Transfer learning and sentence level features for named entity recognition on tweets

We present our system for the WNUT 2017 Named Entity Recognition challenge on Twitter data. We describe two modifications of a basic neural network architecture for sequence tagging. First, we show how we exploit additional labeled data, where the Named Entity tags differ from the target task. Then, we propose a way to incorporate sentence level features. Our system uses both methods and ranked second for entity level annotations, achieving an F1-score of 40.78, and second for surface form annotations, achieving an F1-score of 39.33

Wenn Algorithmen Zeitschriften lesen

In Zusammenarbeit mit dem Institut für Computerlinguistik der Universität Zürich (ICL UZH) lancierte die ETH-Bibliothek Zürich ein Pilotprojekt im Bereich automatisierter Textanreicherung. Grundlage für den Piloten bildeten Volltextdateien der Schweizer Zeitschriftenplattform E-Periodica. Anhand eines ausgewählten Korpus dieser OCR-Daten wurden mit automatisierten Verfahren Tests in den Bereichen OCR-Korrektur, Erkennung von Personen-, Orts- und Ländernamen sowie Verlinkung identifizierter Personen mit der Gemeinsamen Normdatei GND durchgeführt. Insgesamt wurden sehr positive Resultate erzielt. Das verwendete System dient nun als Grundlage für den weiteren Kompetenzausbau der ETH-Bibliothek auf diesem Gebiet. Das gesamte bestehende Angebot der Plattform E-Periodica soll automatisiert angereichert und um neue Funktionalitäten erweitert werden. Dies mit dem Ziel, Forschenden einen Mehrwert bei der Informationsbeschaffung zu bieten. Im vorliegenden Beitrag werden Projektinhalt, Methodik und Resultate erläutert sowie das weitere Vorgehen skizziert

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