The detection of music entities such as songs or performing artists in natural language queries is an important task when designing conversational music recommendation agents. Previous research has observed the applicability of named entity recognition approaches for this task based on pre-trained encoders like BERT. In recent years, large language models (LLMs) have surpassed these encoders in a variety of downstream tasks. In this paper, we validate the use of LLMs for information extraction of music entities in conversational queries by few-shot prompting. We test different numbers of examples and compare two sampling methods to obtain few-shot examples. Our results indicate that LLM performance can achieve state-of-the-art performance in the task.

YouTube is a rich source of cover songs. Since the platform itself is organized in terms of videos rather than songs, the retrieval of covers is not trivial. The field of cover song identification addresses this problem and provides approaches that usually rely on audio content. However, including the user-generated video metadata available on YouTube promises improved identification results. In this paper, we propose a multi-modal approach for cover song identification on online video platforms. We combine the entity resolution models with audio-based approaches using a ranking model. Our findings implicate that leveraging user-generated metadata can stabilize cover song identification performance on YouTube.

In this paper, we present approaches for the automated extraction and disambiguation of a part of the stylistic device Vossian Antonomasia (VA), namely the target entity that is described by the expression. We model the problem as a coreference resolution task and a question answering task and also combine both tasks. To tackle these tasks, we utilize state-of-the-art models in these areas. In addition, we visualize the connection between the source and target entities of VA in a web demo to get a deeper understanding of the interaction of entities used in VA expressions.

We present an approach that leverages expert knowledge contained in scholarly works to automatically identify key passages in literary works. Specifically, we extend a text reuse detection method for finding quotations, such that our system Lotte can deal with common properties of quotations, for example, ellipses or inaccurate quotations. An evaluation shows that Lotte outperforms four existing approaches. To generate key passages, we combine overlapping quotations from multiple scholarly texts. An interactive website, called Annette, for visualizing and exploring key passages makes the results accessible and explorable.

Attributing a particular property to a person by naming another person, who is typically wellknown for the respective property, is called a Vossian Antonomasia (VA). This subtpye of metonymy, which overlaps with metaphor, has a specific syntax and is especially frequent in journalistic texts. While identifying Vossian Antonomasia is of particular interest in the study of stylistics, it is also a source of errors in relation and fact extraction as an explicitly mentioned entity occurs only metaphorically and should not be associated with respective contexts. Despite rather simple syntactic variations, the automatic extraction of VA was never addressed as yet since it requires a deeper semantic understanding of mentioned entities and underlying relations. In this paper, we propose a first method for the extraction of VAs that works completely automatically. Our approaches use named entity recognition, distant supervision based on Wikidata, and a bi-directional LSTM for postprocessing. The evaluation on 1.8 million articles of the New York Times corpus shows that our approach significantly outperforms the only existing semi-automatic approach for VA identification by more than 30 percentage points in precision.