LawORDate: a Service for Distinguishing Legal References from Temporal Expressions

Abstract

References to documents in the legal domain usually follow patterns containing temporal information in di erent forms (e.g. 'Directive 2001/29'). These references mislead algorithms detecting pure temporal references, and false positives occur in named entity recognition algorithms searching dates or intervals. This paper presents methods and techniques to identify these references, applied to two di erent domains. The rst domain is that of news, where the temporal information plays a crucial role for their understanding and automatically building timelines can be hampered by the errors induced from these legal references. The second domain is that dataset descriptions. Dataset descriptions sometimes contain temporal information, not only in their dedicated metadata elds (e.g. dataset creation) but also within the text of their description. LawORDate, the system presented in this paper, is a web service able to detect legal references with temporal information in Spanish texts. The service identi es these references, avoiding their annotation by temporal taggers and enabling a further step of linking the references to the original sources and building co-reference graphs.

Keywords

• legal references
• temporal expressions
• news
• dataset description

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Temporal expressions detection, mainly focused on news, is a emerging eld gaining more and more importance in NLP. E orts such as the NewsReader project1 and the TempEval [ 1, 2 ] initiatives in SemEval, along with subsequent more speci c temporal tasks [ 3, 4 ] show the interest in processing the temporal dimension on all kind of texts. Usually processing of temporal expressions is done regarding the concrete type of text being faced, both depending on its eld (such as news, clinical domain or historical texts) or extension (free texts or length-limited tweets). Due to this specialization, systems do not usually react well when they nd expressions from other elds, such as is the case of legal references in news or dataset description. The boom of open data portals also present this kind of mixed information. Thousands of datasets become publicly available everyday, sometimes presenting just basic scarce metadata such as title and description. Being able to extract additional information and new search parameters from them, such as named entities or temporal references, would facilitate managing them, along with linking them resources or queries.

To this end, a system2 was built to extract temporal coverage from both news and related datasets in Spanish, some of them in the legal domain, and be able to link them based in the temporal dimension. This system calls an existing temporal tagger, HeidelTime [ 5 ], able to detect temporal expressions in texts in Spanish and tag them following the TIMEX3 annotation standard. Nevertheless, this tagger happened to tag as temporal expressions references to Spanish laws and legal documents that led to false positives, such as shown in the example exposed in Fig. 1, extracted from a real article3. The result of the tagging by HeidelTime can be found in Fig. 2.

Estas actividades estan reguladas por Real Decreto 1341/2007, de 11 de octubre sobre la gestion de la calidad de las aguas de ban~o, incorporando al derecho espan~ol la Directiva 2006/7/CE del Parlamento Europeo y del Consejo de 15 de febrero de 2006 relativa a la gestion de la calidad de las aguas de ban~o. Estas actividades estan reguladas por Real Decreto <TIMEX3 tid="t2" type= "DATE" value="1341">1341</TIMEX3>/<TIMEX3 tid="t3" type="DATE" value="2007">2007</TIMEX3>, <TIMEX3 tid="t9" type="DATE" value= "201610-11">de 11 de octubre</TIMEX3> sobre la gestion de la calidad de las aguas de ban~o, incorporando al derecho espan~ol la Directiva <TIMEX3 tid="t4" type="DATE" value="2006">2006</TIMEX3>/7/CE del Parlamento Europeo y del Consejo <TIMEX3 tid="t8" type="DATE" value="2006-02-15">de 15 de febrero de 2006</TIMEX3> relativa a la gestion de la calidad de las aguas de ban~o.

We also nd this problem in the description of datasets, being specially problematic when obtaining obviously inconsistent dates such as happens in the ex2 https://github.com/mnavasloro/AportaCuando 3 http://www.castillalamancha.es/actualidad/notasdeprensa/castilla-la-manchacuenta-con-35-zonas-de-ba%C3%B1o-autorizadas-donde-disfrutar-de-la-naturaleza ample in Fig.3, extracted from the description of a real dataset4. Here the tagged dates without a legal-focused preprocessing were '2093', '2008' and '2008-1219'. While the latest can at least be used as a lower temporal bound (since there is no additional temporal information on the coverage in the description), the year 2093 is obviously inconsistent.

