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Norne: Annotating Named Entities for Norwegian

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Norne: Annotating Named Entities for Norwegian Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020), pages 4547–4556 Marseille, 11–16 May 2020 c European Language Resources Association (ELRA), licensed under CC-BY-NC NorNE: Annotating Named Entities for Norwegian Fredrik Jørgensen,† Tobias Aasmoe,‡ Anne-Stine Ruud Husevåg,} Lilja Øvrelid,‡ Erik Velldal‡ Schibsted Media Group,† Oslo Metropolitan University,} University of Oslo‡ [email protected],† [email protected],} {tobiaaa,liljao,erikve}@ifi.uio.no‡ Abstract This paper presents NorNE, a manually annotated corpus of named entities which extends the annotation of the existing Norwegian Dependency Treebank. Comprising both of the official standards of written Norwegian (Bokmål and Nynorsk), the corpus contains around 600,000 tokens and annotates a rich set of entity types including persons, organizations, locations, geo-political entities, products, and events, in addition to a class corresponding to nominals derived from names. We here present details on the annota- tion effort, guidelines, inter-annotator agreement and an experimental analysis of the corpus using a neural sequence labeling architecture. Keywords: Named Entity Recognition, corpus, annotation, neural sequence labeling 1. Introduction In addition to discussing the annotation process and guide- This paper documents the efforts of creating the first pub- lines, we also provide an exploratory analysis of the re- licly available dataset for named entity recognition (NER) sulting dataset through a series of experiments using state- for Norwegian, dubbed NorNE.1 The dataset adds named of-the-art neural architectures for named entity recognition entity annotations on top of the Norwegian Dependency (combining a character-level CNN and a word-level BiL- Treebank (NDT) (Solberg et al., 2014), containing manu- STM, feeding into a CRF inference layer). The purpose ally annotated syntactic and morphological information for of the experimental section is to provide some preliminary both varieties of written Norwegian – Bokmål and Nynorsk baseline results while simultaneously validating the con- – comprising roughly 300,000 tokens of each. The corpus sistency and usability of the annotations, and finally also contains mostly news texts (around 85% of the corpus), but shedding some light on the consequences of different de- also other types of texts, such as government reports, parlia- sign choices that can be made in the modeling stage, like ment transcripts and blogs. The treebank has following its the choice of which label set to use or which label encod- release also been converted to the Universal Dependencies ing to use and so on. standard (Øvrelid and Hohle, 2016; Velldal et al., 2017). The remainder of the paper is organized as follows. First, We correspondingly distribute the annotations of NorNE in Section 2 briefly outlines previous work on NER for Nor- two versions, mirroring both the original NDT and the UD- wegian. Section 3 then describes the NorNE annotation ef- version. fort in more detail, briefly outlining the annotation guide- The annotations in NorNE include a rich set of entity types. lines while providing illustrating examples and presenting In short, they comprise the following (more details are an analysis of inter-annotator agreement. In Section 4 we given in the guideline discussion in Section 3): summarize the resulting dataset before we in Section 5 turn to an empirically-driven analysis of the data through a se- ◦ Person (PER): Named real or fictional characters. ries of experiments. ◦ Organization (ORG): Any collection of people, such as firms, music groups, political parties etc. 2. Previous Work on NER for Norwegian While NorNE is the first publicly available dataset for NER ◦ Location (LOC): Geographical places and facilities. in Norwegian, there have been some previous efforts to cre- ◦ Geo-political entity (GPE): Geographical regions de- ate similar resources. fined by political and/or social groups, additionally Most notably, in a parallel effort, and as part of his doctoral sub-categorized as either: work, Johansen (2019) added named entity annotations to the same underlying corpus, the UD-version of NDT. How- • GPE with a locative sense (GPE_LOC), ever, the data was single-annotated and using a reduced • GPE with an organization sense (GPE_ORG). label-set with only 4 different categories – locations, or- ◦ Product (PROD): Artificially produced entities, in- ganizations, persons, and miscellaneous – and only consid- cluding abstract entities such as radio shows, program- ering text spans already tagged as PROPN (proper noun) in ming languages, ideas, etc. the original treebank data. Previously, the Nomen Nescio project (Johannessen