FreeLing is an open-source open-source multilingual
language processing library providing a wide
range of language analyzers for several languages.
It offers text processing and language annotation facilities
to natural language processing application
developers, simplifying the task of building those
applications. FreeLing is customizable and extensible.
Developers can use the default linguistic resources
(dictionaries, lexicons, grammars, etc.) directly,
or extend them, adapt them to specific domains,
or even develop new ones for specific languages.
This paper presents the semantic services included
in FreeLing, which are based on WordNet and EuroWordNet
databases. The recent release of the
UKB program under a GPL license made it possible
to integrate a long awaited word sense disambiguation
module into FreeLing. UKB provides state of
the art all-words sense disambiguation for any language
with an available WordNet.Postprint (published version

Agirre, Eneko

Padró, Lluís

Reese, Samuel

Soroa, Aitor

English

UPCommons. Portal del coneixement obert de la UPC

Semantic services in FreeLing 2.1: WordNet and UKBLluı´s Padro´Software DepartmentTALP Research CenterUniversitat Polite`cnicade Catalunyapadro@lsi.upc.eduSamuel ReeseISAE - SupaeroUniversite´ Paul Sabatiersamuel.reese@supaero.orgEneko Agirre, Aitor SoroaIXA NLP GroupUniversity of the Basque Country{e.agirre,a.soroa}@ehu.esAbstractFreeLing is an open-source open-source multilin-gual language processing library providing a widerange of language analyzers for several languages.It offers text processing and language annotation fa-cilities to natural language processing applicationdevelopers, simplifying the task of building thoseapplications. FreeLing is customizable and exten-sible. Developers can use the default linguistic re-sources (dictionaries, lexicons, grammars, etc.) di-rectly, or extend them, adapt them to specific do-mains, or even develop new ones for specific lan-guages.This paper presents the semantic services includedin FreeLing, which are based on WordNet and Eu-roWordNet databases. The recent release of theUKB program under a GPL license made it possibleto integrate a long awaited word sense disambigua-tion module into FreeLing. UKB provides state ofthe art all-words sense disambiguation for any lan-guage with an available WordNet.1 IntroductionBasic language processing tasks such as tokeniz-ing, morphological analysis, lemmatizing, part-of-speech tagging, word sense disambiguation(WSD), dependency parsing, etc. are needed formost natural language processing (NLP) applica-tions such as Machine Translation, Summariza-tion, Dialogue systems, Text mining, etc.This makes language analyzers a very valuableresources for researchers and developers in NLP.Also, the lack of out-of-the-box state-of-the-artsystems is a severe bottleneck for faster progressin the area, both in research and development.Additionally, a large part of the effort requiredto develop NLP systems is devoted to the adapta-tion of existing software resources to the platform,I/O format, or API of the final application.FreeLing was undertaken with the belief thatsteps should be taken towards general availabilityof basic NLP tools and resources, which may beused without restrictions. Thus, to enable fasteradvances and more portable systems in our area,an open–source model was chosen.After five years (first version was released on2004), over 10,000 downloads, and a growing usercommunity which has extended the initial threelanguages (English, Spanish and Catalan) to seven(adding Galician, Italian, Welsh, Portuguese, andAsturian) prove that the collaborative open modelis a productive approach to the development ofNLP tools and resources.In this paper, we focus on the FreeLing servicesrelated to semantic processing, namely wordnetaccess and word sense disambiguation. The nextsection presents the internal structure of the li-brary. Sections 3 and 4 present the wordnet accessand WSD services. Section 5 depicts some exam-ples, and Section 6 outlines some conclusions.2 Data structure and language analysisservicesFreeLing is conceived as a library on top of whichpowerful NLP applications can be developed, andoriented to ease the integration of language analy-sis services into higher level applications.Its architecture consists of a simple two-layerclient-server approach: A basic linguistic servicelayer which provides analysis services (morpho-logical analysis, tagging, parsing, ...), and an ap-plication layer which, acting as a client, requeststhe desired services from the analyzers.The library is written in C++, since speed is amust for real-world oriented applications. Addi-tionaly, APIs are provided to call the library ser-vices from Java, perl, and pyhton.The internal architecture of the system is basedon two kinds of objects: linguistic data objects andprocessing objects.2.1 Linguistic Data ClassesThe basic classes in the library are used to containlinguistic data (such as a word, a PoS tag, a sen-tence, a document...). Any client application mustbe aware of those classes in order to be able to pro-vide to each processing module the right data, andto correctly interpret the module results.The linguistic classes supported by the current ver-sion are:• analysis: A tuple <lemma, PoS tag, proba-bility, sense list>.