chunk_id stringlengths 3 7 | chunk stringlengths 1 823 | source_url stringclasses 416
values | title stringclasses 416
values | chunk_idx int64 0 294 | chunk_start_char int64 0 139k | chunk_end_char int64 303 139k |
|---|---|---|---|---|---|---|
9_4 | The dataset, which is available under a CC-BY 4.0 license in the CoNNL-2002 format, was developed for training an NER service for German legal documents in the EU project Lynx.
Named Entity Recognition, NER, Legal Documents, Legal Domain, Corpus Creation, Corpus Annotation
Introduction and Motivation
Just like any ... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 4 | 1,599 | 2,157 |
9_5 | 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 ... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 5 | 2,157 | 2,680 |
9_6 | 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 avai... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 6 | 2,680 | 3,231 |
9_7 |
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... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 7 | 3,231 | 3,744 |
9_8 | 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 servi... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 8 | 3,744 | 4,407 |
9_9 | 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 docume... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 9 | 4,407 | 4,867 |
9_10 | 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 m... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 10 | 4,867 | 5,467 |
9_11 | 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... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 11 | 5,467 | 6,042 |
9_12 |
Related Work
Until now, NER has not received a lot of attention in the legal domain, developed approaches are fragmented and inconsistent with regard to their respective methods, datasets and typologies used. Among the related work, there is no agreement regarding the selection of relevant semantic categories from t... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 12 | 6,042 | 6,575 |
9_13 |
dozier2010named describe five classes for which taggers are developed based on dictionary lookup, pattern-based rules, and statistical models. These are jurisdiction (a geographic area with legal authority), court, title (of a document), doctype (category of a document), and judge. The taggers were tested with docume... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 13 | 6,575 | 7,078 |
9_14 | On the NERC level, entities were divided in abstraction, act, document, organization, person, and non-entity. With regard to LKIF, company, corporation, contract, statute etc. are used. Unfortunately, the authors do not provide any details regarding the questions how the entities were categorised or if there is any co... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 14 | 7,078 | 7,670 |
9_15 | References are identified based on the rules described by landthaler2016unveiling. The authors created an evaluation dataset of 20 court decisions.
Annotation of the Dataset
In the following, we describe the rationale behind the annotation of the dataset including the definition of the various semantic classes and t... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 15 | 7,670 | 8,235 |
9_16 | The examples (UNKREF6–UNKREF8) show corresponding sentences that contain the named mention `John', the nominal mention `the boy' and the pronominal mention `he'. This distinction between names on the one hand and pronominal or nominal mentions on the other can also be applied to the broad semantic set of named entitie... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 16 | 8,235 | 8,726 |
9_17 |
The BGB regulates the legal relations between private persons.
The law regulates the legal relations [...].
It regulates the legal relations [...].
Annotation of the Dataset ::: Named Entities vs. Legal Entities ::: Legal Entity
Basically, legal entities are either designations or references. A designation (or na... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 17 | 8,726 | 9,204 |
9_18 | The title of the Act on the Federal Constitutional Court is: `Gesetz über das Bundesverfassungsgericht (Bundesverfassungsgerichtsgesetz – BVerfGG)', where `Gesetz über das Bundesverfassungsgericht' is the long title, `Bundesverfassungsgerichtsgesetz' is the short title, and `BVerfGG' is the abbreviation. A reference t... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 18 | 9,204 | 9,727 |
9_19 |
Annotation of the Dataset ::: Named Entities vs. Legal Entities ::: Personal Data
A fundamental characteristic of the published decisions, that are the basis of our dataset, is that all personal information have been anonymised for privacy reasons. This affects the classes person, location and organization. Dependin... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 19 | 9,727 | 10,350 |
9_20 | that are part of this named entity (UNKREF16).
Fernsehmoderator G. PER
`television presenter G.'
Firma X... UN
`company X...'
in der A-Straße STR in ... ST
`in the A-Street in ...'
Annotation of the Dataset ::: Semantic Classes
We defined 19 fine-grained semantic classes. The (proto)typical classes are person,... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 20 | 10,350 | 10,774 |
9_21 | These are the coarse-grained classes legal norm, case-by-case regulation, court decision and legal literature. The classes legal norm and case-by-case regulation include designations and references, while court decision and legal literature include only references.
In the process of developing the typology and annota... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 21 | 10,774 | 11,440 |
9_22 | Continent was integrated into landscape.
The specification of the 19 fine-grained classes was motivated by the need for distinguishing entities in the legal domain. A first distinction was made between standards and binding acts. Standards, which belong to legal norm, are legal rules adopted by a legislative body in ... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 22 | 11,440 | 12,039 |
9_23 | It includes regulation (arrangements or instructions on subjects) and contract (agreements between subjects). In addition, court decision and legal literature, which are important in the decision making process, were put into their own categories.
