Data-efficient methods for information extraction

Strukturierte Wissensrepräsentationssysteme wie Wissensdatenbanken oder Wissensgraphen bieten Einblicke in Entitäten und Beziehungen zwischen diesen Entitäten in der realen Welt. Solche Wissensrepräsentationssysteme können in verschiedenen Anwendungen der natürlichen Sprachverarbeitung eingesetzt werden, z. B. bei der semantischen Suche, der Beantwortung von Fragen und der Textzusammenfassung. Es ist nicht praktikabel und ineffizient, diese Wissensrepräsentationssysteme manuell zu befüllen. In dieser Arbeit entwickeln wir Methoden, um automatisch benannte Entitäten und Beziehungen zwischen den Entitäten aus Klartext zu extrahieren. Unsere Methoden können daher verwendet werden, um entweder die bestehenden unvollständigen Wissensrepräsentationssysteme zu vervollständigen oder ein neues strukturiertes Wissensrepräsentationssystem von Grund auf zu erstellen. Im Gegensatz zu den gängigen überwachten Methoden zur Informationsextraktion konzentrieren sich unsere Methoden auf das Szenario mit wenigen Daten und erfordern keine große Menge an kommentierten Daten. Im ersten Teil der Arbeit haben wir uns auf das Problem der Erkennung von benannten Entitäten konzentriert. Wir haben an der gemeinsamen Aufgabe von Bacteria Biotope 2019 teilgenommen. Die gemeinsame Aufgabe besteht darin, biomedizinische Entitätserwähnungen zu erkennen und zu normalisieren. Unser linguistically informed Named-Entity-Recognition-System besteht aus einem Deep-Learning-basierten Modell, das sowohl verschachtelte als auch flache Entitäten extrahieren kann; unser Modell verwendet mehrere linguistische Merkmale und zusätzliche Trainingsziele, um effizientes Lernen in datenarmen Szenarien zu ermöglichen. Unser System zur Entitätsnormalisierung verwendet String-Match, Fuzzy-Suche und semantische Suche, um die extrahierten benannten Entitäten mit den biomedizinischen Datenbanken zu verknüpfen. Unser System zur Erkennung von benannten Entitäten und zur Entitätsnormalisierung erreichte die niedrigste Slot-Fehlerrate von 0,715 und belegte den ersten Platz in der gemeinsamen Aufgabe. Wir haben auch an zwei gemeinsamen Aufgaben teilgenommen: Adverse Drug Effect Span Detection (Englisch) und Profession Span Detection (Spanisch); beide Aufgaben sammeln Daten von der Social Media Plattform Twitter. Wir haben ein Named-Entity-Recognition-Modell entwickelt, das die Eingabedarstellung des Modells durch das Stapeln heterogener Einbettungen aus verschiedenen Domänen verbessern kann; unsere empirischen Ergebnisse zeigen komplementäres Lernen aus diesen heterogenen Einbettungen. Unser Beitrag belegte den 3. Platz in den beiden gemeinsamen Aufgaben. Im zweiten Teil der Arbeit untersuchten wir Strategien zur Erweiterung synthetischer Daten, um ressourcenarme Informationsextraktion in spezialisierten Domänen zu ermöglichen. Insbesondere haben wir backtranslation an die Aufgabe der Erkennung von benannten Entitäten auf Token-Ebene und der Extraktion von Beziehungen auf Satzebene angepasst. Wir zeigen, dass die Rückübersetzung sprachlich vielfältige und grammatikalisch kohärente synthetische Sätze erzeugen kann und als wettbewerbsfähige Erweiterungsstrategie für die Aufgaben der Erkennung von benannten Entitäten und der Extraktion von Beziehungen dient. Bei den meisten realen Aufgaben zur Extraktion von Beziehungen stehen keine kommentierten Daten zur Verfügung, jedoch ist häufig ein großer unkommentierter Textkorpus vorhanden. Bootstrapping-Methoden zur Beziehungsextraktion können mit diesem großen Korpus arbeiten, da sie nur eine Handvoll Startinstanzen benötigen. Bootstrapping-Methoden neigen jedoch dazu, im Laufe der Zeit Rauschen zu akkumulieren (bekannt als semantische Drift), und dieses Phänomen hat einen drastischen negativen Einfluss auf die endgültige Genauigkeit der Extraktionen. Wir entwickeln zwei Methoden zur Einschränkung des Bootstrapping-Prozesses, um die semantische Drift bei der Extraktion von Beziehungen zu minimieren. Unsere Methoden nutzen die Graphentheorie und vortrainierte Sprachmodelle, um verrauschte Extraktionsmuster explizit zu identifizieren und zu entfernen. Wir berichten über die experimentellen Ergebnisse auf dem TACRED-Datensatz für vier Relationen. Im letzten Teil der Arbeit