Word sense disambiguation is the process of determining which sense of a word is used in a given context . Due to its importance in understanding semantics of natural languages , word sense disambiguation has been extensively studied in Computational Linguistics . However , existing methods either are brittle and narrowly focus on specific topics or words , or provide only mediocre performance in real-world settings . Broad coverage and disambiguation quality are critical for a word sense disambiguation system . In this paper we present a fully unsupervised word sense disambiguation method that requires only a dictionary and unannotated text as input . Such an automatic approach overcomes the problem of brittleness suffered in many existing methods and makes broad-coverage word sense disambiguation feasible in practice . We evaluated our approach using SemEval 2007 Task 7 ( Coarse-grained English All-words Task ) , and our system significantly outperformed the best unsupervised system participating in Se-mEval 2007 and achieved the performance approaching top-performing supervised systems . Although our method was only tested with coarse-grained sense disambiguation , it can be directly applied to fine-grained sense disambiguation . In many natural languages , a word can represent multiple meanings / senses , and such a word is called a homograph . Word sense disambiguation(WSD ) is the process of determining which sense of a homograph is used in a given context . WSD is a long-standing problem in Computational Linguistics , and has significant impact in many real-world applications including machine translation , information extraction , and information retrieval . Generally , WSD methods use the context of a word for its sense disambiguation , and the context information can come from either annotated / unannotated text or other knowledge resources , such as Word-Net ( Fellbaum , 1998 ) , SemCor ( SemCor , 2008 ) , Open Mind Word Expert ( Chklovski and Mihalcea , 2002 ) , eXtended WordNet ( Moldovan and Rus , 2001 ) , Wikipedia ( Mihalcea , 2007 ) , parallel corpora ( Ng , Wang , and Chan , 2003 ) . In ( Ide and Véronis , 1998 ) many different WSD approaches were described . Usually , WSD techniques can be divided into four categories ( Agirre and Edmonds , 2006 ) , • Dictionary and knowledge based methods . These methods use lexical knowledge bases such as dictionaries and thesauri , and hypothesize that context knowledge can be extracted from definitions of words . For example , Lesk disambiguated two words by finding the pair of senses with the greatest word overlap in their dictionary definitions ( Lesk , 1986 ) . • Supervised methods . Supervised methods mainly adopt context to disambiguate words . A supervised method includes a training phase and a testing phase . In the training phase , a sense-annotated training corpus is required , from which syntactic and semantic features are extracted to create a classifier using machine learning techniques , such as Support Vector Machine ( Novischi et al . , 2007 ) . In the following testing phase , a word is classified into senses ( Mihalcea , 2002 ) ( Ng and Lee , 1996 ) . Currently supervised methods achieve the best disambiguation quality ( about 80 % precision and recall for coarse-grained WSD in the most recent WSD evaluation conference SemEval 2007 ( Navigli et al . , 2007 ) ) . Nevertheless , since training corpora are manually annotated and expensive , supervised methods are often brittle due to data scarcity , and it is hard to annotate and acquire sufficient contextual information for every sense of a large number of words existing in natural languages . • Semi-supervised methods . To overcome the knowledge acquisition bottleneck problem suffered by supervised methods , these methods make use of a small annotated corpus as seed data in a bootstrapping process ( Hearst , 1991 ) ( Yarowsky , 1995 ) . A word-aligned bilingual corpus can also serve as seed data ( Ng , Wang , and Chan , 2003 ) . • Unsupervised methods . These methods acquire contextual information directly from unannotated raw text , and senses can be induced from text using some similarity measure ( Lin , 1997 ) . However , automatically acquired information is often noisy or even erroneous . In the most recent SemEval 2007 ( Navigli et al . , 2007 ) , the best unsupervised systems only achieved about 70 % precision and 50 % recall . Disambiguation of a limited number of words is not hard , and necessary context information can be carefully collected and hand-crafted to achieve high disambiguation accuracy as shown in ( Yarowsky , 1995 ) . However , such approaches suffer a significant performance drop in practice when domain or vocabulary is not limited . Such a " cliff-style " performance collapse is called brittleness , which is due to insufficient knowledge and shared by many techniques in Artificial Intelligence . The main challenge of a WSD system is how to overcome the knowledge acquisition bottleneck and efficiently collect the huge amount of context knowledge . More precisely , a practical WSD need figure out how to create and maintain a comprehensive , dynamic , and up-todate context knowledge base in a highly automatic manner . The context knowledge required in WSD has the following properties : 1 . The context knowledge need cover a large number of words and their usage . Such a requirement of broad coverage is not trivial because a natural language usually contains thousands of words , and some popular words can have dozens of senses . For example , the Oxford English Dictionary has approximately 301,100 main entries ( Oxford , 2003 ) , and the average polysemy of the WordNet inventory is 6.18 ( Fellbaum , 1998 ) . Clearly acquisition of such a huge amount of knowledge can only be achieved with automatic techniques . 2 . Natural language is not a static phenomenon . New usage of existing words emerges , which creates new senses . New words are created , and some words may " die " over time . It is estimated that every year around 2,500 new words appear in English ( Kister , 1992 ) . Such dynamics requires a timely maintenance and updating of context knowledge base , which makes manual collection even more impractical . Taking into consideration the large amount and dynamic nature of context knowledge , we only have limited options when choosing knowledge sources for WSD . WSD is often an unconscious process to human beings . With a dictionary and sample sentences / phrases an average educated person can correctly disambiguate most polysemous words . Inspired by human WSD process , we choose an electronic dictionary and unannotated text samples of word instances as context knowledge sources for our WSD system . Both sources can be automatically accessed , provide an excellent coverage of word meanings and usage , and are actively updated to reflect the current state of languages . In this paper we present a fully unsupervised WSD system , which only requires WordNet sense inventory and unannotated text . In the rest of this paper , section 2 describes how to acquire and represent the context knowledge for WSD . We present our WSD algorithm in section 3 . Our WSD system is evaluated with SemEval-2007 Task 7 ( Coarse-grained English All-words Task ) data set , and the experiment results are discussed in section 4 . We conclude in section 5 . Broad coverage and disambiguation quality are critical for WSD techniques to be adopted in practice . This paper proposed a fully unsupervised WSD method . We have evaluated our approach with SemEval-2007 Task 7 ( Coarse-grained English Allwords Task ) data set , and we achieved F-scores approaching the top performing supervised WSD systems . By using widely available unannotated text and a fully unsupervised disambiguation approach , our method may provide a viable solution to the problem of WSD . The future work includes : 1 . Continue to build the knowledge base , enlarge the coverage and improve the system performance . The experiment results in Section 4.2 clearly show that more word instances can improve the disambiguation accuracy and recall scores ; 2 . WSD is often an unconscious process for human beings . It is unlikely that a reader examines all surrounding words when determining the sense of a word , which calls for a smarter and more selective matching strategy than what we have tried in Section 4.1 ; 3 . Test our WSD system on fine-grained SemEval 2007 WSD task 17 . Although we only evaluated our approach with coarse-grained senses , our method can be directly applied to finegrained WSD without any modifications .

Challenge: Unsupervised approaches for word sense disambiguation require no expensive annotations but are often noisy or erroneous and suffer when domain or vocabulary is not limited.
Approach: They propose an unsupervised method which requires only a dictionary and raw texts as context knowledge allowing automatic updates of the state of a language.
Outcome: The proposed method approaches top performing supervised systems in F-scors on the SemEval-2007 Task 7 dataset.