Through the Lens of Core Competency: Survey on Evaluation of Large Language Models

From pre-trained language model (PLM) to large language model (LLM), the field of natural language processing (NLP) has witnessed steep performance gains and wide practical uses. The evaluation of a research field guides its direction of improvement. However, LLMs are extremely hard to thoroughly evaluate for two reasons. First of all, traditional NLP tasks become inadequate due to the excellent performance of LLM. Secondly, existing evaluation tasks are difficult to keep up with the wide range of applications in real-world scenarios. To tackle these problems, existing works proposed various benchmarks to better evaluate LLMs. To clarify the numerous evaluation tasks in both academia and industry, we investigate multiple papers concerning LLM evaluations. We summarize 4 core competencies of LLM, including reasoning, knowledge, reliability, and safety. For every competency, we introduce its definition, corresponding benchmarks, and metrics. Under this competency architecture, similar tasks are combined to reflect corresponding ability, while new tasks can also be easily added into the system. Finally, we give our suggestions on the future direction of LLM's evaluation

Entities with quantities : extraction, search, and ranking

Quantities are more than numeric values. They denote measures of the world’s entities such as heights of buildings, running times of athletes, energy efficiency of car models or energy production of power plants, all expressed in numbers with associated units. Entity-centric search and question answering (QA) are well supported by modern search engines. However, they do not work well when the queries involve quantity filters, such as searching for athletes who ran 200m under 20 seconds or companies with quarterly revenue above $2 Billion. State-of-the-art systems fail to understand the quantities, including the condition (less than, above, etc.), the unit of interest (seconds, dollar, etc.), and the context of the quantity (200m race, quarterly revenue, etc.). QA systems based on structured knowledge bases (KBs) also fail as quantities are poorly covered by state-of-the-art KBs. In this dissertation, we developed new methods to advance the state-of-the-art on quantity knowledge extraction and search.Zahlen sind mehr als nur numerische Werte. Sie beschreiben Maße von Entitäten wie die Höhe von Gebäuden, die Laufzeit von Sportlern, die Energieeffizienz von Automodellen oder die Energieerzeugung von Kraftwerken - jeweils ausgedrückt durch Zahlen mit zugehörigen Einheiten. Entitätszentriete Anfragen und direktes Question-Answering werden von Suchmaschinen häufig gut unterstützt. Sie funktionieren jedoch nicht gut, wenn die Fragen Zahlenfilter beinhalten, wie z. B. die Suche nach Sportlern, die 200m unter 20 Sekunden gelaufen sind, oder nach Unternehmen mit einem Quartalsumsatz von über 2 Milliarden US-Dollar. Selbst moderne Systeme schaffen es nicht, Quantitäten, einschließlich der genannten Bedingungen (weniger als, über, etc.), der Maßeinheiten (Sekunden, Dollar, etc.) und des Kontexts (200-Meter-Rennen, Quartalsumsatz usw.), zu verstehen. Auch QA-Systeme, die auf strukturierten Wissensbanken (“Knowledge Bases”, KBs) aufgebaut sind, versagen, da quantitative Eigenschaften von modernen KBs kaum erfasst werden. In dieser Dissertation werden neue Methoden entwickelt, um den Stand der Technik zur Wissensextraktion und -suche von Quantitäten voranzutreiben. Unsere Hauptbeiträge sind die folgenden: • Zunächst präsentieren wir Qsearch [Ho et al., 2019, Ho et al., 2020] – ein System, das mit erweiterten Fragen mit Quantitätsfiltern umgehen kann, indem es Hinweise verwendet, die sowohl in der Frage als auch in den Textquellen vorhanden sind. Qsearch umfasst zwei Hauptbeiträge. Der erste Beitrag ist ein tiefes neuronales Netzwerkmodell, das für die Extraktion quantitätszentrierter Tupel aus Textquellen entwickelt wurde. Der zweite Beitrag ist ein neuartiges Query-Matching-Modell zum Finden und zur Reihung passender Tupel. • Zweitens, um beim Vorgang heterogene Tabellen einzubinden, stellen wir QuTE [Ho et al., 2021a, Ho et al., 2021b] vor – ein System zum Extrahieren von Quantitätsinformationen aus Webquellen, insbesondere Ad-hoc Webtabellen in HTML-Seiten. Der Beitrag von QuTE umfasst eine Methode zur Verknüpfung von Quantitäts- und Entitätsspalten, für die externe Textquellen genutzt werden. Zur Beantwortung von Fragen kontextualisieren wir die extrahierten Entitäts-Quantitäts-Paare mit informativen Hinweisen aus der Tabelle und stellen eine neue Methode zur Konsolidierung und verbesserteer Reihung von Antwortkandidaten durch Inter-Fakten-Konsistenz vor. • Drittens stellen wir QL [Ho et al., 2022] vor – eine Recall-orientierte Methode zur Anreicherung von Knowledge Bases (KBs) mit quantitativen Fakten. Moderne KBs wie Wikidata oder YAGO decken viele Entitäten und ihre relevanten Informationen ab, übersehen aber oft wichtige quantitative Eigenschaften. QL ist frage-gesteuert und basiert auf iterativem Lernen mit zwei Hauptbeiträgen, um die KB-Abdeckung zu verbessern. Der erste Beitrag ist eine Methode zur Expansion von Fragen, um einen größeren Pool an Faktenkandidaten zu erfassen. Der zweite Beitrag ist eine Technik zur Selbstkonsistenz durch Berücksichtigung der Werteverteilungen von Quantitäten