Within person, we distinguish between judge and lawyer, key roles ment... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 23 | 12,039 | 12,682 |
9_24 |
Annotation of the Dataset ::: Semantic Classes ::: Person
The coarse-grained class person PER contains the fine-grained classes judge RR, lawyer AN and person PER (such as accused, plaintiff, defendant, witness, appraiser, expert, etc.), who are involved in a court process and mentioned in a decision. In example (UN... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 24 | 12,682 | 13,087 |
9_25 |
Zwar ist Paul Kirchhof RR mit dem Vizepräsidenten Kirchhof PER als dessen Bruder in der Seitenlinie im zweiten Grade verwandt...
`Although Paul Kirchhof is related to the Vice President Kirchhof as his brother in the second-degree sidelines...'
Annotation of the Dataset ::: Semantic Classes ::: Location
The coarse... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 25 | 13,087 | 13,663 |
9_26 | Street (UNKREF23) refers to avenues, squares, municipalities, attractions etc., i. e., named entities within a city or a village. Landscape (UNKREF24) includes continents, lakes, rivers and other geographical objects.
... hat bislang nur das Land Mecklenburg-Vor-ForestGreen!50I pommern LD Gebrauch gemacht.
`So far, ... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 26 | 13,663 | 14,129 |
9_27 | Februar 2016 zugrunde ...
`The arrest warrant is based on a decision of the Appeal Court in Bucharest of 18 February 2016 ...'
Zwar legt der Bezug auf die Grenzwertüberschreitung 2015 insbesondere in der Cornelius-GreenYellowI straße STR ...
`Admittedly, the reference to the exceedance of the 2015 threshold applies... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 27 | 14,129 | 14,542 |
9_28 | dort angeboten werden ...
`... come from the region around the river Main or are offered there...'
Annotation of the Dataset ::: Semantic Classes ::: Organization
The coarse-grained class organization ORG is divided into public/social, state and economic institutions. Social and public institutions such as parties,... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 28 | 14,542 | 15,025 |
9_29 | Institution INN (UNKREF27) contain public administrations, including federal and state ministries and the constitutional bodies of the Federal Republic of Germany at the federal and state level, i. e., the Federal Government, the Federal Council, the Bundestag, the state parliaments and governments. Company UN (UNKREF... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 29 | 15,025 | 15,610 |
9_30 |
`The state government of Rhineland-Palatinate refrained from commenting.'
... eingeführte Smartphone-Modellreihe des US-amerikanischen Unternehmens Apple UN ...
`... introduced smartphone model series of the US company Apple ...'
Court designations play a central role in decisions, which is why they are collected ... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 30 | 15,610 | 16,170 |
9_31 | Furthermore, brands are often discussed in decisions of the Federal Patent Court. They are subsumed under brand MRK, which can be contextual and semantically ambiguous, such as `Becker' from (UNKREF30), which has evolved from a personal name. | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 31 | 16,170 | 16,413 |
9_32 |
Diesen Anspruch hat das LSG Mecklenburg-RubineRed!50I Vorpommern GRT mit Urteil vom 22.2.2017 verneint ...
`This claim was rejected by the LSG Mecklenburg-Vorpommern by judgment of 22.2.2017 ...'
Vorliegend stehen sich die Widerspruchsmarke Becker Mining MRK und die angegriffene Marke Becker MRK gegenüber.
`In the... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 32 | 16,413 | 16,830 |
9_33 |
Annotation of the Dataset ::: Semantic Classes ::: Legal Norms
Norms are divided according to their legal status into the fine-grained classes of law GS, ordinance VO and European legal norm EUN. Law is composed of the standards adopted and designated by the legislature (Bundestag, Bundesrat, Landtag). Ordinance inc... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 33 | 16,830 | 17,371 |
9_34 |
Example (UNKREF32) includes a reference to the `Part-Time and Limited Term Employment Act' and the designation 'Basic Law'. The complex reference consists of the reference to the particular section of the law, its name and abbreviation (in brackets), date of issue, the reference in parenthesis and the details of the ... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 34 | 17,371 | 17,933 |
9_35 |
... § 14 Absatz 2 Satz 2 des Gesetzes über Teil- RedOrange!70I zeitarbeit und IRedOrange!70Ibefristete Arbeitsverträge RedOrange!70I(Tz-RedOrange!70I BfG) vom 21. | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 35 | 17,933 | 18,097 |
9_36 | Dezember 2000 (Bundesgesetz- RedOrange!70I blatt Seite 1966), zuletzt geändert durch GesetzRedOrange!70I vomRedOrange!70IRedOrange!70I20.RedOrange!70IDezemberRedOrange!70I2011 (Bundesgesetzblatt IRedOrange!70I Seite 2854 ) RedOrange!70I GS, ist nach Maßgabe der Gründe mit dem Grundgesetz GS vereinbar. | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 36 | 18,097 | 18,400 |
9_37 |
`... section 14 paragraph 2 sentence 2 of the Law on Part-Time and Limited Term Employment Act (TzBfG) of 21 December 2000 (Federal Law Gazette I, page 1966), as last amended by the Law of 20 December 2011 (Federal Law Gazette I, page 2854), shall be published in accordance with the reasons compatible with the Basic ... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 37 | 18,400 | 18,797 |
9_38 | 35 para. 6 StVO...'
