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Proceedings of the 7th Linguistic Annotation Workshop & Interoperability with Discourse, pages 98–102,Sofia, Bulgaria, August 8-9, 2013. c©2013 Association for Computational Linguistics
Importing MASC into the ANNIS linguistic database:A case study of mapping GrAF
Arne NeumannEB Cognitive Science and SFB 632
University of Potsdam
Nancy IdeDepartment of Computer Science
Vassar College
Manfred StedeEB Cognitive Science and SFB 632
University of Potsdam
Abstract
This paper describes the importation ofManually Annotated Sub-Corpus (MASC)data and annotations into the linguisticdatabase ANNIS, which allows users to vi-sualize and query linguistically-annotatedcorpora. We outline the process ofmapping MASC’s GrAF representation toANNIS’s internal format relANNIS anddemonstrate how the system provides ac-cess to multiple annotation layers in thecorpus. This access provides informationabout inter-layer relations and dependen-cies that have been previously difficult toexplore, and which are highly valuable forcontinued development of language pro-cessing applications.
1 Introduction
Over the past decade, corpora with multiple lay-ers of linguistic annotation have been developedin order to extend the range of empirically-basedlinguistic research and enable study of inter-layerinteractions. Recently created corpora includeOntoNotes (Pradhan et al., 2007), the GroningenMeaning Bank (Basile et al., 2012), and the Man-ually Annotated Sub-Corpus (MASC)1 (Ide et al.,2010). Typically, such corpora are representedin idiosyncratic in-house formats, and developersprovide special software to access and query theannotations (for example, the OntoNotes “db tool”and Groningen’s GMB Explorer). Access withoutthe use of developer-supplied software often re-quires significant programming expertise, and asa result, it is not easy–or even possible–for othersto add to or modify data and annotations in the re-source.
This paper describes the importation of MASCdata and annotations into the linguistic database
1www.anc.org/MASC
ANNIS2 (Chiarcos et al., 2008; Zeldes et al.,2009), which was designed to visualize and querylinguistically-annotated corpora. Unlike mostother corpora with multi-layer annotations, nospecial software has been developed for accessto MASC. Instead, all MASC data and annota-tions are represented in GrAF (Ide and Suder-man, 2007), the XML serialization of the abstractmodel for annotations defined by ISO TC37 SC4’sLinguistic Annotation Framework (ISO/LAF) (Ideand Suderman, In press). GrAF is intended toserve as a generic “pivot” format that is isomor-phic to annotation schemes conforming to the ab-stract model and therefore readily mappable toschemes used in available systems. We outlinethe process of mapping GrAF to ANNIS’s internalformat relANNIS and demonstrate how the sys-tem provides access to multiple annotation layersin MASC.
2 The ANNIS Infrastructure
The ANNIS system is a linguistic database gearedtoward the requirements of querying multi-layerannotated corpora, and providing various visual-ization means for layers with different structuralproperties. In particular, the annotation typessupported are spans, DAGs with labelled edges,and pointing relations between terminals or non-terminals. For illustration, Figure 1 shows ascreenshot where various parallel annotations ofthe same data are provided: dependency trees,constituent trees (here with “secondary edges” indotted lines), and a grid view for annotations thatassign labels to token spans. In addition, ANNISoffers a “discourse view” giving the complete textwith coreference relations indicated by color andunderlining. In the top of the screenshot, it can benoted that the system also stored video (and au-
2http://www.sfb632.uni-potsdam.de/annis/
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Figure 1: Screenshot of ANNIS2
Figure 2: Querying MASC in ANNIS2 for an NP that includes botha food frame element and a location named entity
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dio) data, but that aspect shall not concern us inthis paper.
The system is web-based; the user interface iswritten in Java and ExtJS. The backend is Post-greSQL3. In general, all components are opensource under the Apache License 2.0, and youcan download ANNIS from the above-mentionedURL. We offer two versions: A server version, andthe more lightweight “ANNIS kickstarter”, whichcan be installed locally, e.g., on laptops.