Neural Topic Modeling with Continual Lifelong Learning

Lifelong learning has recently attracted attention in building machine learning systems that continually accumulate and transfer knowledge to help future learning. Unsupervised topic modeling has been popularly used to discover topics from document collections. However, the application of topic modeling is challenging due to data sparsity, e.g., in a small collection of (short) documents and thus, generate incoherent topics and sub-optimal document representations. To address the problem, we propose a lifelong learning framework for neural topic modeling that can continuously process streams of document collections, accumulate topics and guide future topic modeling tasks by knowledge transfer from several sources to better deal with the sparse data. In the lifelong process, we particularly investigate jointly: (1) sharing generative homologies (latent topics) over lifetime to transfer prior knowledge, and (2) minimizing catastrophic forgetting to retain the past learning via novel selective data augmentation, co-training and topic regularization approaches. Given a stream of document collections, we apply the proposed Lifelong Neural Topic Modeling (LNTM) framework in modeling three sparse document collections as future tasks and demonstrate improved performance quantified by perplexity, topic coherence and information retrieval task.Comment: ICML202

Linguistically-Informed Neural Architectures for Lexical, Syntactic and Semantic Tasks in Sanskrit

The primary focus of this thesis is to make Sanskrit manuscripts more accessible to the end-users through natural language technologies. The morphological richness, compounding, free word orderliness, and low-resource nature of Sanskrit pose significant challenges for developing deep learning solutions. We identify four fundamental tasks, which are crucial for developing a robust NLP technology for Sanskrit: word segmentation, dependency parsing, compound type identification, and poetry analysis. The first task, Sanskrit Word Segmentation (SWS), is a fundamental text processing task for any other downstream applications. However, it is challenging due to the sandhi phenomenon that modifies characters at word boundaries. Similarly, the existing dependency parsing approaches struggle with morphologically rich and low-resource languages like Sanskrit. Compound type identification is also challenging for Sanskrit due to the context-sensitive semantic relation between components. All these challenges result in sub-optimal performance in NLP applications like question answering and machine translation. Finally, Sanskrit poetry has not been extensively studied in computational linguistics. While addressing these challenges, this thesis makes various contributions: (1) The thesis proposes linguistically-informed neural architectures for these tasks. (2) We showcase the interpretability and multilingual extension of the proposed systems. (3) Our proposed systems report state-of-the-art performance. (4) Finally, we present a neural toolkit named SanskritShala, a web-based application that provides real-time analysis of input for various NLP tasks. Overall, this thesis contributes to making Sanskrit manuscripts more accessible by developing robust NLP technology and releasing various resources, datasets, and web-based toolkit.Comment: Ph.D. dissertatio

