from natural language processing (NLP) work. Biomedical named entity recognition (NER) in particular provides a foundation for advanced NLP applications, such as automated medical question-answering and translation services. However, while a large body of biomedical documents are available in an array of languages, most work in biomedical NER remains in English, with the remainder in official national or regional languages. Minority languages so far remain an underexplored area. The Hmong language, a minority language with sizable populations in several countries and without official status anywhere, represents an exceptional challenge for effective communication in medical contexts. Taking advantage of the large number of government-produced medical information documents in Hmong, we have developed the first named entity-annotated biomedical corpus for a resource-poor minority language. The Hmong Medical Corpus contains 100,535 tokens with 4554 named entities (NEs) of three UMLS semantic types: diseases/syndromes, signs/symptoms, and body parts/organs/organ components. Furthermore, a subset of the corpus is annotated for word position and parts of speech, representing the first such gold-standard dataset publicly available for Hmong. The methodology presented provides a readily reproducible approach for the creation of biomedical NE-annotated corpora for other resource-poor languages

Transfer learning for Turkish named entity recognition on noisy text

This is an accepted manuscript of an article published by Cambridge University Press in Natural Language Engineering on 28/01/2020, available online: https://doi.org/10.1017/S1351324919000627 The accepted version of the publication may differ from the final published version.© Cambridge University Press 2020. In this article, we investigate using deep neural networks with different word representation techniques for named entity recognition (NER) on Turkish noisy text. We argue that valuable latent features for NER can, in fact, be learned without using any hand-crafted features and/or domain-specific resources such as gazetteers and lexicons. In this regard, we utilize character-level, character n-gram-level, morpheme-level, and orthographic character-level word representations. Since noisy data with NER annotation are scarce for Turkish, we introduce a transfer learning model in order to learn infrequent entity types as an extension to the Bi-LSTM-CRF architecture by incorporating an additional conditional random field (CRF) layer that is trained on a larger (but formal) text and a noisy text simultaneously. This allows us to learn from both formal and informal/noisy text, thus improving the performance of our model further for rarely seen entity types. We experimented on Turkish as a morphologically rich language and English as a relatively morphologically poor language. We obtained an entity-level F1 score of 67.39% on Turkish noisy data and 45.30% on English noisy data, which outperforms the current state-of-art models on noisy text. The English scores are lower compared to Turkish scores because of the intense sparsity in the data introduced by the user writing styles. The results prove that using subword information significantly contributes to learning latent features for morphologically rich languages.Published versio

한국어 사전학습모델 구축과 확장 연구: 감정분석을 중심으로