Annotation of the Dataset ::: Semantic Classes ::: Case-by-case Regulation
The class case-by-case regulation REG contains individual binding acts. These include regulation VS and contract VT. Regulation is an internal order or instruction from a superordinate authority to a subordinate, regulatin... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 38 | 18,797 | 19,401 |
9_39 | Some designations and references from these classes are similar to legal norm (UNKREF35, UNKREF36).
... insbesondere durch die Richtlinien zur Be-Peach!70I wertung des Grundvermögens –BewRGr– vom Peach!70I 19. September 1966 (BStBl I, S. 890) VS.
`... in particular by the Guidelines for the Valuation of Real Estate ... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 39 | 19,401 | 19,772 |
9_40 |
... fand der Manteltarifvertrag für die Beschäf-Goldenrod!70I tigten der Mitglieder der TGAOK VT (BAT/Goldenrod!70I AOK-Neu VT) vom 7. August 2003 Anwendung.
`... the Collective Agreement for the Employees of Members of TGAOK (BAT/AOK-New) was applied of 7 August 2003 ...'
Annotation of the Dataset ::: Semantic Cla... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 40 | 19,772 | 20,178 |
9_41 | It does not have any subclasses, the coarsed and fine-grained versions are identical. In court decision, the name of the official decision-making collection, the volume and the numbered article are cited. Often mentioned are also the court, if necessary the decision type, date and file number. Example (UNKREF39) cites... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 41 | 20,178 | 20,743 |
9_42 |
Annotation of the Dataset ::: Semantic Classes ::: Legal Literature
Legal literature LIT also contains references, but they refer to legal commentaries, legislative material, legal textbooks and monographs. The commentary in example (UNKREF39) includes the details of an author's and/or publisher's name, the name of ... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 42 | 20,743 | 21,356 |
9_43 |
... vgl zB BVerfGE 62, 1, 45 RS; BVerfGEDandelion!70I 119, 96, 179 RS; BSG SozR 4–2500 § 62 NrDandelion!70I 8 RdNr 20 f RS; Hauck/Wiegand, KrV 2016,Tan!60!Bittersweet!70!whiteI 1, 4 LIT ...
`... cf. i.e. | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 43 | 21,356 | 21,562 |
9_44 | BVerfGE 62, 1, 45; BVerfGE 119, 96, 179; BSG SozR 4–2500 § 62 Nr 8 RdNr 20 f; Hauck/Wiegand, KrV 2016, 1, 4 ...'
Description of the Dataset
The dataset, which also includes annotation guidelines, is freely available under a CC-BY 4.0 license. | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 44 | 21,562 | 21,807 |
9_45 | The named entity annotations adhere to the CoNLL-2002 format BIBREF14, while time expressions were annotated using TimeML BIBREF15.
Description of the Dataset ::: Original Source Documents
Legal documents are a rather heterogeneous class, which also manifests in their linguistic properties, including the use of name... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 45 | 21,807 | 22,416 |
9_46 | When comparing legal documents such as laws, court decisions or administrative regulations, decisions are the best option. In laws and administrative regulations, the frequencies of person, location and organization are not high enough for NER experiments. Court decisions, on the other hand, include person, location, ... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 46 | 22,416 | 22,943 |
9_47 | The documents originate from seven federal courts: Federal Labour Court (BAG), Federal Fiscal Court (BFH), Federal Court of Justice (BGH), Federal Patent Court (BPatG), Federal Social Court (BSG), Federal Constitutional Court (BVerfG) and Federal Administrative Court (BVerwG).