• word: A word form with a list of possibleanalysis objects.• sentence: A list of word known to be a com-plete sentence, it may include also a parse treeand/or a dependency tree.• paragraph: A list of sentence known tobe an independent paragraph.• document: A list of paragraph that form acomplete document. It may contain also coref-erence information about the entity mentions inthe document.Figure 1 presents a UML diagram with the lin-guistic data classes.Figure 1: FreeLing-2.1 Linguistic Data Classes.2.2 Processing ClassesApart from classes containing linguistic data, thelibrary provides classes able to transform them.See Figure 2 below for a UML diagram.• tokenizer: Receives plain text and returns alist of word objects.• splitter: Receives a list of word objectsand returns a list of sentence objects.• morfo: Receives a list of sentence and mor-phologically annotates each word of each sen-tence in the list. In fact, this class applies a cas-cade of specialized processors (number detec-tion, date/time detection, multi-word detection,dictionary search, etc.) each of which is in turna processing class:– locutions: Multi-word recognizer.– dictionary: Dictionary lookup and suffixhandling.– numbers: Numerical expressions recog-nizer.– dates: Date/time expressions recognizer.– quantities: Ratio and percentage expres-sions and monetary amount recognizer.– punts: Punctuation symbol annotator.– probabilities: Lexical probabilities an-notator and unknown word handler.– np: Proper noun recognizer.• tagger: Receives a list of sentence anddisambiguates the PoS of each word in thegiven sentences. If the selected analysis car-ries retokenization information, the word maybe split in two or more new words.• NE classifier: Receives a list ofsentence and classifies all word tagged asproper nouns in the given sentences.• Sense annotator: Receives a list ofsentence and adds synset information to theselected analysis for each word.• Word sense disambiguator: Receivesa list of sentence and ranks the possiblesenses for each word selected analysis.• chunk parser: Receives a list ofsentence and enriches each of themwith a parse tree.• dependency parser: Receives a list ofparsed sentence and enriches each of themwith a dependency tree.• coreference solver: Receives a docu-ment formed by parsed sentence and en-riches the document with coreference informa-tion.3 Semantic services: WordNet accessThere are two basic semantic services: First, a ba-sic database access module that enables the clientapplication to consult a WordNet (Miller et al.,1991) structure (e.g. to find out which synsets alemma belongs to, which words are contained inone synset, or which are the hypernyms of certainsynset). Second, a knowledge-based word sensedisambiguator, which has been recently integratedthanks to the release of UKB disambiguator undera GPL license (Agirre and Soroa, 2009).3.1 SemanticDB moduleThis module handles WordNet-like structures,which are indexed in a local database. Thedatabase sources are provided with FreeLing, andcan be adapted –or completely changed– to matchthe application needs.The source database consists of two files:• The WN structure file contains a list of synsetcodes, with information about its PoS, its hy-pernyms, its WN semantic file, and its featuresin EuroWordNet TCO (A´lvez et al., 2008). Forinstance, the entry in this file for WN1.6 nounsynset {01630731 cat,true cat} is:01630731:N 01630126 05 Animal:ObjectThis file is indexed and used to find out synsetproperties or their hypernyms.• The language lexical file contains direct and in-verse links between lemmas and synset codes.For instance, the first line in the example be-low establishes a link from the noun lemmacat to all the synsets it belongs to. If theyare provided sorted by frequency, the first onecan be used to perform most-frequent-sense dis-ambiguation. The two last lines define whichwords are contained in the given synsets:W:cat:N 01630731 07306044 07143161S:01630731:N cat true_catS:07306044:N cat guy hombreNote that this module does not (yet) offer as ad-vanced functionalities as the standard WordNetsearch library, but it has the following advantages:• Source files are plain text and easy to build. In-dexing programs are provided with FreeLing toenable anyone to create his/her own semanticdatabase.• Language and WN structure files are separated,making it possible to use the structure file as anILI and map all languages to the same structureif necessary.• The synset codes serve as mere concept iden-tifiers, so they can be replaced by any othersemantic code (e.g. later WN versions synsetcodes, or even ad-hoc concept codes).• Being open-source, the capabilities of the mod-ule can be easily extended (e.g. to include moresemantic tags or more relations in the structurefile), or customized to one specific needs.Currently, FreeLing includes only semantic datafor English, Spanish, and Catalan, that are the onlylanguages that offer a version in the Global Word-Net Grid under an open-source license.3.1.1 Use of semantic information byFreeLing modulesThe Semantic DB module can be used directly bythe client application, but it is also used by othermodules in FreeLing:• The sense annotator: Accesses the database andenriches the text with all possible synsets foreach form.