Neural information extraction from natural language text

Natural language processing (NLP) deals with building computational techniques that allow computers to automatically analyze and meaningfully represent human language. With an exponential growth of data in this digital era, the advent of NLP-based systems has enabled us to easily access relevant information via a wide range of applications, such as web search engines, voice assistants, etc. To achieve it, a long-standing research for decades has been focusing on techniques at the intersection of NLP and machine learning. In recent years, deep learning techniques have exploited the expressive power of Artificial Neural Networks (ANNs) and achieved state-of-the-art performance in a wide range of NLP tasks. Being one of the vital properties, Deep Neural Networks (DNNs) can automatically extract complex features from the input data and thus, provide an alternative to the manual process of handcrafted feature engineering. Besides ANNs, Probabilistic Graphical Models (PGMs), a coupling of graph theory and probabilistic methods have the ability to describe causal structure between random variables of the system and capture a principled notion of uncertainty. Given the characteristics of DNNs and PGMs, they are advantageously combined to build powerful neural models in order to understand the underlying complexity of data. Traditional machine learning based NLP systems employed shallow computational methods (e.g., SVM or logistic regression) and relied on handcrafting features which is time-consuming, complex and often incomplete. However, deep learning and neural network based methods have recently shown superior results on various NLP tasks, such as machine translation, text classification, namedentity recognition, relation extraction, textual similarity, etc. These neural models can automatically extract an effective feature representation from training data. This dissertation focuses on two NLP tasks: relation extraction and topic modeling. The former aims at identifying semantic relationships between entities or nominals within a sentence or document. Successfully extracting the semantic relationships greatly contributes in building structured knowledge bases, useful in downstream NLP application areas of web search, question-answering, recommendation engines, etc. On other hand, the task of topic modeling aims at understanding the thematic structures underlying in a collection of documents. Topic modeling is a popular text-mining tool to automatically analyze a large collection of documents and understand topical semantics without actually reading them. In doing so, it generates word clusters (i.e., topics) and document representations useful in document understanding and information retrieval, respectively. Essentially, the tasks of relation extraction and topic modeling are built upon the quality of representations learned from text. In this dissertation, we have developed task-specific neural models for learning representations, coupled with relation extraction and topic modeling tasks in the realms of supervised and unsupervised machine learning paradigms, respectively. More specifically, we make the following contributions in developing neural models for NLP tasks: 1. Neural Relation Extraction: Firstly, we have proposed a novel recurrent neural network based architecture for table-filling in order to jointly perform entity and relation extraction within sentences. Then, we have further extended our scope of extracting relationships between entities across sentence boundaries, and presented a novel dependency-based neural network architecture. The two contributions lie in the supervised paradigm of machine learning. Moreover, we have contributed in building a robust relation extractor constrained by the lack of labeled data, where we have proposed a novel weakly-supervised bootstrapping technique. Given the contributions, we have further explored interpretability of the recurrent neural networks to explain their predictions for the relation extraction task. 2. Neural Topic Modeling: Besides the supervised neural architectures, we have also developed unsupervised neural models to learn meaningful document representations within topic modeling frameworks. Firstly, we have proposed a novel dynamic topic model that captures topics over time. Next, we have contributed in building static topic models without considering temporal dependencies, where we have presented neural topic modeling architectures that also exploit external knowledge, i.e., word embeddings to address data sparsity. Moreover, we have developed neural topic models that incorporate knowledge transfers using both the word embeddings and latent topics from many sources. Finally, we have shown improving neural topic modeling by introducing language structures (e.g., word ordering, local syntactic and semantic information, etc.) that deals with bag-of-words issues in traditional topic models. The class of proposed neural NLP models in this section are based on techniques at the intersection of PGMs, deep learning and ANNs. Here, the task of neural relation extraction employs neural networks to learn representations typically at the sentence level, without access to the broader document context. However, topic models have access to statistical information across documents. Therefore, we advantageously combine the two complementary learning paradigms in a neural composite model, consisting of a neural topic and a neural language model that enables us to jointly learn thematic structures in a document collection via the topic model, and word relations within a sentence via the language model. Overall, our research contributions in this dissertation extend NLP-based systems for relation extraction and topic modeling tasks with state-of-the-art performances