학위논문 (박사) -- 서울대학교 대학원 : 인문대학 언어학과, 2021. 2. 신효필.Recently, as interest in the Bidirectional Encoder Representations from Transformers (BERT) model has increased, many studies have also been actively conducted in Natural Language Processing based on the model. Such sentence-level contextualized embedding models are generally known to capture and model lexical, syntactic, and semantic information in sentences during training. Therefore, such models, including ELMo, GPT, and BERT, function as a universal model that can impressively perform a wide range of NLP tasks. This study proposes a monolingual BERT model trained based on Korean texts. The first released BERT model that can handle the Korean language was Google Research’s multilingual BERT (M-BERT), which was constructed with training data and a vocabulary composed of 104 languages, including Korean and English, and can handle the text of any language contained in the single model. However, despite the advantages of multilingualism, this model does not fully reflect each language’s characteristics, so that its text processing performance in each language is lower than that of a monolingual model. While mitigating those shortcomings, we built monolingual models using the training data and a vocabulary organized to better capture Korean texts’ linguistic knowledge. Therefore, in this study, a model named KR-BERT was built using training data composed of Korean Wikipedia text and news articles, and was released through GitHub so that it could be used for processing Korean texts. Additionally, we trained a KR-BERT-MEDIUM model based on expanded data by adding comments and legal texts to the training data of KR-BERT. Each model used a list of tokens composed mainly of Hangul characters as its vocabulary, organized using WordPiece algorithms based on the corresponding training data. These models reported competent performances in various Korean NLP tasks such as Named Entity Recognition, Question Answering, Semantic Textual Similarity, and Sentiment Analysis. In addition, we added sentiment features to the BERT model to specialize it to better function in sentiment analysis. We constructed a sentiment-combined model including sentiment features, where the features consist of polarity and intensity values assigned to each token in the training data corresponding to that of Korean Sentiment Analysis Corpus (KOSAC). The sentiment features assigned to each token compose polarity and intensity embeddings and are infused to the basic BERT input embeddings. The sentiment-combined model is constructed by training the BERT model with these embeddings. We trained a model named KR-BERT-KOSAC that contains sentiment features while maintaining the same training data, vocabulary, and model configurations as KR-BERT and distributed it through GitHub. Then we analyzed the effects of using sentiment features in comparison to KR-BERT by observing their performance in language modeling during the training process and sentiment analysis tasks. Additionally, we determined how much each of the polarity and intensity features contributes to improving the model performance by separately organizing a model that utilizes each of the features, respectively. We obtained some increase in language modeling and sentiment analysis performances by using both the sentiment features, compared to other models with different feature composition. Here, we included the problems of binary positivity classification of movie reviews and hate speech detection on offensive comments as the sentiment analysis tasks. On the other hand, training these embedding models requires a lot of training time and hardware resources. Therefore, this study proposes a simple model fusing method that requires relatively little time. We trained a smaller-scaled sentiment-combined model consisting of a smaller number of encoder layers and attention heads and smaller hidden sizes for a few steps, combining it with an existing pre-trained BERT model. Since those pre-trained models are expected to function universally to handle various NLP problems based on good language modeling, this combination will allow two models with different advantages to interact and have better text processing capabilities. In this study, experiments on sentiment analysis problems have confirmed that combining the two models is efficient in training time and usage of hardware resources, while it can produce more accurate predictions than single models that do not include sentiment features.