• The relaxation–labelling tagger (Padro´, 1998):Deals with constraint-grammar-like rules deal-ing with PoS tag, form, lemma, or sense toguide the selection of the right analysis.• The dependency parser (Atserias et al., 2005;Carrera et al., 2008): Uses heuristic rules deal-ing with PoS, syntax, senses, and TCO infor-mation to combine into a complete dependencytree the chunks produced by the shallow parser.• The coreference solver –based on (Soon et al.,2001): Uses TCO and hypernym relations be-tween two mentions as features used by a ma-chine learning classifier to determine whetherthey corefer.Figure 2: FreeLing-2.1 Main Processing Classes.4 Semantic services: UKB word sensedisambiguationThe PageRank-based word sense disambiguationalgorithm UKB (Agirre and Soroa, 2009), and theavailability of its code under GPL has recentlymade it possible to include a long-awaited fea-ture in FreeLing: A language-independent state-of-the-art all-words WSD module. UKB uses thestructure of local wordnets in order to performWSD, and it can be easily applied to any language,with the only requirement of having a wordnet.The original code has been integrated as is, anda simple wrapper has been developed that loadsthe sentences being analyzed by FreeLing into theappropriate UKB data structure (after the lemma-tizer and the tagger have chosen the right PoSand lemma for each word), calls the disambigua-tor, and loads its results back to the FreeLing datastructure. In this way, the UKB module enrichesthe analysis of a set of sentences with the rankedlist of synsets for each word.Knowledge files handled by this module are:• The dictionary file, which contains the associa-tion between words and synset codes. The samefile described above used by the semantic DBmodule is used. It is converted to the formatneeded by UKB at installation time. Conversionprograms are provided with FreeLing to enablethe user to handle his/her own dictionaries.• The relation graph, containing all relations be-tween synsets to be used by the PageRank al-gorithm. Since this file contains relations otherthan hyper/hyponymy, it is currently providedseparately, in text format, and indexed at in-stallation time (indexing programs are also pro-vided). Ideally, in the near future this file andthe WN structure file used by the SemanticDBshould be unified.Note that, again, the UKB algorithm is a genericgraph-based disambiguation tool, which can befed with any sense dictionary and any relationgraph for those senses. Currently, synset codesand relations from wordnets are used, but thismodule can be used to disambiguate on any senserepository just changing the used knowledge files.Since this approach of keeping knowledge/datacomponents as separated as possible from process-ing/code components is also followed by Free-Ling, they match easily and both together form avery flexible and sound platform to develop syntaxand semantic analyzers for any language.5 ExamplesIn this section we will show some simple ex-amples of the semantic capabilities of Free-Ling and its UKB component. An onlinedemo of the whole system can be found athttp://www.lsi.upc.edu/˜nlp/freeling.5.1 Basic sense annotationThe basic semantic functionality is mere sense an-notation, enriching a PoS-tagged sentence with alist of possible senses for each word. An exampleis shown in Figure 3.Figure 3: Sense annotation of a PoS-tagged sen-tence.If this annotation takes place before PoS tag-ging, the tagger may use the semantic informa-tion to help the disambiguation (e.g. a ConstraintGrammar based tagger). If that is not the case, theannotation can take place either before or after thetagging, depending on the user’s needs.If the synset codes provided in the sense dictio-nary are sorted by frequency, the user applicationonly needs to pick the first one to have a basic MFSdisambiguator.5.2 Semantics used by other FreeLingmodulesThe module in FreeLing that –currently– takes thelarger advantage from the availability of semanticinformation is the dependency parser. The parseris based on a set of heuristic rules that combinechunks and label their dependencies. See (Atseriaset al., 2005; Carrera et al., 2008) for details.Those heuristic rules may refer to certain prop-erties of the chunks (e.g. head PoS tag, headlemma, position relative to other chunks) includ-ing semantic features (TCO properties, WN se-mantic file, hypernyms).For instance, consider the Spanish sentencesJuan vio´ a su amigo (Juan saw his friend) and Juanescribio´ a su amigo (Juan wrote to his friend). InFigure 4: Analysis requiring semantics in dependency parsingthe former, his friend is the direct object of theverb to see, and in the later, it is the indirect objectof to write.The reason is that transitive Spanish verbs suchas to see that do not have indirect object requirethe use of the preposition a when the direct ob-ject is a person. On the other hand, for ditransitiveverbs such as to write the preposition a marks theindirect object.So, to properly parse these sentences, rules haveto be able to check about the Human conditionof the candidate objects. This is achieved thanksto the TCO access provided by the SemanticDBmodule, as illustrated in Figure 4.Another module that