From the table of contents, 107 document... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 47 | 22,943 | 23,308 |
9_48 | The data was collected from the XML documents, i. e., it was extracted from the XML elements Mitwirkung, Titelzeile, Leitsatz, Tenor, Tatbestand, Entscheidungsgründe, Gründen, abweichende Meinung, and sonstiger Titel. The metadata at the beginning of the documents (name of court, date of decision, file number, Europea... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 48 | 23,308 | 23,770 |
9_49 | The extracted data was split into sentences, tokenised using SoMaJo BIBREF16 and manually annotated in WebAnno BIBREF17.
The annotated documents are available in CoNNL-2002. The information originally represented by and through the XML markup was lost in the conversion process. We decided to use CoNNL-2002 because ou... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 49 | 23,770 | 24,277 |
9_50 | Nevertheless, it is possible, of course, to re-insert the annotated information back into the XML documents.
Description of the Dataset ::: Annotation of Named Entities
The dataset consists of 66,723 sentences with 2,157,048 tokens (incl. punctuation), see Table . The sizes of the seven court-specific datasets varie... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 50 | 24,277 | 24,741 |
9_51 | 19–23 %. The Federal Patent Court (BPatG) dataset contains the lowest number of annotated entities (10.41 %).
The dataset includes two different versions of annotations, one with a set of 19 fine-grained semantic classes and another one with a set of 7 coarse-grained classes (Table ). There are 53,632 annotated entit... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 51 | 24,741 | 25,186 |
9_52 | Overall, the most frequent entities are law GS (34.53 %) and court decision RS (23.46 %). The other legal classes (ordinance VO, European legal norm EUN, regulation VS, contract VT, and legal literature LIT) are much less frequent (1–6 % each). Even less frequent (less than 1 %) are lawyer AN, street STR, landscape LD... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 52 | 25,186 | 25,649 |
9_53 | More than half of city and street, about 55 %, have also been modified. Landscape and organization are affected as well, with 40 % and 15 % of the occurrences edited accordingly. However, anonymisation is typically not applied to judge, country, institution and court (1–5 %).
The dataset was originally annotated by t... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 53 | 25,649 | 26,257 |
9_54 | For the sentence extraction we paid special attention to the anonymised mentions of person, location or organization entities, because these are usually explained at their first mention. The resulting sample consisted of 2005 sentences with a broad variety of different entities (3 % of all sentences from each federal ... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 54 | 26,257 | 26,864 |
9_55 | Differences were in the identification of court decision and legal literature. Some unusual references of court decision (consisting only of decision type, court, date, file number) were not annotated such as `Urteil des Landgerichts Darmstadt vom 16. April 2014 – 7 S 8/13 –'. Apart from missing legal literature annot... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 55 | 26,864 | 27,329 |
9_56 | 35', `Bekanntmachung des BMG gemäß §§ 295 und 301 SGB V zur Anwendung des OPS vom 21.10.2010').
The second annotator had difficulties annotating the class law, not all instances were identified (`§ 272 Abs. 1a und 1b HGB', `§ 3c Abs. | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 56 | 27,329 | 27,564 |
9_57 | 2 Satz 1 EStG'), others only partially (`§ 716 in Verbindung mit' in `§ 716 in Verbindung mit §§ 321 , 711 ZPO'). Some titles of contract were not recognised and annotated (`BAT', `TV-L', `TVÜ-Länder' etc.).
This evaluation has revealed deficiencies in the annotation guidelines, especially regarding court decision an... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 57 | 27,564 | 28,067 |
9_58 |
Description of the Dataset ::: Annotation of Time Expressions
All court decisions were annotated automatically for time expressions using a customised version of HeidelTime BIBREF19, which was adapted to the legal domain BIBREF20. This version of Heideltime achieves an F$_1$ value of 89.1 for partial identification ... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 58 | 28,067 | 28,579 |
9_59 | It also includes expressions such as `present', `former' or `future'. DURATION describes time periods such as `two hours' or `six years'. SET describes a set of times/periods (`every day', `twice a week'). TIME describes a time expression (`13:12', `tomorrow afternoon'). Expressions with a granularity less than 24 hou... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 59 | 28,579 | 29,076 |
9_60 | 94 of which are of type DATE.
...vgl. BGH, Beschluss vom blue$<$TIMEX3 redtid=Purple"t14" redtype=Purple"DATE" redvalue=Purple”1999-02-03”blue$>$3. Februar 1999blue$<$/TIMEX3$>$ – 5 StR 705/98, juris Rn. 2 ...
