In text analysis, authorship attribution occurs in a variety of ways. The field of computational linguistics becomes more important as the need of authorship attribution and text analysis becomes more widespread. For this research, pre-existing authorship attribution software, Java Graphical Authorship Attribution Program (JGAAP), implements a named entity recognizer, specifically the Stanford Named Entity Recognizer, to probe into similar genre text and to aid in extricating the correct author. This research specifically examines the words authors use around named entities in order to test the ability of these words at attributing authorship / McAnulty College and Graduate School of Liberal Arts; / Computational Mathematics / MS; / Thesis;

The information overload problem becomes imperative with the explosion of information, and people need some agents to facilitate them to filter the information to meet their personal need. In this work, we conduct a research for the article classification in the tourism domain so as to identify articles that meet users¡¦ information need. We propose an information need orientation model in tourism, which consists of four goals: Initiation, Attraction, Accommodation, and Route planning. These goals can be characterized by 13 features. Some of the identified features can be enhanced by WordNet and Named Entity Recognition techniques as supplement techniques. To test the effectiveness of using the 13 features for classification and the relevant methods, we collected 15,797 articles from TripAdvisor.com, the world's largest travel site, and randomly selected 600 articles as training data labeled by two labelers. The experimental results show that our approach generally has comparable or better performance than that of using purely lexical features, namely TF-IDF, for classification, with fewer features.

Large digital libraries typically contain large collections of heterogeneous resources intended to be delivered to a variety of user communities. One key challenge for these libraries is providing tight integration between resources both within a single collection and across the several collections of the library with out requiring hand coding. One key tool in doing this is elucidating the internal structure of the digital resources and using that structure to form connections between the resources. The heterogeneous nature of the collections and the diversity of the needs in the user communities complicates this task. Accordingly, in this thesis, I describe an approach to implementing a feature identification system to support digital collections that provides a general framework for applications while allowing decisions about the details of document representation and features identification to be deferred to domain specific implementations of that framework. These deferred decisions include details of the semantics and syntax of markup, the types of metadata to be attached to documents, the types of features to be identified, the feature identification algorithms to be applied, and which features should be indexed. This approach results in strong support for the general aspects of developing a feature identification system allowing future work to focus on the details of applying that system to the specific needs of individual collections and user communities.

Atualmente, é comum que usuários levem em consideração a localização geográfica dos documentos, é dizer considerar o escopo geográfico que está sendo tratado no contexto do documento, nos processos de Recuperação de Informação. No entanto, os sistemas convencionais de extração de informação que estão baseados em palavras-chave não consideram que as palavras podem representar entidades geográficas espacialmente relacionadas com outras entidades nos documentos. Para resolver esse problema, é necessário viabilizar o georreferenciamento dos textos, ou seja, identificar as entidades geográficas presentes e associá-las com sua correta localização espacial. A identificação e desambiguação das entidades geográficas apresenta desafios importantes, principalmente do ponto de vista linguístico, já que um topônimo, pode possuir variados tipos de ambiguidade associados. Esse problema de ambiguidade causa ruido nos processos de recuperação de informação, já que o mesmo termo pode ter informação relevante ou irrelevante associada. Assim, a principal estratégia para superar os problemas de ambiguidade, compreende a identificação de evidências que auxiliem na identificação e desambiguação das localidades nos textos. O presente trabalho propõe uma metodologia que permite identificar e determinar a cobertura espacial dos documentos, denominada SpatialCIM. A metodologia SpatialCIM tem o objetivo de organizar os processos de resolução de topônimos. Assim, o principal objetivo deste trabalho é avaliar e selecionar técnicas de desambiguação que permitam resolver a ambiguidade dos topônimos nos textos. Para isso, foram propostas e desenvolvidas as abordagens de (1)Desambiguação por Pontos e a (2)Desambiguação Textual e Estrutural. Essas abordagens, exploram duas técnicas diferentes de desambiguação de topônimos, as quais, geram e desambiguam os caminhos geográficos associados aos topônimos reconhecidos para cada documento. Assim, a hipótese desta pesquisa é que o uso das técnicas de desambiguação de topônimos viabilizam uma melhor localização espacial dos documentos. A partir dos resultados obtidos neste trabalho, foi possível demonstrar que as técnicas de desambiguação melhoram a precisão e revocação na classificação espacial dos documentos. Demonstrou-se também o impacto positivo do uso de uma ferramenta linguística no processo de reconhecimento das entidades geográficas. Assim, foi demostrada a utilidade dos processos de desambiguação para a obtenção da cobertura espacial dos documentos / Currently, it is usual that users take into account the geographical localization of the documents in the Information Retrieval process. However, the conventional information retrieval systems based on key-word matching do not consider which words can represent geographical entities that are spatially related to other entities in the documents. To solve this problem, it is necessary to enable the geo-referencing of texts by identifying the geographical entities present in text and associate them with their correct spatial location. The identification and disambiguation of the geographical entities present major challenges mainly from the linguistic point of view, since one location can have different types of associated ambiguity. The ambiguity problem causes noise in the process of information retrieval, since the same term may have relevant or irrelevant information associated. Thus, the main strategy to overcome these problems, include the identification of evidence to assist in the identification and disambiguation of locations in the texts. This study proposes a methodology that allows the identification and spatial localization of the documents, denominated SpatialCIM. The SpatialCIM methodology has the objective to organize the Topônym Resolution process. Therefore the main objective of this study is to evaluate and select disambiguation techniques that allow solving the toponym ambiguity in texts. Therefore, we proposed and developed the approaches of (1) Disambiguation for Points and (2) Textual and Structural Disambiguation. These approaches exploit two different techniques of toponym disambiguation, which generate and desambiguate the associated paths with the recognized geographical toponym for each document. Therefore the hypothesis is, that the use of the toponyms disambiguation techniques enable a better spatial localization of documents. From the results it was possible to demonstrate that the disambiguation techniques improve the precision and recall for the spatial classification of documents. The positive effect of using a linguistic tool for the process of geographical entities recognition was also demonstrated. Thus, it was proved the usefulness of the disambiguation process for obtaining a spatial coverage of the document