최근 트랜스포머 양방향 인코더 표현 (Bidirectional Encoder Representations from Transformers, BERT) 모델에 대한 관심이 높아지면서 자연어처리 분야에서 이에 기반한 연구 역시 활발히 이루어지고 있다. 이러한 문장 단위의 임베딩을 위한 모델들은 보통 학습 과정에서 문장 내 어휘, 통사, 의미 정보를 포착하여 모델링한다고 알려져 있다. 따라서 ELMo, GPT, BERT 등은 그 자체가 다양한 자연어처리 문제를 해결할 수 있는 보편적인 모델로서 기능한다. 본 연구는 한국어 자료로 학습한 단일 언어 BERT 모델을 제안한다. 가장 먼저 공개된 한국어를 다룰 수 있는 BERT 모델은 Google Research의 multilingual BERT (M-BERT)였다. 이는 한국어와 영어를 포함하여 104개 언어로 구성된 학습 데이터와 어휘 목록을 가지고 학습한 모델이며, 모델 하나로 포함된 모든 언어의 텍스트를 처리할 수 있다. 그러나 이는 그 다중언어성이 갖는 장점에도 불구하고, 각 언어의 특성을 충분히 반영하지 못하여 단일 언어 모델보다 각 언어의 텍스트 처리 성능이 낮다는 단점을 보인다. 본 연구는 그러한 단점들을 완화하면서 텍스트에 포함되어 있는 언어 정보를 보다 잘 포착할 수 있도록 구성된 데이터와 어휘 목록을 이용하여 모델을 구축하고자 하였다. 따라서 본 연구에서는 한국어 Wikipedia 텍스트와 뉴스 기사로 구성된 데이터를 이용하여 KR-BERT 모델을 구현하고, 이를 GitHub을 통해 공개하여 한국어 정보처리를 위해 사용될 수 있도록 하였다. 또한 해당 학습 데이터에 댓글 데이터와 법조문과 판결문을 덧붙여 확장한 텍스트에 기반해서 다시 KR-BERT-MEDIUM 모델을 학습하였다. 이 모델은 해당 학습 데이터로부터 WordPiece 알고리즘을 이용해 구성한 한글 중심의 토큰 목록을 사전으로 이용하였다. 이들 모델은 개체명 인식, 질의응답, 문장 유사도 판단, 감정 분석 등의 다양한 한국어 자연어처리 문제에 적용되어 우수한 성능을 보고했다. 또한 본 연구에서는 BERT 모델에 감정 자질을 추가하여 그것이 감정 분석에 특화된 모델로서 확장된 기능을 하도록 하였다. 감정 자질을 포함하여 별도의 임베딩 모델을 학습시켰는데, 이때 감정 자질은 문장 내의 각 토큰에 한국어 감정 분석 코퍼스 (KOSAC)에 대응하는 감정 극성(polarity)과 강도(intensity) 값을 부여한 것이다. 각 토큰에 부여된 자질은 그 자체로 극성 임베딩과 강도 임베딩을 구성하고, BERT가 기본으로 하는 토큰 임베딩에 더해진다. 이렇게 만들어진 임베딩을 학습한 것이 감정 자질 모델(sentiment-combined model)이 된다. KR-BERT와 같은 학습 데이터와 모델 구성을 유지하면서 감정 자질을 결합한 모델인 KR-BERT-KOSAC를 구현하고, 이를 GitHub을 통해 배포하였다. 또한 그로부터 학습 과정 내 언어 모델링과 감정 분석 과제에서의 성능을 얻은 뒤 KR-BERT와 비교하여 감정 자질 추가의 효과를 살펴보았다. 또한 감정 자질 중 극성과 강도 값을 각각 적용한 모델을 별도 구성하여 각 자질이 모델 성능 향상에 얼마나 기여하는지도 확인하였다. 이를 통해 두 가지 감정 자질을 모두 추가한 경우에, 그렇지 않은 다른 모델들에 비하여 언어 모델링이나 감정 분석 문제에서 성능이 어느 정도 향상되는 것을 관찰할 수 있었다. 이때 감정 분석 문제로는 영화평의 긍부정 여부 분류와 댓글의 악플 여부 분류를 포함하였다. 그런데 위와 같은 임베딩 모델을 사전학습하는 것은 많은 시간과 하드웨어 등의 자원을 요구한다. 따라서 본 연구에서는 비교적 적은 시간과 자원을 사용하는 간단한 모델 결합 방법을 제시한다. 적은 수의 인코더 레이어, 어텐션 헤드, 적은 임베딩 차원 수로 구성한 감정 자질 모델을 적은 스텝 수까지만 학습하고, 이를 기존에 큰 규모로 사전학습되어 있는 임베딩 모델과 결합한다. 기존의 사전학습모델에는 충분한 언어 모델링을 통해 다양한 언어 처리 문제를 처리할 수 있는 보편적인 기능이 기대되므로, 이러한 결합은 서로 다른 장점을 갖는 두 모델이 상호작용하여 더 우수한 자연어처리 능력을 갖도록 할 것이다. 본 연구에서는 감정 분석 문제들에 대한 실험을 통해 두 가지 모델의 결합이 학습 시간에 있어 효율적이면서도, 감정 자질을 더하지 않은 모델보다 더 정확한 예측을 할 수 있다는 것을 확인하였다.1 Introduction 1 1.1 Objectives 3 1.2 Contribution 9 1.3 Dissertation Structure 10 2 Related Work 13 2.1 Language Modeling and the Attention Mechanism 13 2.2 BERT-based Models 16 2.2.1 BERT and Variation Models 16 2.2.2 Korean-Specific BERT Models 19 2.2.3 Task-Specific BERT Models 22 2.3 Sentiment Analysis 24 2.4 Chapter Summary 30 3 BERT Architecture and Evaluations 33 3.1 Bidirectional Encoder Representations from Transformers (BERT) 33 3.1.1 Transformers and the Multi-Head Self-Attention Mechanism 34 3.1.2 Tokenization and Embeddings of BERT 39 3.1.3 Training and Fine-Tuning BERT 42 3.2 Evaluation of BERT 47 3.2.1 NLP Tasks 47 3.2.2 Metrics 50 3.3 Chapter Summary 52 4 Pre-Training of Korean BERT-based Model 55 4.1 The Need for a Korean Monolingual Model 55 4.2 Pre-Training Korean-specific BERT Model 58 4.3 Chapter Summary 70 5 Performances of Korean-Specific BERT Models 71 5.1 Task Datasets 71 5.1.1 Named Entity Recognition 71 5.1.2 Question Answering 73 5.1.3 Natural Language Inference 74 5.1.4 Semantic Textual Similarity 78 5.1.5 Sentiment Analysis 80 5.2 Experiments 81 5.2.1 Experiment