Evaluation
The dataset was thoroughly evaluated, see leitner2019fine for more details. | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 60 | 29,076 | 29,376 |
9_61 | As state of the art models, Conditional Random Fields (CRFs) and bidirectional Long-Short Term Memory Networks (BiLSTMs) were tested with the two variants of annotation. For CRFs, these are: CRF-F (with features), CRF-FG (with features and gazetteers), CRF-FGL (with features, gazetteers and lookup). | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 61 | 29,376 | 29,677 |
9_62 | For BiLSTM, we used models with pre-trained word embeddings BIBREF22: BiLSTM-CRF BIBREF23, BiLSTM-CRF+ with character embeddings from BiLSTM BIBREF24, and BiLSTM-CNN-CRF with character embeddings from CNN BIBREF25. To evaluate the performance we used stratified 10-fold cross-validation. As expected, BiLSTMs perform be... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 62 | 29,677 | 30,117 |
9_63 | CRFs reach up to 93.23 F$_1$ for the fine-grained classes and 93.22 F$_1$ for the coarse-grained ones. Both models perform best for judge, court and law.
Conclusions and Future Work
We describe a dataset that consists of German legal documents. For the annotation, we specified a typology of characteristic semantic c... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 63 | 30,117 | 30,597 |
9_64 | A functional service based on the work described in this paper will be made available through the European Language Grid BIBREF26.
In terms of future work, we will look into approaches for extending and further optimizing the dataset. We will also perform additional experiments with more recent state of the art appro... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 64 | 30,597 | 31,200 |
9_65 | We also plan to produce an XML version of the dataset that also includes the original XML annotations.
Acknowledgements
This work has been partially funded by the project Lynx, which has received funding from the EU's Horizon 2020 research and innovation programme under grant agreement no. 780602, see http://www.lyn... | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 65 | 31,200 | 31,533 |
9_66 |
Table 1: Dataset size (tokens, sentences, annotated tokens)
Table 2: Distribution of fine-grained (f) and coarse-grained (c) classes in the dataset
Table 3: Distribution of time expressions in the dataset
Table 4: Precision, recall and F1 values of the CRF and BiLSTM models for the fine- and coarse-grained classes | https://arxiv.org/abs/2003.13016 | A Dataset of German Legal Documents for Named Entity Recognition | 66 | 31,533 | 31,853 |
10_0 | Character-Level Models versus Morphology in Semantic Role Labeling
Character-level models have become a popular approach specially for their accessibility and ability to handle unseen data. However, little is known on their ability to reveal the underlying morphological structure of a word, which is a crucial skill fo... | https://arxiv.org/abs/1805.11937 | Character-Level Models versus Morphology in Semantic Role Labeling | 0 | 0 | 601 |
10_1 | We conduct an in-depth error analysis for each morphological typology and analyze the strengths and limitations of character-level models that relate to out-of-domain data, training data size, long range dependencies and model complexity. Our exhaustive analyses shed light on important characteristics of character-lev... | https://arxiv.org/abs/1805.11937 | Character-Level Models versus Morphology in Semantic Role Labeling | 1 | 601 | 1,307 |
10_2 | This assumption has two major shortcomings especially for languages with rich morphology: (1) inability to handle unseen or out-of-vocabulary (OOV) word-forms (2) inability to exploit the regularities among word parts. The limitations of word embeddings are particularly pronounced in sentence-level semantic tasks, esp... | https://arxiv.org/abs/1805.11937 | Character-Level Models versus Morphology in Semantic Role Labeling | 2 | 1,307 | 1,844 |
10_3 | Here the stems köy (village) and sendika (union) function similarly in semantic terms with respect to the verb come (as the origin of the agents of the verb), where şehir (town) and meclis (council) both function as the end point. These semantic similarities are determined by the common word parts shown in bold. Howev... | https://arxiv.org/abs/1805.11937 | Character-Level Models versus Morphology in Semantic Role Labeling | 3 | 1,844 | 2,337 |
10_4 | Therefore, for a successful semantic application, the model should be able to capture both the regularities, i.e, morphological tags and the irregularities, i.e, lemmas of the word.