Base de datos que proporciona informacion sobre los Centros Tecnologicos y Centros de apoyo a la Innovacin inscritos en el registro creado mediante el Real Decreto 2093/2008, de 19 de diciembre. Permite la consulta por Modalidad, rea Tecnologica, Sector, Comunidad Autonoma y/o Provincia. Ademas, posibilita la descarga de la version completa en PDF.

The aim of the web service LawORDate5 introduced in this paper is to detect common legal expressions appearing in non-legal texts that tend to mislead temporal taggers and replace them in the text, in order to obtain a clean version of it where temporal taggers are able to detect just temporal expressions. The remainder will expose a brief state-of-the-art and an analysis on usual legal expressions with patterns similar to temporal expressions in Spanish, along with examples of regular expressions able to detect most of them (tested in a case of use on descriptions of datasets from the Spanish Open Data portal). Finally, conclusions derived from this analysis and future work on this topic will be exposed. 2

State of the Art

Processing the temporal dimension of legal text has been previously tackled in literature [6{8], and the confusion between legal and temporal references has been previously exposed [ 9 ]. Nevertheless, to the best of her knowledge, the author is not aware to any previous dedicated approach to detect legal references speci cally for ulterior temporal processing.

Identi cation of legal cross-references has been widely studied in literature [ 10 ], being targeted in di erent languages (such as French [ 11, 12 ], Dutch [ 13 ], Italian [ 14 ] or Japanese [ 15 ]) and with di erent levels of deepness. We nd for instance the approach of Adedjouma et al. [ 11 ] for the Luxembourg's Legislation (later expanded to a Canadian legal corpus [ 12 ]), where a complete schema 4 http://datos.gob.es/catalogo/e04990501-registro-de-centros-tecnologicos-y-centrosde-apoyo-a-la-innovacion-tecnologica 5 https://github.com/mnavasloro/LawORDate (with information on how to use the web service) identifying di erent parts that can be included in a reference in this context (such as Part, Book or Article), along with the di erent information in them (dates, names, headers...) are built. The authors also make a distinction between simple and complex cross reference patterns; this had been previously exposed also in [ 13 ], including also some special cases, where a grammar allowed identi cation of just in-collection legal references in documents from the Dutch Tax and Customs Administration. Finally, the work by Tran et al. [ 15 ] focused on references to sub-document targets, proposing machine-learning based approaches. Also more generic-aimed frameworks for managing legal documents, such as NORMA-system [ 16 ], include services for marking-up legal references, called by further works [ 17 ].

Di erently from the temporal aim presented in this paper, the use of this techniques for legal references identi cation go from mark-up and linking [ 16 ] to normalization [ 14 ]. Most of these approaches are based in patterns; the only work in Spanish the author is aware of also follows this pattern-based approach [ 18 ]. 3

Analysis of the problem

In the frame of news and dataset description processing, namely trying to locate them into a temporal instant or interval, several legal references happened to be tagged as temporal expressions by a state-of-the-art temporal tagger. Some examples are the following expressions, that refer to di erent o cial Spanish documents or laws: { Ley Organica 10/1995 (Organic Law). { Ley 22/2011, de 28 de julio (Law). { BOE: 29/07/2011 or BOE de 22 de julio or BOE num. 306, de 23 de diciembre (BOE: Bolet n O cial del Estado O cial State Gazette). { Real Decreto 1341/2007 (sometimes also expressed as RD 1463/2007, Royal

Decree) { Directiva 2012/27/UE.