Neural information extraction from natural language text

Natural language processing (NLP) deals with building computational techniques that allow computers to automatically analyze and meaningfully represent human language. With an exponential growth of data in this digital era, the advent of NLP-based systems has enabled us to easily access relevant information via a wide range of applications, such as web search engines, voice assistants, etc. To achieve it, a long-standing research for decades has been focusing on techniques at the intersection of NLP and machine learning. In recent years, deep learning techniques have exploited the expressive power of Artificial Neural Networks (ANNs) and achieved state-of-the-art performance in a wide range of NLP tasks. Being one of the vital properties, Deep Neural Networks (DNNs) can automatically extract complex features from the input data and thus, provide an alternative to the manual process of handcrafted feature engineering. Besides ANNs, Probabilistic Graphical Models (PGMs), a coupling of graph theory and probabilistic methods have the ability to describe causal structure between random variables of the system and capture a principled notion of uncertainty. Given the characteristics of DNNs and PGMs, they are advantageously combined to build powerful neural models in order to understand the underlying complexity of data. Traditional machine learning based NLP systems employed shallow computational methods (e.g., SVM or logistic regression) and relied on handcrafting features which is time-consuming, complex and often incomplete. However, deep learning and neural network based methods have recently shown superior results on various NLP tasks, such as machine translation, text classification, namedentity recognition, relation extraction, textual similarity, etc. These neural models can automatically extract an effective feature representation from training data. This dissertation focuses on two NLP tasks: relation extraction and topic modeling. The former aims at identifying semantic relationships between entities or nominals within a sentence or document. Successfully extracting the semantic relationships greatly contributes in building structured knowledge bases, useful in downstream NLP application areas of web search, question-answering, recommendation engines, etc. On other hand, the task of topic modeling aims at understanding the thematic structures underlying in a collection of documents. Topic modeling is a popular text-mining tool to automatically analyze a large collection of documents and understand topical semantics without actually reading them. In doing so, it generates word clusters (i.e., topics) and document representations useful in document understanding and information retrieval, respectively. Essentially, the tasks of relation extraction and topic modeling are built upon the quality of representations learned from text. In this dissertation, we have developed task-specific neural models for learning representations, coupled with relation extraction and topic modeling tasks in the realms of supervised and unsupervised machine learning paradigms, respectively. More specifically, we make the following contributions in developing neural models for NLP tasks: 1. Neural Relation Extraction: Firstly, we have proposed a novel recurrent neural network based architecture for table-filling in order to jointly perform entity and relation extraction within sentences. Then, we have further extended our scope of extracting relationships between entities across sentence boundaries, and presented a novel dependency-based neural network architecture. The two contributions lie in the supervised paradigm of machine learning. Moreover, we have contributed in building a robust relation extractor constrained by the lack of labeled data, where we have proposed a novel weakly-supervised bootstrapping technique. Given the contributions, we have further explored interpretability of the recurrent neural networks to explain their predictions for the relation extraction task. 2. Neural Topic Modeling: Besides the supervised neural architectures, we have also developed unsupervised neural models to learn meaningful document representations within topic modeling frameworks. Firstly, we have proposed a novel dynamic topic model that captures topics over time. Next, we have contributed in building static topic models without considering temporal dependencies, where we have presented neural topic modeling architectures that also exploit external knowledge, i.e., word embeddings to address data sparsity. Moreover, we have developed neural topic models that incorporate knowledge transfers using both the word embeddings and latent topics from many sources. Finally, we have shown improving neural topic modeling by introducing language structures (e.g., word ordering, local syntactic and semantic information, etc.) that deals with bag-of-words issues in traditional topic models. The class of proposed neural NLP models in this section are based on techniques at the intersection of PGMs, deep learning and ANNs. Here, the task of neural relation extraction employs neural networks to learn representations typically at the sentence level, without access to the broader document context. However, topic models have access to statistical information across documents. Therefore, we advantageously combine the two complementary learning paradigms in a neural composite model, consisting of a neural topic and a neural language model that enables us to jointly learn thematic structures in a document collection via the topic model, and word relations within a sentence via the language model. Overall, our research contributions in this dissertation extend NLP-based systems for relation extraction and topic modeling tasks with state-of-the-art performances

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

Cross-Domain information extraction from scientific articles for research knowledge graphs