Morphological analysis already provides the aforementioned information about the words. However access to useful morphological features ... | https://arxiv.org/abs/1805.11937 | Character-Level Models versus Morphology in Semantic Role Labeling | 4 | 2,337 | 2,969 |
10_5 | However the extent to which these tasks depend on morphology is small; and their relation to semantics is weak. Hence, little is known on their true ability to reveal the underlying morphological structure of a word and their semantic capabilities. Furthermore, their behaviour across languages from different families;... | https://arxiv.org/abs/1805.11937 | Character-Level Models versus Morphology in Semantic Role Labeling | 5 | 2,969 | 3,531 |
10_6 |
To achieve this, we perform a case study on semantic role labeling (SRL), a sentence-level semantic analysis task that aims to identify predicate-argument structures and assign meaningful labels to them as follows:
$[$ Villagers $]$ comers came $[$ to town $]$ end point
We use a simple method based on bidirectiona... | https://arxiv.org/abs/1805.11937 | Character-Level Models versus Morphology in Semantic Role Labeling | 6 | 3,531 | 4,008 |
10_7 | The gold morphology serves as the upper bound for us to compare and analyze the performances of character-level models on languages of varying morphological typologies. We carry out an exhaustive error analysis for each language type and analyze the strengths and limitations of character-level models compared to morph... | https://arxiv.org/abs/1805.11937 | Character-Level Models versus Morphology in Semantic Role Labeling | 7 | 4,008 | 4,751 |
10_8 | Our experiments and analysis reveal insights such as:
Method
Formally, we generate a label sequence $\vec{l}$ for each sentence and predicate pair: $(s,p)$ . Each $l_t\in \vec{l}$ is chosen from $\mathcal {L}=\lbrace \mathit {roles \cup nonrole}\rbrace $ , where $roles$ are language-specific semantic roles (mostly ... | https://arxiv.org/abs/1805.11937 | Character-Level Models versus Morphology in Semantic Role Labeling | 8 | 4,751 | 5,164 |
10_9 | Given $\theta $ as model parameters and $g_t$ as gold label for $t_{th}$ token, we find the parameters that minimize the negative log likelihood of the sequence:
$$\hat{\theta }=\underset{\theta }{\arg \min } \left( -\sum _{t=1}^n log (p(g_t|\theta ,s,p)) \right)$$ (Eq. 7)
Label probabilities, $p(l_t|\theta ,s,p... | https://arxiv.org/abs/1805.11937 | Character-Level Models versus Morphology in Semantic Role Labeling | 9 | 5,164 | 5,531 |
10_10 | First, the word encoding layer splits tokens into subwords via $\rho $ function.
$$\rho (w) = {s_0,s_1,..,s_n}$$ (Eq. 8)
As proposed by BIBREF0 , we treat words as a sequence of subword units. Then, the sequence is fed to a simple bi-LSTM network BIBREF15 , BIBREF16 and hidden states from each direction are weig... | https://arxiv.org/abs/1805.11937 | Character-Level Models versus Morphology in Semantic Role Labeling | 10 | 5,531 | 5,996 |
10_11 |
$$hs_f, hs_b = \text{bi-LSTM}({s_0,s_1,..,s_n}) \\
\vec{w} = W_f \cdot hs_f + W_b \cdot hs_b + b$$ (Eq. 9)
There may be more than one predicate in the sentence so it is crucial to inform the network of which arguments we aim to label. In order to mark the predicate of interest, we concatenate a predicate flag $p... | https://arxiv.org/abs/1805.11937 | Character-Level Models versus Morphology in Semantic Role Labeling | 11 | 5,996 | 6,350 |
10_12 |
$$\vec{x_{t}} = [\vec{w};pf_t]$$ (Eq. 10)
Final vector, $\vec{x_t}$ serves as an input to another bi-LSTM unit.
$$\vec{h_{f}, h_{b}} = \text{bi-LSTM}(x_{t})$$ (Eq. 11)
Finally, the label distribution is calculated via softmax function over the concatenated hidden states from both directions. | https://arxiv.org/abs/1805.11937 | Character-Level Models versus Morphology in Semantic Role Labeling | 12 | 6,350 | 6,655 |
10_13 |
$$\vec{p(l_t|s,p)} = softmax(W_{l}\cdot [\vec{h_{f}};\vec{h_{b}}]+\vec{b_{l}})$$ (Eq. 12)
For simplicity, we assign the label with the highest probability to the input token. .
Subword Units
We use three types of units: (1) words (2) characters and character sequences and (3) outputs of morphological analysis.... | https://arxiv.org/abs/1805.11937 | Character-Level Models versus Morphology in Semantic Role Labeling | 13 | 6,655 | 7,064 |
10_14 | Table 1 shows sample outputs of various $\rho $ functions.