benefits from the semanticknowledge included in FreeLing is the machine-learning based coreference solver. The solver con-siders pairs of nominal mentions (noun phrasesand pronouns) and uses a classifier based on (Soonet al., 2001) to determine whether they corefer.The features used by the classifier include mor-phosyntax features such as the distance betweenthe mentions, their relative positions, whether theyare definite noun phrases, personal pronouns, theirgender, number, etc.They also include semantic information on thekind of entity they may be referring to: If the nounphrase head is a proper noun, a NE classifier isused to determine if it is a person, an organization,or a geographical name. If the noun phrase head isa common noun, its TCO properties are checked tofind out whether it is Human, Group or Place.Then, this information is provided as features tothe classifier.5.3 Word Sense DisambiguationThe frequency-ordered semantic dictionaries en-able the user to perform a straightforward most-frequent-sense disambiguation just picking thefirst sense in the list.The integration of the UKB module (Agirre andSoroa, 2009) offers a more informed disambigua-tion mechanism. The sense list is ordered accord-ing to the PageRank assigned by the algorithm.The user application can simply select the firstone, or use the rank information to perform anydesired action.The example sentences in Figure 5 illustratehow UKB is able to distinguish the two mainsenses for the word bank in different contexts, in-stead of choosing always the same, as a MFS dis-ambiguator would.Note that this doesn’t mean that UKB has ahigher accuracy than MFS at WSD. As reportedby (Agirre and Soroa, 2009), the results of UKB atthe performed experiments on English and Span-ish are quite near of MFS results, and clearly im-prove those of other unsupervised WSD systems.6 ConclusionsWe presented the semantic services included inthe FreeLing 2.1 library, which includes accessto wordnets and graph-based all-word sense dis-ambiguation on those wordnet, using the state-————————————————Figure 5: UKB disambiguation of bank in different contexts.of-the-art UKB system (Agirre and Soroa, 2009).The open source licence of these software toolsand their architecture, which completely separatescode from linguistic data, makes it possible to eas-ily adapt them to any domain or application needs,and provides a platform for affordable develop-ment of analyzers for new languages.AcknowledgementsThis work has been partially funded by the Span-ish Science and Innovation Ministry, via theKNOW project (TIN2006-15049-C3-03). For fur-ther details visit http://ixa.si.ehu.es/know.ReferencesEneko Agirre and Aitor Soroa. 2009. Personalizingpagerank for word sense disambiguation. In Pro-ceedings of the 12th conference of the Europeanchapter of the Association for Computational Lin-guistics (EACL-2009), Athens, Greece.Jordi Atserias, Elisabet Comelles, and Aingeru Mayor.2005. Txala un analizador libre de dependenciaspara el castellano. Procesamiento del Lenguaje Nat-ural, (35):455–456, September.Jordi Carrera, Irene Castello´n, Marina Lloberes, Lluı´sPadro´, and Nevena Tinkova. 2008. Dependencygrammars in freeling. Procesamiento del LenguajeNatural, (41):21–28, September.G. A. Miller, R. Beckwith, Christiane Fellbaum,D. Gross, K. Miller, and R. Tengi. 1991. Five pa-pers on wordnet. Special Issue of the InternationalJournal of Lexicography, 3(4):235–312.Lluı´s Padro´. 1998. A Hybrid Environment for Syntax–Semantic Tagging. Ph.D. thesis, Dep. Llenguatgesi Sistemes Informa`tics. Universitat Polite`cnica deCatalunya, February. http://www.lsi.upc.es/˜padro.W.M. Soon, H. T. Ng, and D.C.Y. Lim. 2001. Amachine learning approach to coreference resolu-tion of noun phrases. Computational Linguistics,27(4):521–544.Javier A´lvez, Jordi Atserias, Jordi Carrera, SalvadorCliment, Egoitz Laparra, Antoni Oliver, and Ger-man Rigau. 2008. Complete and consistent anno-tation of wordnet using the top concept ontology. InProceedings of the the 6th Conference on LanguageResources and Evaluation (LREC 2008), Marrakech(Morocco), May.

Semantic Services in FreeLing 2.1: WordNet and UKB

FreeLing is an open-source open-source multilingual

language processing library providing a wide

range of language analyzers for several languages.

It offers text processing and language annotation facilities

to natural language processing application

developers, simplifying the task of building those

applications. FreeLing is customizable and extensible.

Developers can use the default linguistic resources

(dictionaries, lexicons, grammars, etc.) directly,

or extend them, adapt them to specific domains,

or even develop new ones for specific languages.

This paper presents the semantic services included

in FreeLing, which are based on WordNet and EuroWordNet

databases. The recent release of the

UKB program under a GPL license made it possible

to integrate a long awaited word sense disambiguation

module into FreeLing. UKB provides state of

the art all-words sense disambiguation for any language

with an available WordNet

UPCommons

https://upcommons.upc.edu/bitstream/2117/7617/1/padro10a.pdf

Semantic Services in FreeLing 2.1: WordNet and UKB

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