ANNIS is complemented by SaltNPepper, aframework for converting annotations stemmingfrom various popular annotation tools (MMAX,EXMARaLDA, annotate/Synpathy, RSTTool) –see Section 4.
3 MASC and GrAF
MASC is a fully open, half-million word corpuscovering nineteen diverse genres of American En-glish drawn from the Open American NationalCorpus (OANC)4. The corpus includes manuallyproduced or hand-validated annotations for mul-tiple linguistic layers, including morphosyntax(two different annotations), shallow parse (nounand verb chunks), Penn Treebank syntax, andnamed entities. Portions of the corpus are alsoannotated for FrameNet frames, opinion, Prop-Bank predicate-arguments, and WordNet 3.1 wordsenses. Discourse-level annotation, including co-reference, clauses, and discourse markers, will beavailable in fall, 2013.
Like the OANC, all MASC annotationsare rendered in standoff form using GrAF,the graph-based format developed as a partof the ISO Linguistic Annotation Framework(ISO/LAF)(ISO 24612, 2012). GrAF is an XMLserialization of the LAF abstract model for annota-tions, a formalization of models used across mul-tiple applications for associating (linking) infor-mation, including not only directed-acyclic graphs(DAGs) but also ER diagrams, the Universal Mod-eling Language (UML), semantic and neural net-works, RDF/OWL, and, more generally, hyper-linked data on the World Wide Web. The modelis sufficiently general to represent any type of lin-guistic annotation; any serialization of the modelcan therefore serve as a pivot or intermediaryamong diverse annotation formats that conform tothe abstract model. Thus, any sufficiently well-
3http://www.postgresql.org/4www.anc.org/OANC
formed annotation scheme should be isomorphicto a GrAF representation of the same information.Problems arise only when a scheme does not spec-ify information explicitly but rather embeds the in-terpretation in processing software rather than inthe representation itself; for transduction to GrAF,this information must be made explicit in the rep-resentation.
Funding for MASC did not allow for extensivesoftware development; the expectation is that byrendering the corpus in the ISO standard GrAFformat, access could rely on GrAF-aware softwaredeveloped by others, or transduction from GrAFto appropriate alternative formats would be trivial.We have already developed and deployed meansto import linguistic data represented in GrAF intoUIMA, GATE, and NLTK, and we provide trans-ducers from GrAF to inline XML and the CoNLLIOB format.5 Additionally, a GrAF-to-RDF trans-ducer is near completion, which will enable inclu-sion of MASC in the Linguistic Linked Open Data(LLOD) cloud6. The incorporation of a GRAFtransducer for ANNIS provides another exampleof the flexibility afforded via the GrAF represen-tation.
4 Mapping GrAF to ANNIS viaSaltNPepper
Pepper is a software framework that converts lin-guistic data among various formats, e.g. CoNLL,EXMARaLDA, PAULA, TigerXML, RSTTooland TreeTagger (Zipser et al., 2011). It is builtupon the graph-based Salt meta model (Zipser andRomary, 2010), which is in turn based on the LAFabstract model for linguistic annotation. Map-ping GrAF to Salt extends the range of formatsinto which annotations represented in GrAF canbe automatically transduced to those to which Salthas been mapped, including ANNIS’s relationaldatabase format relANNIS.
The following steps were taken to import theMASC corpus into ANNIS: first, the MASC cor-pus data was extracted with the GrAF API7. Sec-ond, a mapping between GrAF and Salt datastructures was created. Most of the conversionis straightforward, since both models are graph-based. The only added processing is to provide
5Available from http://www.anc.org/MASC.6http://linguistics.okfn.org/
resources/llod/7http://sourceforge.net/projects/
iso-graf/
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explicit edge labels in the Salt representation forordered constiuents: in GrAF, directed edges fromone to several other nodes by default represent setsof ordered constituents and need not be explicitlylabeled as such, whereas in Salt, the role of alledges must be specified explicitly. Explicit labelsin ANNIS are required in order to generate the ap-propriate visualizations automatically (e.g. treesfor syntactic hierarchies and arc diagrams for syn-tactic dependencies).