Here, char function simply splits the token into its characters. Similar to n-gram language models, char3 slides a character window of width $n=3$ over the token. Finally, gold morphological features are used as outputs of morph-language. Throughout this pape... | https://arxiv.org/abs/1805.11937 | Character-Level Models versus Morphology in Semantic Role Labeling | 14 | 7,064 | 7,621 |
10_15 | As an exception, it outputs additional information for some languages, such as parts-of-speech tags for Turkish. Word segmenters such as Morfessor and Byte Pair Encoding (BPE) are other commonly used subword units. Due to low scores obtained from our preliminary experiments and unsatisfactory results from previous stu... | https://arxiv.org/abs/1805.11937 | Character-Level Models versus Morphology in Semantic Role Labeling | 15 | 7,621 | 7,981 |
10_16 |
Experiments
We use the datasets distributed by LDC for Catalan (CAT), Spanish (SPA), German (DEU), Czech (CZE) and English (ENG) BIBREF17 , BIBREF18 ; and datasets made available by BIBREF19 , BIBREF20 for Finnish (FIN) and Turkish (TUR) respectively . Datasets are provided with syntactic dependency annotations and ... | https://arxiv.org/abs/1805.11937 | Character-Level Models versus Morphology in Semantic Role Labeling | 16 | 7,981 | 8,540 |
10_17 | Here, #pred is number of predicates, and #role refers to number distinct semantic roles that occur more than 10 times. More detailed statistics about the datasets can be found in BIBREF27 , BIBREF19 , BIBREF20 .
Experimental Setup
To fit the requirements of the SRL task and of our model, we performed the following:
... | https://arxiv.org/abs/1805.11937 | Character-Level Models versus Morphology in Semantic Role Labeling | 17 | 8,540 | 9,047 |
10_18 | For the sake of memory efficiency and performance, we used an abbreviation (e.g., CFdT) for each MWE during training and testing.
Original dataset defines its own format of semantic annotation, such as 17:PBArgM_mod $\mid $ 19:PBArgM_mod meaning the node is an argument of $17_{th}$ and $19_{th}$ tokens with ArgM-mod ... | https://arxiv.org/abs/1805.11937 | Character-Level Models versus Morphology in Semantic Role Labeling | 18 | 9,047 | 9,522 |
10_19 |
Words are splitted from derivational boundaries in the original dataset, where each inflectional group is represented as a separate token. We first merge boundaries of the same word, i.e, tokens of the word, then we use our own $\rho $ function to split words into subwords.
We lowercase all tokens beforehand and pla... | https://arxiv.org/abs/1805.11937 | Character-Level Models versus Morphology in Semantic Role Labeling | 19 | 9,522 | 10,103 |
10_20 | We used gradient clipping and early stopping to prevent overfitting. Stochastic gradient descent is used as the optimizer. The initial learning rate is set to 1 and reduced by half if scores on development set do not improve after 3 epochs. We use the provided splits and evaluate the results with the official evaluati... | https://arxiv.org/abs/1805.11937 | Character-Level Models versus Morphology in Semantic Role Labeling | 20 | 10,103 | 10,653 |
10_21 | Most of the early SRL work report combined scores (argument labeling with predicate sense disambiguation (PSD)). However, PSD is considered a simpler task with higher F1 scores . Therefore, we believe omitting PSD helps us gain more useful insights on character level models.
Results and Analysis
Our main results on ... | https://arxiv.org/abs/1805.11937 | Character-Level Models versus Morphology in Semantic Role Labeling | 21 | 10,653 | 11,260 |
10_22 | IOW and IOC values are calculated on the test set.
The biggest improvement over the word baseline is achieved by the models that have access to morphology for all languages (except for English) as expected. Character trigrams consistently outperformed characters by a small margin. Same pattern is observed on the resu... | https://arxiv.org/abs/1805.11937 | Character-Level Models versus Morphology in Semantic Role Labeling | 22 | 11,260 | 11,746 |
10_23 | We analyze the results separately for agglutinative and fusional languages and reveal the links between certain linguistic phenomena and the IOC, IOW values.
Similarity between models
One way to infer similarity is to measure diversity. Consider a set of baseline models that are not diverse, i.e., making similar err... | https://arxiv.org/abs/1805.11937 | Character-Level Models versus Morphology in Semantic Role Labeling | 23 | 11,746 | 12,378 |
10_24 | Suppose that a prediction $p_{i}$ is generated for each token by a model $m_i$ , $i \in n$ , then the final prediction is calculated from these predictions by:
$$p_{final} = f(p_0, p_1,..,p_n|\phi )$$ (Eq. 36)
where $f$ is the combining function with parameter $\phi $ . The simplest global approach is averaging ... | https://arxiv.org/abs/1805.11937 | Character-Level Models versus Morphology in Semantic Role Labeling | 24 | 12,378 | 12,781 |
10_25 | Mean function combines model outputs linearly, therefore ignores the nonlinear relation between base models/units. In order to exploit nonlinear connections, we learn the parameters $\phi $ of $f$ via a simple linear layer followed by sigmoid activation. In other words, we train a new model that learns how to best com... | https://arxiv.org/abs/1805.11937 | Character-Level Models versus Morphology in Semantic Role Labeling | 25 | 12,781 | 13,393 |
10_26 | To ensure that the only factor contributing to the diversity of the learners is the input representation, all parameters, training data and model settings are left unchanged.