Details 81 5.2.2 Task Results 83 5.3 Chapter Summary 89 6 An Extended Study to Sentiment Analysis 91 6.1 Sentiment Features 91 6.1.1 Sources of Sentiment Features 91 6.1.2 Assigning Prior Sentiment Values 94 6.2 Composition of Sentiment Embeddings 103 6.3 Training the Sentiment-Combined Model 109 6.4 Effect of Sentiment Features 113 6.5 Chapter Summary 121 7 Combining Two BERT Models 123 7.1 External Fusing Method 123 7.2 Experiments and Results 130 7.3 Chapter Summary 135 8 Conclusion 137 8.1 Summary of Contribution and Results 138 8.1.1 Construction of Korean Pre-trained BERT Models 138 8.1.2 Construction of a Sentiment-Combined Model 138 8.1.3 External Fusing of Two Pre-Trained Models to Gain Performance and Cost Advantages 139 8.2 Future Directions and Open Problems 140 8.2.1 More Training of KR-BERT-MEDIUM for Convergence of Performance 140 8.2.2 Observation of Changes Depending on the Domain of Training Data 141 8.2.3 Overlap of Sentiment Features with Linguistic Knowledge that BERT Learns 142 8.2.4 The Specific Process of Sentiment Features Helping the Language Modeling of BERT is Unknown 143 Bibliography 145 Appendices 157 A. Python Sources 157 A.1 Construction of Polarity and Intensity Embeddings 157 A.2 External Fusing of Different Pre-Trained Models 158 B. Examples of Experiment Outputs 162 C. Model Releases through GitHub 165Docto

Automating information extraction task for Turkish texts

Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2011.Thesis (Ph. D.) -- Bilkent University, 2011.Includes bibliographical references leaves 85-97.Throughout history, mankind has often suffered from a lack of necessary resources. In today’s information world, the challenge can sometimes be a wealth of resources. That is to say, an excessive amount of information implies the need to find and extract necessary information. Information extraction can be defined as the identification of selected types of entities, relations, facts or events in a set of unstructured text documents in a natural language. The goal of our research is to build a system that automatically locates and extracts information from Turkish unstructured texts. Our study focuses on two basic Information Extraction (IE) tasks: Named Entity Recognition and Entity Relation Detection. Named Entity Recognition, finding named entities (persons, locations, organizations, etc.) located in unstructured texts, is one of the most fundamental IE tasks. Entity Relation Detection task tries to identify relationships between entities mentioned in text documents. Using supervised learning strategy, the developed systems start with a set of examples collected from a training dataset and generate the extraction rules from the given examples by using a carefully designed coverage algorithm. Moreover, several rule filtering and rule refinement techniques are utilized to maximize generalization and accuracy at the same time. In order to obtain accurate generalization, we use several syntactic and semantic features of the text, including: orthographical, contextual, lexical and morphological features. In particular, morphological features of the text are effectively used in this study to increase the extraction performance for Turkish, an agglutinative language. Since the system does not rely on handcrafted rules/patterns, it does not heavily suffer from domain adaptability problem. The results of the conducted experiments show that (1) the developed systems are successfully applicable to the Named Entity Recognition and Entity Relation Detection tasks, and (2) exploiting morphological features can significantly improve the performance of information extraction from Turkish, an agglutinative language.Tatar, SerhanPh.D