demonstrieren wir die Anwendung der Domänenanpassung auf die anspruchsvolle Aufgabe der mehrsprachigen Akronymextraktion. Unsere Experimente zeigen, dass die Domänenanpassung die Akronymextraktion in wissenschaftlichen und juristischen Bereichen in sechs Sprachen verbessern kann, darunter auch Sprachen mit geringen Ressourcen wie Persisch und Vietnamesisch.The structured knowledge representation systems such as knowledge base or knowledge graph can provide insights regarding entities and relationship(s) among these entities in the real-world, such knowledge representation systems can be employed in various natural language processing applications such as semantic search, question answering and text summarization. It is infeasible and inefficient to manually populate these knowledge representation systems. In this work, we develop methods to automatically extract named entities and relationships among the entities from plain text and hence our methods can be used to either complete the existing incomplete knowledge representation systems to create a new structured knowledge representation system from scratch. Unlike mainstream supervised methods for information extraction, our methods focus on the low-data scenario and do not require a large amount of annotated data. In the first part of the thesis, we focused on the problem of named entity recognition. We participated in the shared task of Bacteria Biotope 2019, the shared task consists of recognizing and normalizing the biomedical entity mentions. Our linguistically informed named entity recognition system consists of a deep learning based model which can extract both nested and flat entities; our model employed several linguistic features and auxiliary training objectives to enable efficient learning in data-scarce scenarios. Our entity normalization system employed string match, fuzzy search and semantic search to link the extracted named entities to the biomedical databases. Our named entity recognition and entity normalization system achieved the lowest slot error rate of 0.715 and ranked first in the shared task. We also participated in two shared tasks of Adverse Drug Effect Span detection (English) and Profession Span Detection (Spanish); both of these tasks collect data from the social media platform Twitter. We developed a named entity recognition model which can improve the input representation of the model by stacking heterogeneous embeddings from a diverse domain(s); our empirical results demonstrate complementary learning from these heterogeneous embeddings. Our submission ranked 3rd in both of the shared tasks. In the second part of the thesis, we explored synthetic data augmentation strategies to address low-resource information extraction in specialized domains. Specifically, we adapted backtranslation to the token-level task of named entity recognition and sentence-level task of relation extraction. We demonstrate that backtranslation can generate linguistically diverse and grammatically coherent synthetic sentences and serve as a competitive augmentation strategy for the task of named entity recognition and relation extraction. In most of the real-world relation extraction tasks, the annotated data is not available, however, quite often a large unannotated text corpus is available. Bootstrapping methods for relation extraction can operate on this large corpus as they only require a handful of seed instances. However, bootstrapping methods tend to accumulate noise over time (known as semantic drift) and this phenomenon has a drastic negative impact on the final precision of the extractions. We develop two methods to constrain the bootstrapping process to minimise semantic drift for relation extraction; our methods leverage graph theory and pre-trained language models to explicitly identify and remove noisy extraction patterns. We report the experimental results on the TACRED dataset for four relations. In the last part of the thesis, we demonstrate the application of domain adaptation to the challenging task of multi-lingual acronym extraction. Our experiments demonstrate that domain adaptation can improve acronym extraction within scientific and legal domains in 6 languages including low-resource languages such as Persian and Vietnamese