Today’s scholarly communication is a document-centred process and as such, rather inefficient. Fundamental contents of research papers are not accessible by computers since they are only present in unstructured PDF files. Therefore, current research infrastructures are not able to assist scientists appropriately in their core research tasks. This thesis addresses this issue and proposes methods to automatically extract relevant information from scientific articles for Research Knowledge Graphs (RKGs) that represent scholarly knowledge structured and interlinked. First, this thesis conducts a requirements analysis for an Open Research Knowledge Graph (ORKG). We present literature-related use cases of researchers that should be supported by an ORKG-based system and their specific requirements for the underlying ontology and instance data. Based on this analysis, the identified use cases are categorised into two groups: The first group of use cases needs manual or semi-automatic approaches for knowledge graph (KG) construction since they require high correctness of the instance data. The second group requires high completeness and can tolerate noisy instance data. Thus, this group needs automatic approaches for KG population. This thesis focuses on the second group of use cases and provides contributions for machine learning tasks that aim to support them. To assess the relevance of a research paper, scientists usually skim through titles, abstracts, introductions, and conclusions. An organised presentation of the articles' essential information would make this process more time-efficient. The task of sequential sentence classification addresses this issue by classifying sentences in an article in categories like research problem, used methods, or obtained results. To address this problem, we propose a novel unified cross-domain multi-task deep learning approach that makes use of datasets from different scientific domains (e.g. biomedicine and computer graphics) and varying structures (e.g. datasets covering either only abstracts or full papers). Our approach outperforms the state of the art on full paper datasets significantly while being competitive for datasets consisting of abstracts. Moreover, our approach enables the categorisation of sentences in a domain-independent manner. Furthermore, we present the novel task of domain-independent information extraction to extract scientific concepts from research papers in a domain-independent manner. This task aims to support the use cases find related work and get recommended articles. For this purpose, we introduce a set of generic scientific concepts that are relevant over ten domains in Science, Technology, and Medicine (STM) and release an annotated dataset of 110 abstracts from these domains. Since the annotation of scientific text is costly, we suggest an active learning strategy based on a state-of-the-art deep learning approach. The proposed method enables us to nearly halve the amount of required training data. Then, we extend this domain-independent information extraction approach with the task of \textit{coreference resolution}. Coreference resolution aims to identify mentions that refer to the same concept or entity. Baseline results on our corpus with current state-of-the-art approaches for coreference resolution showed that current approaches perform poorly on scientific text. Therefore, we propose a sequential transfer learning approach that exploits annotated datasets from non-academic domains. Our experimental results demonstrate that our approach noticeably outperforms the state-of-the-art baselines. Additionally, we investigate the impact of coreference resolution on KG population. We demonstrate that coreference resolution has a small impact on the number of resulting concepts in the KG, but improved its quality significantly. Consequently, using our domain-independent information extraction approach, we populate an RKG from 55,485 abstracts of the ten investigated STM domains. We show that every domain mainly uses its own terminology and that the populated RKG contains useful concepts. Moreover, we propose a novel approach for the task of \textit{citation recommendation}. This task can help researchers improve the quality of their work by finding or recommending relevant related work. Our approach exploits RKGs that interlink research papers based on mentioned scientific concepts. Using our automatically populated RKG, we demonstrate that the combination of information from RKGs with existing state-of-the-art approaches is beneficial. Finally, we conclude the thesis and sketch possible directions of future work.Die Kommunikation von Forschungsergebnissen erfolgt heutzutage in Form von Dokumenten und ist aus verschiedenen Gründen ineffizient. Wesentliche Inhalte von Forschungsarbeiten sind für Computer nicht zugänglich, da sie in unstrukturierten PDF-Dateien verborgen sind. Daher können derzeitige Forschungsinfrastrukturen Forschende bei ihren Kernaufgaben nicht angemessen unterstützen. Diese Arbeit befasst sich mit dieser Problemstellung und untersucht Methoden