text: Recognizing Musical Entities is important for Music Information Retrieval (MIR) since it can improve the performance of several tasks such as music recommendation, genre classification or artist similarity. However, most entity recognition systems in the music domain have concentrated on formal texts (e.g. artists' biographies, encyclopedic articles, etc.), ignoring rich and noisy user-generated content. In this work, we present a novel method to recognize musical entities in Twitter content generated by users following a classical music radio channel. Our approach takes advantage of both formal radio schedule and users' tweets to improve entity recognition. We instantiate several machine learning algorithms to perform entity recognition combining task-specific and corpus-based features. We also show how to improve recognition results by jointly considering formal and user-generated content
  @cite_18: Comunicacio presentada a la 18th International Society for Music Information Retrieval Conference (ISMIR 2017), celebrada els dies 23 a 27 d'octubre de 2017 a Suzhou, Xina.
  @cite_1: Several recent software systems have been designed to obtain novel annotation of cross-referencing text fragments and Wikipedia pages. Tagme is state of the art in this setting and can accurately manage short textual fragments (such as snippets of search engine results, tweets, news, or blogs) on the fly.
  @cite_19: Paper presented at 10th International Conference on Language Resources and Evaluation LREC 2016; 2016 May 23-28; Portoroz, Slovenia.
  @cite_16: Interlinking text documents with Linked Open Data enables the Web of Data to be used as background knowledge within document-oriented applications such as search and faceted browsing. As a step towards interconnecting the Web of Documents with the Web of Data, we developed DBpedia Spotlight, a system for automatically annotating text documents with DBpedia URIs. DBpedia Spotlight allows users to configure the annotations to their specific needs through the DBpedia Ontology and quality measures such as prominence, topical pertinence, contextual ambiguity and disambiguation confidence. We compare our approach with the state of the art in disambiguation, and evaluate our results in light of three baselines and six publicly available annotation systems, demonstrating the competitiveness of our system. DBpedia Spotlight is shared as open source and deployed as a Web Service freely available for public use.
  @cite_13: The amount of music information available on the Web is rapidly increasing. There is a pressing need for music information extraction. To extract useful information from natural language text, we must recognize music named entities first. This paper introduces a hybrid method to identify the Chinese named entities in music domain. Recently, machine learning approaches are frequently used to solve Name Entity Recognition (NER). So our method uses a Hidden Markov Model (HMM) as the underlying method. Since HMM has innate weaknesses, we incorporate it with rule-based method for pre-processing and post-processing. The combination of machine learning method and rule-based method results in a high precision recognition. And we improve both training and recognizing process of HMM for Music Named Entity Recognition (MNER). In this paper, a novel and convenient Musical Name Entity (MNE) tagging method to generate training data is proposed, which makes HMM method practically usable. In addition, we present an effective method of unknown words tagging in recognition. The experimental results show that our framework brings significant improvements for solving MNER.
  @cite_11: Entity Linking (EL) and Word Sense Disambiguation (WSD) both address the lexical ambiguity of language. But while the two tasks are pretty similar, they differ in a fundamental respect: in EL the textual mention can be linked to a named entity which may or may not contain the exact mention, while in WSD there is a perfect match between the word form (better, its lemma) and a suitable word sense. In this paper we present Babelfy, a unified graph-based approach to EL and WSD based on a loose identification of candidate meanings coupled with a densest subgraph heuristic which selects high-coherence semantic interpretations. Our experiments show state-ofthe-art performances on both tasks on 6 different datasets, including a multilingual setting. Babelfy is online at http: babelfy.org
summary: In the field of IE, a first approach for detecting musical named entities from raw text, based on Hidden Markov Models, has been proposed in @cite_13 . @cite_19 , the authors combine state-of-the-art Entity Linking (EL) systems to tackle the problem of detecting musical entities from raw texts. The method proposed relies on the intuition, so if two or more different EL systems perform the same prediction in linking a named entity mention, the more likely this prediction is to be correct. In detail, the off-the-shelf systems used are: DBpedia Spotlight @cite_16 , TagMe @cite_1 , Babelfy @cite_11 . Moreover, a first Musical Entity Linking, MEL http: mel.mtg.upf.edu has been presented in @cite_18 which combines different state-of-the-art NLP libraries and SimpleBrainz, an RDF knowledge base created from MusicBrainz https: musicbrainz.org after a simplification process.