These references are often also surrounded by a date referred to their creation (being therefore important to detect them as well). These legal expressions can also include additional words such as in 'Real Decreto Legislativo' (Legislative Royal Decree) or be combined such as in 'Real Decreto Legislativo 1/2004 de 5 de enero BOE de 8 de marzo'. Also exceptions where dates near to references to legal documents can be found, such as happens when the dataset contains information about the proper legal document, such as in the example6 depicted below, where the dates refer indeed to temporal coverage: 6 http://datos.gob.es/catalogo/l01280148-publicaciones-en-boletin-o cial-del-estadoboe-2013-2017 Publicaciones en Boletn O cial del Estado (BOE): 2013-2017. (for English: 'Publications in the O cial State Gazette (BOE): 2013-2017.' )

The problem of detecting these references is therefore not straightforward. In the following section some patterns for references found in a concrete application case (dataset descriptions and news) are introduced. 4

Hands on and rst patterns

The corpus we have worked with is a dump of metadata from the Spanish open data portal datos.gob.es7, consisting of almost 16k datasets. Some of them contained temporal coverage information expressed as dcat:temporal property8, but most of them had their upload and creation date as only temporal information, along with information on the publisher, the title and the description.

A rst analysis performed on metadata from these datasets showed that most appearances followed constrained patterns, and that texts that presented this kind of references misled the temporal tagger. Besides detecting temporal expressions that are not actually from the text timeline, another major problem derives from this misleading: temporal normalization9 is also a ected, since some dates can be wrongly normalized because of the misidenti cation of legal references nearby as temporal expressions.

Some of the used patterns for detecting these problematic legal references are the exposed in Fig. 4. (([D|d](irectiva|IRECTIVA)) (\d*)\/(\d*)\/(\w*))(,? de (\d*) de ([E|e]nero|[F|f]ebrero|[M|m] arzo|[A|a]bril|[M|m]ayo|[J|j]unio|[J|j]ulio|[A|a]gosto|[S|s]emptiembre|[O|o]ctubre|[N|n]ovie mbre|[D|d]iciembre)( de (\d\d\d\d))?)? (([R|r](eal|EAL) [D|d](ecreto|ECRETO)) (\d*)\/(\d*))(,? de (\d*) de ([E|e]nero|[F|f]ebrero|[ M|m]arzo|[A|a]bril|[M|m]ayo|[J|j]unio|[J|j]ulio|[A|a]gosto|[S|s]emptiembre|[O|o]ctubre|[N|n] oviembre|[D|d]iciembre)( de (\d\d\d\d))?)? ((([L|l]ey [O|o]rg[|a]nica)|(LEY ORG[|A]NICA)) (\d*)\/(\d*))(,? de (\d*) de ([E|e]nero|[F|f] ebrero|[M|m]arzo|[A|a]bril|[M|m]ayo|[J|j]unio|[J|j]ulio|[A|a]gosto|[S|s]emptiembre|[O|o]ctub re|[N|n]oviembre|[D|d]iciembre)( de (\d\d\d\d))?)? 7 http://datos.gob.es/ 8 https://www.w3.org/TR/vocab-dcat/#Property:dataset temporal 9 Temporal normalization can be described as "to assign the same value to all expressions carrying the same semantics or referring to the same point in time" [ 5 ]; this is, the temporal anchoring (often derived from context) for an incomplete temporal expression. An example can be how from the sentence 'The 4th of October of 1991 he came here. The 6th he left.', the date '06/10/1991' can be derived for '6th'. Once these patterns are detected, they are replaced in the text by strings containing information of the legal references detected, but in a format that does not mislead the temporal tagger. This new version of the text maintains all the original genuine temporal expressions, being therefore the ones remaining those that must be detected by the tagger. Once the text is correctly tagged, old legal references can be recovered. Beside facilitating single-use temporal processing of isolate documents, this service also allows to generate correctly temporally tagged texts with legal references that can be used for training machine-learning based temporal taggers in order to adapt them to the legal domain. 5

Conclusions and Future Work

The work presented shows how just a basic preprocessing for detecting legal expressions to prevent temporal taggers from tagging them can improve temporal tagging on all kind of legal related texts, being for instance able to solve similar cases to the examples exposed in the introduction. Also other languages or kinds of texts could bene t from this preprocessing: the work made for Spanish and general but legal related texts (news and datasets in our case) can be adopted also for other languages and kinds of texts, such as genuine legal documents. Future work include asking experts in the eld for more ways in which legal references can be written, along with increasing the amount of references detected and the languages covered by the web service.

Acknowledgments References

This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 780602.

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