et ◦ Event (EVT): Festivals, weather phenomena, etc. al., 2005), focusing on Scandinavian languages, created a named entity annotated corpus for Norwegian. How- ◦ Derived (DRV): Nominals that are derived from a ever, due to copyright restrictions the data was unfortu- name, but not a named entity in themselves. nately not made publicly available. The Nomen Nescio 1https://github.com/ltgoslo/norne/ corpus was based on articles from several news papers and 4547 magazines, in addition to some works of fiction. It to- main principles behind their annotation. We will comment taled 226,984 tokens, of which 7,590 were part of a named on how various aspects of the annotation relates to other entity annotation, and the following six entity categories well-known datasets, but also discuss entity properties that were used: person, organization, location, event, work of are specific to Norwegian. art, and miscellaneous. For a while, Norwegian named en- Person (PER) The person name category includes names tity recognition based on these categories was supported in of real people and fictional characters. Names of other ani- the Oslo–Bergen tagger (Johannessen et al., 2012), a rule- mate beings such as animals are also annotated as PER, e.g., based morpho-syntactic tagger and lemmatizer for Norwe- Lassie in den kjente TV-hunden Lassie ‘the famous TV-dog gian based on constraint grammar, but unfortunately NER Lassie’. Family names should be annotated as PER even is no longer supported in the tagger. For more information though they refer to several people. on the Nomen Nescio data, including experimental results, Organization (ORG) This entity category includes any see Nøklestad (2009). named group of people, such as firms, institutions, organi- The TORCH (Transforming the Organization and Retrieval zations, pop groups, political parties etc. ORG also includes of Cultural Heritage) project (Hoff and Preminger, 2015; names of places when they act as administrative entities, as Tallerås et al., 2014) focused on automatic metadata gen- in (1) below which annotates sport teams associated with eration to improve access to digitized cultural expressions. a location. Corporate designators like AS, Co. and Ltd. As part of this effort, a corpus of subtitles and metadata should always be included as part of the named entity, as in descriptions from the archive of NRK (the national pub- (2). lic broadcaster), was annotated with named entities. To fa- cilitate comparison to previous work on Norwegian NER, (1) VålerengaORG tapte mot TromsøORG. the project adopted the categories used in Nomen Nescio, Vålerenga lost against Tromsø also extending the original annotation guidelines (Jónsdot- ‘Vålerenga lost against Tromsø’ tír, 2003), as documented in (Howard, 2014). However, as some of the data contain confidential information and (2) Advokatfirmaet Lie & CoORG representerer was only made available to the researchers on the basis of Lawyers Lie & Co represent a non-disclosure agreement, the resulting dataset could un- HansenPER. fortunately not be made publicly available. Hansen ‘The lawyers Lie & Co represent Hansen’ 3. Annotation LOC In this section we discuss in more detail various aspects Location ( ) This entity category denotes geograph- related to the annotations; we first describe some relevant ical places, buildings and various facilities. Examples properties of named entities in Norwegian, and go on to are airports, churches, restaurants, hotels, hospitals, shops, flesh out the entity types in more detail, we highlight impor- street addresses, roads, oceans, fjords, mountains and tant parts of the guidelines, discuss issues that are particu- parks. Postal addresses are not annotated, but the build- lar to mentions of named entities in Norwegian and finally ing, town, county and country within the address are to be present an analysis of inter-annotator agreement. annotated, all as LOC entities, see (3). Øvregaten 2a , 5003 Bergen 3.1. Proper Nouns in Norwegian (3) LOC LOC Øvre-street 2a, 5003 Bergen In Norwegian, proper nouns are generally capitalized. For ‘Øvre-street 2a, 5003 Bergen’ multi-token names, however, the general rule is that only the first token should be capitalized (Faarlund et al., 1997), Geo-political entity (GPE) Similarly to OntoNotes e.g. Oslo rådhus ‘Oslo city hall’. There are a number of (Weischede et al., 2013), but also the Groningen Meaning exceptions to this general rule as well, for instance in com- Bank (GMB) (Bos et al., 2017), we annotate geo-political pany names, e.g. Den Norske Bank ‘The Norwegian Bank’. entities (GPEs). This category was introduced in the an- Proper nouns may also be nested to form a larger unit, e.g. notation scheme of the Automatic Content Extraction pro- Universitetet i Oslo ‘University of Oslo’. Unlike in English, gram (ACE) (Mitchell et al.,
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