Information Extraction on Para-Relational Data.

Para-relational data (such as spreadsheets and diagrams) refers to a type of nearly relational data that shares the important qualities of relational data but does not present itself in a relational format. Para-relational data often conveys highly valuable information and is widely used in many different areas. If we can convert para-relational data into the relational format, many existing tools can be leveraged for a variety of interesting applications, such as data analysis with relational query systems and data integration applications. This dissertation aims to convert para-relational data into a high-quality relational form with little user assistance. We have developed four standalone systems, each addressing a specific type of para-relational data. Senbazuru is a prototype spreadsheet database management system that extracts relational information from a large number of spreadsheets. Anthias is an extension of the Senbazuru system to convert a broader range of spreadsheets into a relational format. Lyretail is an extraction system to detect long-tail dictionary entities on webpages. Finally, DiagramFlyer is a web-based search system that obtains a large number of diagrams automatically extracted from web-crawled PDFs. Together, these four systems demonstrate that converting para-relational data into the relational format is possible today, and also suggest directions for future systems.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120853/1/chenzhe_1.pd

Biomedical Information Extraction Pipelines for Public Health in the Age of Deep Learning

abstract: Unstructured texts containing biomedical information from sources such as electronic health records, scientific literature, discussion forums, and social media offer an opportunity to extract information for a wide range of applications in biomedical informatics. Building scalable and efficient pipelines for natural language processing and extraction of biomedical information plays an important role in the implementation and adoption of applications in areas such as public health. Advancements in machine learning and deep learning techniques have enabled rapid development of such pipelines. This dissertation presents entity extraction pipelines for two public health applications: virus phylogeography and pharmacovigilance. For virus phylogeography, geographical locations are extracted from biomedical scientific texts for metadata enrichment in the GenBank database containing 2.9 million virus nucleotide sequences. For pharmacovigilance, tools are developed to extract adverse drug reactions from social media posts to open avenues for post-market drug surveillance from non-traditional sources. Across these pipelines, high variance is observed in extraction performance among the entities of interest while using state-of-the-art neural network architectures. To explain the variation, linguistic measures are proposed to serve as indicators for entity extraction performance and to provide deeper insight into the domain complexity and the challenges associated with entity extraction. For both the phylogeography and pharmacovigilance pipelines presented in this work the annotated datasets and applications are open source and freely available to the public to foster further research in public health.Dissertation/ThesisDoctoral Dissertation Biomedical Informatics 201

Unsupervised learning of relation detection patterns

L'extracció d'informació és l'àrea del processament de llenguatge natural l'objectiu de la qual és l'obtenir dades estructurades a partir de la informació rellevant continguda en fragments textuals. L'extracció d'informació requereix una quantitat considerable de coneixement lingüístic. La especificitat d'aquest coneixement suposa un inconvenient de cara a la portabilitat dels sistemes, ja que un canvi d'idioma, domini o estil té un cost en termes d'esforç humà. Durant dècades, s'han aplicat tècniques d'aprenentatge automàtic per tal de superar aquest coll d'ampolla de portabilitat, reduint progressivament la supervisió humana involucrada. Tanmateix, a mida que augmenta la disponibilitat de grans col·leccions de documents, esdevenen necessàries aproximacions completament nosupervisades per tal d'explotar el coneixement que hi ha en elles. La proposta d'aquesta tesi és la d'incorporar tècniques de clustering a l'adquisició de patrons per a extracció d'informació, per tal de reduir encara més els elements de supervisió involucrats en el procés En particular, el treball se centra en el problema de la detecció de relacions. L'assoliment d'aquest objectiu final ha requerit, en primer lloc, el considerar les diferents estratègies en què aquesta combinació es podia dur a terme; en segon lloc, el desenvolupar o adaptar algorismes de clustering adequats a les nostres necessitats; i en tercer lloc, el disseny de procediments d'adquisició de patrons que incorporessin la informació de clustering. Al final d'aquesta tesi, havíem estat capaços de desenvolupar i implementar una aproximació per a l'aprenentatge de patrons per a detecció de relacions que, utilitzant tècniques de clustering i un mínim de supervisió humana, és competitiu i fins i tot supera altres aproximacions comparables en l'estat de l'art.Information extraction is the natural language processing area whose goal is to obtain structured data from the relevant information contained in textual fragments. Information extraction requires a significant amount of linguistic knowledge. The specificity of such knowledge supposes a drawback on the portability of the systems, as a change of language, domain or style demands a costly human effort. Machine learning techniques have been applied for decades so as to overcome this portability bottleneck¿progressively reducing the amount of involved human supervision. However, as the availability of large document collections increases, completely unsupervised approaches become necessary in order to mine the knowledge contained in them. The proposal of this thesis is to incorporate clustering techniques into pattern learning for information extraction, in order to further reduce the elements of supervision involved in the process. In particular, the work focuses on the problem of relation detection. The achievement of this ultimate goal has required, first, considering the different strategies in which this combination could be carried out; second, developing or adapting clustering algorithms suitable to our needs; and third, devising pattern learning procedures which incorporated clustering information. By the end of this thesis, we had been able to develop and implement an approach for learning of relation detection patterns which, using clustering techniques and minimal human supervision, is competitive and even outperforms other comparable approaches in the state of the art.Postprint (published version