Finally, the code was structured as a plug-infor Pepper and parameterized to make it usablefor GrAF-formatted corpora other than MASC. Itwill be included in the next SaltNPepper release.The code is currently available from our softwarerepository8.
5 MASC in ANNIS: Examples
The ANNIS Query Language (AQL) allows usersto search for specific token values and annotationsas well as relationships between them, even acrossannotation level boundaries.9 Token values arerepresented as text between quotes (e.g. "men"),while annotations are specified as attribute-valuepairs (e.g. pos="NN", a part-of-speech attributewith the value NN). A query for an annotation willreturn all elements with that annotation. Wherenecessary, namespaces10 can be added to any ele-ment to disambiguate, e.g., ptb:cat="NP" sig-nifies all annotation attribute-value pairs (attribute:cat, value: NP) that are in the ptb (Penn Tree-bank) namespace.
Relations among elements are specified byback-referencing incremental variable numbers,e.g. #1, #2 etc. Linguistically motivated opera-tors bind the elements together; e.g. #1 > #2means that the first element dominates the secondin a tree. Operators can express overlap and adja-cency between annotation spans, as well as recur-sive hierarchical relations that hold between nodes(such as elements in a syntactic tree).
The following examples show AQL queries thatcombine annotations from different layers:
8https://korpling.german.hu-berlin.de/svn/saltnpepper/PepperModules/GrAFModules/
9Note that ANNIS does not allow searching for arbitrarystrings from the primary data, but only for pre-identified seg-ments such as tokens, named entities, etc.
10A namespace groups one or more types of annotationinto a logical unit, e.g all annotations produced by a specifictool or project.
1. A VP that dominates a PP which contains anamed person at its right border:
cat="VP" & cat="PP" & NER="person" &
#1>#2 & #2 r #3
2. a VP of passive form in past tense that in-cludes a mention of a FrameNet frame ele-ment:
cat="VP" & voice="passive" &
tense="SimPas" & FE="Event" & #1 i #2
& #1 i #3 & #1 i #4
Figure 2 shows the results of a search for anNP that includes both a named entity of the typecountry and a FrameNet frame element of the typeFood:
cat="NP" & anc:type="country" &
FE="Food" & #1 i #2 & #1 i #3
6 Summary and Outlook
We explained the mapping of the MASC multi-layer corpus to the ANNIS database by interpret-ing the GrAF format via the Pepper framework.Both MASC and ANNIS are freely available; aportion of MASC will also be added to the onlinedemo version of ANNIS. We are also making thePepper converter module for GrAF available.
Version 3 of ANNIS is currently under devel-opment11. Besides a new front-end and a REST-based API, it offers improved tokenization support(annotation on the level of subtokens; conflictingtokenizations) and handles dialogue corpora withsimultaneous speakers as well as time-aligned au-dio/video data.
The ability to query across multiple annota-tion levels opens up significant new possibilitiesfor exploring linguistically annotated data. Mostcommonly, language models are developed us-ing information from at most one or two linguis-tic layers; ANNIS enables user to explore inter-dependencies that have been previously difficultto detect. By providing tools and data that areentirely free for use by the community, the AN-NIS and MASC efforts contribute to the growingtrend toward transparent sharing and openness oflinguistic data and tools.
11Early development releases can be found athttp://www.sfb632.uni-potsdam.de/annis/annis3.html
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Acknowledgments
MASC and GrAF development was supported byUS NSF award CRI-0708952. The work of A.N.and M.S. was supported by Deutsche Forschungs-gemeinschaft as part of the Collaborative ResearchCenter ”Information Structure” (SFB 632) at Univ.Potsdam and HU Berlin.
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