Question: What does the dataset of legal documents include?
Evidence: Just like any other field, the legal domain is facing multiple challenges in the era of digitisation. Document collections are growing at an enormous pace and their complete and deep analysis can only be tackled with the help of assisting technologies. This is where content curation technologies based on text analytics come in rehm2016j. Such domain-specific semantic technologies enable the fast and efficient automated processing of heterogeneous document collections, extracting important information units and metadata such as, among others, named entities, numeric expressions, concepts and topics, time expressions, and text structure. One of the fundamental processing tasks is the identification and categorisation of named entities (Named Entity Recognition, NER). Typically, NER is focused upon the identification of semantic categories such as person, location and organization but, especially in domain-specific applications, other typologies have been developed that correspond to task-, language- or domain-specific needs. With regard to the legal domain, the lack of freely available datasets has been a stumbling block for text analytics research. German newspaper datasets from CoNNL 2003 BIBREF0 or GermEval 2014 BIBREF1 are simply not suitable in terms of domain, text type or semantic categories covered.
The work described in this paper was carried out under the umbrella of the project Lynx: Building the Legal Knowledge Graph for Smart Compliance Services in Multilingual Europe, a three-year EU-funded project that started in December 2017 BIBREF2. Its objective is the creation of a legal knowledge graph that contains different types of legal and regulatory data BIBREF3, BIBREF4, BIBREF5. Lynx aims to help European companies, especially SMEs, that want to become active in new European countries and markets. The project offers compliance-related services that are currently tested and validated in three use cases (UC): (i) UC1 aims to analyse contracts, enriching them with domain-specific semantic information (document structure, entities, temporal expressions, claims, summaries, etc.); (ii) UC2 focuses on compliance services related to geothermal energy operations, where Lynx supports the understanding of regulatory regimes, including norms and standards; (iii) UC3 is a compliance solution in the domain of labour law, where legal provisions, case law, and expert literature are interlinked, analysed, and compared to define legal strategies for legal practice. The Lynx services are developed for several European languages including English, Spanish, and – relevant for this paper – German BIBREF6.
Documents in the legal domain contain multiple references to named entities, especially domain-specific named entities, i. e., jurisdictions, legal institutions, etc. Legal documents are unique and differ greatly from newspaper texts. On the one hand, the occurrence of general-domain named entities is relatively rare. On the other hand, in concrete applications, crucial domain-specific entities need to be identified in a reliable way, such as designations of legal norms and references to other legal documents (laws, ordinances, regulations, decisions, etc.). However, most NER solutions operate in the general or news domain, which makes them inapplicable to the analysis of legal documents BIBREF7, BIBREF8. Accordingly, there is a great need for an NER-annotated dataset consisting of legal documents, including the corresponding development of a typology of semantic concepts and uniform annotation guidelines. In this paper, we describe the development of a dataset of legal documents, which includes (i) named entities and (ii) temporal expressions.
The remainder of this article is structured as follows. First, Section SECREF3 gives a brief overview of related work. Section SECREF4 describes, in detail, the rationale behind the annotation of the dataset including the different semantic classes annotated. Section SECREF5 describes several characteristics of the dataset, followed by a short evaluation (Section SECREF6) and conclusions as well as future work (Section SECREF7).
In this paper, we describe the devel - opment of a dataset of legal documents, which includes (i) named entities and (ii) temporal expressions.
Answer: (i) named entities and (ii) temporal expressions.