Background: Traditionally text mention normalization corpora have normalized concepts to single ontology identifiers ("pre-coordinated concepts"). Less frequently, normalization corpora have used concepts with multiple identifiers ("post-coordinated concepts") but the additional identifiers have been restricted to a defined set of relationships to the core concept. This approach limits the ability of the normalization process to express semantic meaning. We generated a freely available corpus using post-coordinated concepts without a defined set of relationships that we term "compositional concepts" to evaluate their use in clinical text. Methods: We annotated 5397 disorder mentions from the ShARe corpus to SNOMED CT that were previously normalized as "CUI-less" in the "SemEval-2015 Task 14" shared task because they lacked a pre-coordinated mapping. Unlike the previous normalization method, we do not restrict concept mappings to a particular set of the Unified Medical Language System (UMLS) semantic types and allow normalization to occur to multiple UMLS Concept Unique Identifiers (CUIs). We computed annotator agreement and assessed semantic coverage with this method. Results: We generated the largest clinical text normalization corpus to date with mappings to multiple identifiers and made it freely available. All but 8 of the 5397 disorder mentions were normalized using this methodology. Annotator agreement ranged from 52.4% using the strictest metric (exact matching) to 78.2% using a hierarchical agreement that measures the overlap of shared ancestral nodes. Conclusion: Our results provide evidence that compositional concepts can increase semantic coverage in clinical text. To our knowledge we provide the first freely available corpus of compositional concept annotation in clinical text.

The goal of this master thesis is to design and implement a named entity recognition and linking algorithm. A part of this goal is to propose and create a knowledge base that will be used in the algorithm. Because of the limited amount of data for languages other than English, we want to be able to train our method on one language, and then transfer the learned parameters to other languages (that do not have enough training data). The thesis consists of description of available knowledge bases, existing methods and design and implementation of our own knowledge base and entity linking method. Our method achieves state of the art result on a few variants of the AIDA CoNLL-YAGO dataset. The method also obtains comparable results on a sample of Czech annotated data from the PDT dataset using the parameters trained on the English CoNLL dataset. Powered by TCPDF (www.tcpdf.org)

With the constantly growing amount of biomedical literature, methods for automatically distilling information from unstructured data, collectively known as information extraction, have become indispensable. Whilst most biomedical information extraction efforts in the last decade have focussed on the identification of gene products and interactions between them, the biomedical text mining community has recently extended their scope to capture associations between biomedical and chemical entities with the aim of supporting applications in drug discovery. This thesis is the first comprehensive study focussing on information extraction from pharmaceutical chemistry literature. In this research, we describe our work on (1) recognising names of chemical compounds and drugs, facilitated by the incorporation of domain knowledge; (2) exploring different coreference resolution paradigms in order to recognise co-referring expressions given a full-text article; and (3) defining drug-target interactions as events and distilling them from pharmaceutical chemistry literature using event extraction methods.