Our results are given in Table 4 . IOB shows the improvement over the best of the baseline models in the ensemble. Averaging and stacking metho... | https://arxiv.org/abs/1805.11937 | Character-Level Models versus Morphology in Semantic Role Labeling | 26 | 13,393 | 14,014 |
10_27 | Amongst the first set, we observe that the improvement gained by character-morphology ensembles is higher (shown with green) than ensembles between characters and character trigrams (shown with red), whereas the opposite is true for the second set of languages. It can be interpreted as character level models being mor... | https://arxiv.org/abs/1805.11937 | Character-Level Models versus Morphology in Semantic Role Labeling | 27 | 14,014 | 14,502 |
10_28 |
Limitations and Strengths
To expand our understanding and reveal the limitations and strengths of the models, we analyze their ability to handle long range dependencies, their relation with training data and model size; and measure their performances on out of domain data.
Long Range Dependencies
Long range depend... | https://arxiv.org/abs/1805.11937 | Character-Level Models versus Morphology in Semantic Role Labeling | 28 | 14,502 | 15,094 |
10_29 | The unit of measure is number of tokens between the two; and argument is defined as the head of the argument phrase in accordance with dependency-based SRL task. For that purpose, we created bins of [0-4], [5-9], [10-14] and [15-19] distances. Then, we have calculate F1 scores for arguments in each bin. Due to low num... | https://arxiv.org/abs/1805.11937 | Character-Level Models versus Morphology in Semantic Role Labeling | 29 | 15,094 | 15,581 |
10_30 | We observe that either char or char3 closely follows the oracle for all languages. The gap between the two does not increase with the distance, suggesting that the performance gap is not related to long range dependencies. In other words, both characters and the oracle handle long range dependencies equally well.
Tra... | https://arxiv.org/abs/1805.11937 | Character-Level Models versus Morphology in Semantic Role Labeling | 30 | 15,581 | 16,210 |
10_31 | 4 . Apparently as the data size increases, the performances of both models logarithmically increase - with a varying speed. To speak in statistical terms, we fit a logarithmic curve to the observed F1 scores (shown with transparent lines) and check the x coefficients, where x refers to the number of sentences. This co... | https://arxiv.org/abs/1805.11937 | Character-Level Models versus Morphology in Semantic Role Labeling | 31 | 16,210 | 16,699 |
10_32 | It can be interpreted as: in the presence of more training data, char3 may surpass the oracle; i.e., char3 relies on data more than the oracle.
Out-of-Domain (OOD) Data
As part of the CoNLL09 shared task BIBREF27 , out of domain test sets are provided for three languages: Czech, German and English. We test our model... | https://arxiv.org/abs/1805.11937 | Character-Level Models versus Morphology in Semantic Role Labeling | 32 | 16,699 | 17,202 |
10_33 | The dramatic drop in German oracle model is due to the high lemma OOV rate which is a consequence of keeping compounds as a single lemma. Czech oracle model performs reasonably however is unable to beat the generalization power of the char3 model. Furthermore, the scores of the character models in Table 5 are higher t... | https://arxiv.org/abs/1805.11937 | Character-Level Models versus Morphology in Semantic Role Labeling | 33 | 17,202 | 17,801 |
10_34 |
Model Size
Throughout this paper, our aim was to gain insights on how models perform on different languages rather than scoring the highest F1. For this reason, we used a model that can be considered small when compared to recent neural SRL models and avoided parameter search. However, we wonder how the models behav... | https://arxiv.org/abs/1805.11937 | Character-Level Models versus Morphology in Semantic Role Labeling | 34 | 17,801 | 18,450 |
10_35 | This indicates that morphological signals help to extract more complex linguistic features that have semantic clues.
Predicted Morphological Tags
Although models with access to gold morphological tags achieve better F1 scores than character models, they can be less useful a in real-life scenario since they require g... | https://arxiv.org/abs/1805.11937 | Character-Level Models versus Morphology in Semantic Role Labeling | 35 | 18,450 | 19,056 |
10_36 | 5 , show that (except for Czech), predicted morphological tags are not as useful as characters alone.
Conclusion
Character-level neural models are becoming the defacto standard for NLP problems due to their accessibility and ability to handle unseen data. In this work, we investigated how they compare to models with... | https://arxiv.org/abs/1805.11937 | Character-Level Models versus Morphology in Semantic Role Labeling | 36 | 19,056 | 19,556 |
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