Minimally-supervised Methods for Arabic Named Entity Recognition

Named Entity Recognition (NER) has attracted much attention over the past twenty years, as a main task of Information Extraction. The current dominant techniques for addressing NER are supervised methods that can achieve high performance, but require new manually annotated data for every new domain and/or genre change. Our work focuses on approaches that make it possible to tackle new domains with minimal human intervention to identify Named Entities (NEs) in Arabic text. Specifically, we investigate two minimally-supervised methods: semi-supervised learning and distant learning. Our semi-supervised algorithm for identifying NEs does not require annotated training data or gazetteers. It only requires, for each NE type, a seed list of a few instances to initiate the learning process. Novel aspects of our algorithm include (i) a new way to produce and generalise the extraction patterns (ii) a new filtering criterion to remove noisy patterns (iii) a comparison of two ranking measures for determining the most reliable candidate NEs. Next, we present our methodology to exploit Wikipedia structure to automatically develop an Arabic NE annotated corpus. A novel mechanism is introduced, based on the high coverage of Wikipedia, in order to address two challenges particular to tagging NEs in Arabic text: rich morphology and the absence of capitalisation. Neither technique has yet achieved performance levels comparable to those of supervised methods. Semi-supervised algorithms tend to have high precision but comparatively low recall, whereas distant learning tends to achieve higher recall but lower precision. Therefore, we present a novel approach to Arabic NER using a combination of semi-supervised and distant learning techniques. We used a variety of classifier combination schemes, including the Bayesian Classifier Combination (BCC) procedure, recently proposed for sentiment analysis. According to our results, the BCC model leads to an increase in performance of 8 percentage points over the best minimally-supervised classifier

Deep learning methods for knowledge base population

Knowledge bases store structured information about entities or concepts of the world and can be used in various applications, such as information retrieval or question answering. A major drawback of existing knowledge bases is their incompleteness. In this thesis, we explore deep learning methods for automatically populating them from text, addressing the following tasks: slot filling, uncertainty detection and type-aware relation extraction. Slot filling aims at extracting information about entities from a large text corpus. The Text Analysis Conference yearly provides new evaluation data in the context of an international shared task. We develop a modular system to address this challenge. It was one of the top-ranked systems in the shared task evaluations in 2015. For its slot filler classification module, we propose contextCNN, a convolutional neural network based on context splitting. It improves the performance of the slot filling system by 5.0% micro and 2.9% macro F1. To train our binary and multiclass classification models, we create a dataset using distant supervision and reduce the number of noisy labels with a self-training strategy. For model optimization and evaluation, we automatically extract a labeled benchmark for slot filler classification from the manual shared task assessments from 2012-2014. We show that results on this benchmark are correlated with slot filling pipeline results with a Pearson's correlation coefficient of 0.89 (0.82) on data from 2013 (2014). The combination of patterns, support vector machines and contextCNN achieves the best results on the benchmark with a micro (macro) F1 of 51% (53%) on test. Finally, we analyze the results of the slot filling pipeline and the impact of its components. For knowledge base population, it is essential to assess the factuality of the statements extracted from text. From the sentence "Obama was rumored to be born in Kenya", a system should not conclude that Kenya is the place of birth of Obama. Therefore, we address uncertainty detection in the second part of this thesis. We investigate attention-based models and make a first attempt to systematize the attention design space. Moreover, we propose novel attention variants: External attention, which incorporates an external knowledge source, k-max average attention, which only considers the vectors with the k maximum attention weights, and sequence-preserving attention, which allows to maintain order information. Our convolutional neural network with external k-max average attention sets the new state of the art on a Wikipedia benchmark dataset with an F1 score of 68%. To the best of our knowledge, we are the first to integrate an uncertainty detection component into a slot filling pipeline. It improves precision by 1.4% and micro F1 by 0.4%. In the last part of the thesis, we investigate type-aware relation extraction with neural networks. We compare different models for joint entity and relation classification: pipeline models, jointly trained models and globally normalized models based on structured prediction. First, we show that using entity class prediction scores instead of binary decisions helps relation classification. Second, joint training clearly outperforms pipeline models on a large-scale distantly supervised dataset with fine-grained entity classes. It improves the area under the precision-recall curve from 0.53 to 0.66. Third, we propose a model with a structured prediction output layer, which globally normalizes the score of a triple consisting of the classes of two entities and the relation between them. It improves relation extraction results by 4.4% F1 on a manually labeled benchmark dataset. Our analysis shows that the model learns correct correlations between entity and relation classes. Finally, we are the first to use neural networks for joint entity and relation classification in a slot filling pipeline. The jointly trained model achieves the best micro F1 score with a score of 22% while the neural structured prediction model performs best in terms of macro F1 with a score of 25%

Automatic text filtering using limited supervision learning for epidemic intelligence

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