Cieľom tejto práce je preskúmať dostupné systémy pre extrakciu informácií a možnosti ich použitia v projekte MedIEQ. Teoretickú časť obsahuje úvod do oblasti extrakcie informácií. Popisujem účel, potreby a použitie a vzťah k iným úlohám spracovania prirodzeného jazyka. Prechádzam históriou, nedávnym vývojom, meraním výkonnosti a jeho kritikou. Taktiež popisujem všeobecnú architektúru IE systému a základné úlohy, ktoré má riešiť, s dôrazom na extrakciu entít. V praktickej časti sa nacházda prehľad algoritmov používaných v systémoch pre extrakciu informácií. Opisujem oba typy algoritmov ? pravidlové aj štatistické. V ďalšej kapitole je zoznam a krátky popis existujúcich voľných systémov. Nakoniec robím vlastný experiment s dvomi systémami ? LingPipe a GATE na vybraných korpusoch. Meriam rôzne výkonnostné štatistiky. Taktiež som vytvoril malý slovník a regulárny výraz pre email aby som demonštroval taktiež pravidlá pre extrahovanie určitých špecifických informácií.

Thesis Title: Named Entity Recognition in the Biomedical Domain Named entity recognition (NER) is the task of information extraction that attempts to recognize and extract particular entities in a text. One of the issues that stems from NER is that its models are domain specific. The goal of the thesis is to focus on entities strictly from the biomedical domain. The other issue with NER comes the synonymous terms that may be linked to one entity, moreover they lead to issue of disambiguation of the entities. Due to the popularity of neural networks and their success in NLP tasks, the work should use a neural network architecture for the task of named entity disambiguation, which is described in the paper by Eshel et al [1]. One of the subtasks of the thesis is to map the words and entities to a vector space using word embeddings, which attempts to provide textual context similarity, and coherence [2]. The main output of the thesis will be a model that attempts to disambiguate entities of the biomedical domain, using scientific journals (PubMed and Embase) as the documents of our interest.

An der Verarbeitung natürlicher Sprache durch computerbasierte Systeme wurde immer aktiv entwickelt und geforscht, um Aufgaben in den am weitesten verbreiteten Sprachen zu lösen. In dieser Arbeit werden verschiedene Ansätze zur Lösung von Problemen in diesem Bereich mittels künstlicher neuronaler Netze beschrieben. Dabei konzentriert sich diese Arbeit hauptsächlich auf modernere Architekturen wie Transformatoren oder BERT. Ziel dabei ist es, diese besser zu verstehen und herauszufinden, welche Vorteile sie gegenüber herkömmlichen künstlichen neuronalen Netzwerken haben. Anschließend wird dieses erlangte Wissen an einer Aufgabe aus dem Bereich der Verarbeitung natürlicher Sprache getestet, in welcher mittels einer sogenannten Named Entity Recognition (NER) spezielle Informationen aus Texten extrahiert werden.:1 Einleitung 1.1 Verarbeitung natürlicher Sprache (NLP) 1.2 Neuronale Netze 1.2.1 Biologischer Hintergrund 1.3 Aufbau der Arbeit 2 Grundlagen 2.1 Künstliche neuronale Netze 2.1.1 Arten des Lernens 2.1.2 Aktivierungsfunktionen 2.1.3 Verlustfunktionen 2.1.4 Optimierer 2.1.5 Über- und Unteranpassung 2.1.6 Explodierender und verschwindender Gradient 2.1.7 Optimierungsverfahren 3 Netzwerkarchitekturen zur Verarbeitung natürlicher Sprache 3.1 Rekurrente neuronale Netze (RNN) 3.1.1 Langes Kurzzeitgedächtnis (LSTM) 3.2 Autoencoder 3.3 Transformator 3.3.1 Worteinbettungen 3.3.2 Positionscodierung 3.3.3 Encoderblock 3.3.4 Decoderblock 3.3.5 Grenzen Transformatorarchitektur 3.4 Bidirektionale Encoder-Darstellungen von Transformatoren (BERT) 3.4.1 Vortraining 3.4.2 Feinabstimmung 4 Praktischer Teil und Ergebnisse 4.1 Aufgabe 4.2 Verwendete Bibliotheken, Programmiersprachen und Software 4.2.1 Python 4.2.2 NumPy 4.2.3 pandas 4.2.4 scikit-learn 4.2.5 Tensorflow 4.2.6 Keras 4.2.7 ktrain 4.2.8 Data Version Control (dvc) 4.2.9 FastAPI 4.2.10 Docker 4.2.11 Amazon Web Services 4.3 Daten 4.4 Netzwerkarchitektur 4.5 Training 4.6 Auswertung 4.7 Implementierung 5 Schlussbemerkungen 5.1 Zusammenfassung und Ausblick