zur automatischen Extraktion von relevanten Informationen aus Forschungspapieren für Forschungswissensgraphen (Research Knowledge Graphs). Solche Graphen sollen wissenschaftliches Wissen maschinenlesbar strukturieren und verknüpfen. Zunächst wird eine Anforderungsanalyse für einen Open Research Knowledge Graph (ORKG) durchgeführt. Wir stellen literaturbezogene Anwendungsfälle von Forschenden vor, die durch ein ORKG-basiertes System unterstützt werden sollten, und deren spezifische Anforderungen an die zugrundeliegende Ontologie und die Instanzdaten. Darauf aufbauend werden die identifizierten Anwendungsfälle in zwei Gruppen eingeteilt: Die erste Gruppe von Anwendungsfällen benötigt manuelle oder halbautomatische Ansätze für die Konstruktion eines ORKG, da sie eine hohe Korrektheit der Instanzdaten erfordern. Die zweite Gruppe benötigt eine hohe Vollständigkeit der Instanzdaten und kann fehlerhafte Daten tolerieren. Daher erfordert diese Gruppe automatische Ansätze für die Konstruktion des ORKG. Diese Arbeit fokussiert sich auf die zweite Gruppe von Anwendungsfällen und schlägt Methoden für maschinelle Aufgabenstellungen vor, die diese Anwendungsfälle unterstützen können. Um die Relevanz eines Forschungsartikels effizient beurteilen zu können, schauen sich Forschende in der Regel die Titel, Zusammenfassungen, Einleitungen und Schlussfolgerungen an. Durch eine strukturierte Darstellung von wesentlichen Informationen des Artikels könnte dieser Prozess zeitsparender gestaltet werden. Die Aufgabenstellung der sequenziellen Satzklassifikation befasst sich mit diesem Problem, indem Sätze eines Artikels in Kategorien wie Forschungsproblem, verwendete Methoden oder erzielte Ergebnisse automatisch klassifiziert werden. In dieser Arbeit wird für diese Aufgabenstellung ein neuer vereinheitlichter Multi-Task Deep-Learning-Ansatz vorgeschlagen, der Datensätze aus verschiedenen wissenschaftlichen Bereichen (z. B. Biomedizin und Computergrafik) mit unterschiedlichen Strukturen (z. B. Datensätze bestehend aus Zusammenfassungen oder vollständigen Artikeln) nutzt. Unser Ansatz übertrifft State-of-the-Art-Verfahren der Literatur auf Benchmark-Datensätzen bestehend aus vollständigen Forschungsartikeln. Außerdem ermöglicht unser Ansatz die Klassifizierung von Sätzen auf eine domänenunabhängige Weise. Darüber hinaus stellen wir die neue Aufgabenstellung domänenübergreifende Informationsextraktion vor. Hierbei werden, unabhängig vom behandelten wissenschaftlichen Fachgebiet, inhaltliche Konzepte aus Forschungspapieren extrahiert. Damit sollen die Anwendungsfälle Finden von verwandten Arbeiten und Empfehlung von Artikeln unterstützt werden. Zu diesem Zweck führen wir eine Reihe von generischen wissenschaftlichen Konzepten ein, die in zehn Bereichen der Wissenschaft, Technologie und Medizin (STM) relevant sind, und veröffentlichen einen annotierten Datensatz von 110 Zusammenfassungen aus diesen Bereichen. Da die Annotation wissenschaftlicher Texte aufwändig ist, kombinieren wir ein Active-Learning-Verfahren mit einem aktuellen Deep-Learning-Ansatz, um die notwendigen Trainingsdaten zu reduzieren. Die vorgeschlagene Methode ermöglicht es uns, die Menge der erforderlichen Trainingsdaten nahezu zu halbieren. Anschließend erweitern wir unseren domänenunabhängigen Ansatz zur Informationsextraktion um die Aufgabe der Koreferenzauflösung. Die Auflösung von Koreferenzen zielt darauf ab, Erwähnungen zu identifizieren, die sich auf dasselbe Konzept oder dieselbe Entität beziehen. Experimentelle Ergebnisse auf unserem Korpus mit aktuellen Ansätzen zur Koreferenzauflösung haben gezeigt, dass diese bei wissenschaftlichen Texten unzureichend abschneiden. Daher schlagen wir eine Transfer-Learning-Methode vor, die annotierte Datensätze aus nicht-akademischen Bereichen nutzt. Die experimentellen Ergebnisse zeigen, dass unser Ansatz deutlich besser abschneidet als die bisherigen Ansätze. Darüber hinaus untersuchen wir den Einfluss der Koreferenzauflösung auf die Erstellung von Wissensgraphen. Wir zeigen, dass diese einen geringen Einfluss auf die Anzahl der resultierenden Konzepte in dem Wissensgraphen hat, aber die Qualität des Wissensgraphen deutlich verbessert. Mithilfe unseres domänenunabhängigen Ansatzes zur Informationsextraktion haben wir aus 55.485 Zusammenfassungen der zehn untersuchten STM-Domänen einen Forschungswissensgraphen erstellt. Unsere Analyse zeigt, dass jede Domäne hauptsächlich ihre eigene Terminologie verwendet und dass der erstellte Wissensgraph nützliche Konzepte enthält. Schließlich schlagen wir einen Ansatz für die Empfehlung von passenden Referenzen vor. Damit können Forschende einfacher relevante verwandte Arbeiten finden oder passende Empfehlungen erhalten. Unser Ansatz nutzt Forschungswissensgraphen, die Forschungsarbeiten mit in ihnen erwähnten wissenschaftlichen Konzepten verknüpfen. Wir zeigen, dass aktuelle Verfahren zur Empfehlung von Referenzen von zusätzlichen Informationen aus einem automatisch erstellten Wissensgraphen profitieren. Zum Schluss wird ein Fazit gezogen und ein Ausblick für mögliche zukünftige Arbeiten gegeben