Abstract. Coreference Resolution is the process of identifying all words and phrases in a text that refer to the same entity. It has proven to be a useful intermediary step for a number of natural language processing applications. In this paper, we describe three implementations for performing coreference resolution: rule-based, statistical, and projectionbased (from English to German). After a comparative evaluation on benchmark datasets, we conclude with an application of these systems on German and English texts from different scenarios in digital curation such as an archive of personal letters, excerpts from a museum exhibition, and regional news articles.
Fine-grained entity typing is important to tasks like relation extraction and knowledge base construction. We find however, that finegrained entity typing systems perform poorly on general entities (e.g. "ex-president") as compared to named entities (e.g. "Barack Obama"). This is due to a lack of general entities in existing training data sets. We show that this problem can be mitigated by automatically generating training data from WordNets. We use a German WordNet equivalent, Ger-maNet, to automatically generate training data for German general entity typing. We use this data to supplement named entity data to train a neural fine-grained entity typing system. This leads to a 10% improvement in accuracy of the prediction of level 1 FIGER types for German general entities, while decreasing named entity type prediction accuracy by only 1%.
There are many factors that influence political elections, among them, money may be the most important one. The starting point of this Article is the judgment of the U.S. Supreme Court in Citizens United v. Federal Election Commission. After this decision is described, the approaches of the United States and Germany in regulating political speech by campaign finance laws will be discussed, focusing on the role of companies. This Article will outline the status quo of federal American campaign finance laws (Part B). Regarding the German approach, this Article will outline the Parteiengesetz (Political Parties Act, hereinafter: Part G) and decisions of the Bundesverfassungsgericht (Constitutional Court, hereinafter: BVerfG) (Part C). European regulations are not the subject of this Article. Both approaches will be compared and future prospects will be given as a conclusion (Part D).
The training of NLP models often requires large amounts of labelled training data, which makes it difficult to expand existing models to new languages. While zero-shot cross-lingual transfer relies on multilingual word embeddings to apply a model trained on one language to another, Yarowsky and Ngai (2001) propose the method of annotation projection to generate training data without manual annotation. This method was successfully used for the tasks of named entity recognition and coarse-grained entity typing, but we show that it is outperformed by zero-shot cross-lingual transfer when applied to the similar task of fine-grained entity typing. In our study of fine-grained entity typing with the FIGER type ontology for German, we show that annotation projection amplifies the English model's tendency to underpredict level 2 labels and is beaten by zero-shot cross-lingual transfer on three novel test sets.
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