Word Sense Disambiguation (WSD) is a key task in Natural Language Understanding. It consists of assigning the appropriate meaning from a pre-defined sense inventory to a word in context. While knowledge-based approaches to this task have been proposed Agirre et al. (2014); Moro et al. (2014); Camacho-Collados et al. (2016b); Butnaru et al. (2017); Chaplot and Salakhutdinov (2018), supervised approaches Zhong and Ng (2010); Melamud et al. (2016); Iacobacci et al. (2016); Kågebäck and Salomonsson (2016) have been more effective in terms of performance Raganato et al. (2017a), on those languages where sense-annotated datasets are available. Unfortunately, obtaining such data is heavily time-consuming and expensive Schubert (2006), and reasonable amounts of sense-annotated data tend to be available for English only. This produces the so-called knowledge-acquisition bottleneck Gale et al. (1992).
The first main approach towards building sense-annotated corpora was SemCor Miller et al. (1993), providing annotations for the WordNet sense inventory Fellbaum (1998). Since then, several semi-automatic and automatic approaches have also been proposed. These automatic efforts tend to produce noisier annotations, but their coverage has been shown to lead to better supervised and semi-supervised WSD systems Taghipour and Ng (2015b); Otegi et al. (2016); Raganato et al. (2016); Yuan et al. (2016); Delli Bovi et al. (2017); Pasini and Navigli (2017), as well as to learn effective embedded representations for senses Iacobacci et al. (2015); Flekova and Gurevych (2016).
In this short survey we present the main approaches of the literature to build sense-annotated corpora, not only for WordNet but also for multilingual sense inventories like Wikipedia or BabelNet. There have been additional works to provide sense-annotated data for other resources such as the New Oxford American Dictionary Yuan et al. (2016) or other language-specific versions like GermaNet Henrich et al. (2012). While these works on these language-specific resources are certainly relevant, in this work we have kept a focus on English WordNet and multilingual resources with a higher coverage like Wikipedia and BabelNet. For a more specific survey on corpora annotated with language-specific versions of WordNet, please refer to petrolito2014survey.
Finally, we provide a general overview and statistics of these sense-annotated resources, providing hints for a better use and interoperability among resources and languages.
2 Sense-Annotated Corpora
Several works have attempted to construct sense-annotated datasets to overcome the knowledge-acquisition bottleneck. In this section we describe the main efforts on this direction, either manual, semi-automatic or fully automatic. In particuar, we present currently available sense-annotated corpora for three resources: WordNet (Section 2.1), Wikipedia (Section 2.2) and BabelNet (Section 2.3). Figure 1 presents a general overview of the resources and the sense-annotated corpora for each sense inventory.
WordNet Fellbaum (1998) has been one of the most widely used knowledge resource in lexical semantics. In fact, it has been the de-facto sense inventory for Word Sense Disambiguation for many years. The core unit in WordNet is the synset. A synset represents a concept or a meaning which is represented by its various lexicalizations (i.e. senses). For example, the meaning motor vehicle with four wheels can be expressed by its synonym senses auto, automobile, machine and motorcar.
The first and most prominent example of sense-annotated corpora is SemCor Miller et al. (1993). SemCor was manually annotated and consists of 352 documents and 226,040 sense annotations. SemCor has been the largest manually-annotated corpus for many years, and is the main corpus used in the literature to train supervised WSD systems Agirre et al. (2009); Zhong and Ng (2010); Raganato et al. (2017b).
SemEval evaluation datasets.
SemEval datasets provide reliable benchmarks for testing WSD systems. The main datasets from Senseval and SemEval competitions have been compiled and unified by raganatoetal:17. In particular, the datasets from Senseval-2 Edmonds and Cotton (2001), Senseval-3 task 1 Snyder and Palmer (2004), SemEval-2007 task 17 Pradhan et al. (2007), SemEval-2013 task 12 Navigli et al. (2013), and SemEval-2015 task 13 Moro and Navigli (2015). These datasets, which have in the main been used as evaluation benchmarks for WSD systems, contain a total of 7,253 sense annotations.
Princeton WordNet Gloss.
The Princeton WordNet Gloss Corpus111http://wordnet.princeton.edu/glosstag.shtml is a sense-annotated corpus of textual definitions (glosses) from WordNet synsets. The corpus was tagged semi-automatically: 330,499 manually sense instances were annotated manually while the remaining annotations (i.e. 118,856) were obtained automatically. This corpus of disambiguated glosses has already been proved useful in tasks like semantic similarity Pilehvar et al. (2013), domain labeling González et al. (2012) and Word Sense Disambiguation Baldwin et al. (2008); Agirre and Soroa (2009); Camacho-Collados et al. (2015).
The task of gathering sense annotations has proved expensive and not easily scalable. That is the reason why more recent approaches have attempted to exploit semi-automatic or automatic techniques. OMSTI222http://www.comp.nus.edu.sg/~nlp/corpora.html (Taghipour and Ng, 2015a, One Million Sense-Tagged Instances), which is a semi-automatically constructed corpus annotated with WordNet senses, is a prominent example. It was built by exploiting the alignment-based WSD approach of chan2005scaling on a large English-Chinese parallel corpus (Eisele and Chen, 2010, MultiUN corpus). OMSTI333Even though OMSTI was released along with SemCor, in this survey we refer to the portion of sense-annotated data from the MultiUN corpus only., coupled with SemCor, has already been successfully leveraged as training data for training supervised systems Taghipour and Ng (2015a); Iacobacci et al. (2016); Raganato et al. (2017a).
Wikipedia is a collaboratively-constructed encyclopedic resource consisting of concepts and entities and their corresponding pages. In addition to a large coverage of concepts and entities, Wikipedia provides multilinguality, as it covers over 250 languages and these languages are connected via interlingual links. In this section we describe two datasets providing disambiguations from Wikipedia pages.444Note that more Wikipedia sense-annotated datasets extracted from the Wikilinks project exist Singh et al. (2012); Eshel et al. (2017). However, due to privacy and license issues, these datasets cannot be shared directly. Please also refer to usbeck2015gerbil for an overview and unification of datasets focused on Entity Linking. For these two datasets we have used the same version of Wikipedia for a more accurate comparison555We used the Wikipedia dumps of November 2014..
This corpus contains the whole Wikipedia corpus with hyperlinks as sense-annotated instances. Hyperlinks are highlighted mentions within a Wikipedia article that directly links to another Wikipedia page. These links are provided by Wikipedia editors. 271 languages, with a different degree of coverage, were already available in the Wikipedia dump of November 2014.
is a corpus based on Wikipedia where the original links have been exploited in order to annotate other content word in the same Wikipedia page with a sense from BabelNet. Its English version comprises over 160M sense annotations with an estimated precision over 90%.
BabelNet Navigli and Ponzetto (2012) is a wide-coverage multilingual semantic network obtained from the integration of various encyclopedias and dictionaries (inter alia WordNet and Wikipedia). Being a superset of all these resources, BabelNet brings together lexicographic and encyclopedic knowledge, thus containing named entities and concepts from over 250 languages.
SenseDefs777http://lcl.uniroma1.it/disambiguated-glosses Camacho-Collados et al. (2016a) extends the effort from the Princeton WordNet Gloss Corpus project (see Section 2.1) by automatically disambiguating textual definitions from various heterogeneous sources in 263 languages. The underlying idea lies on the exploitation of the cross-complementarities of definitions of identical concepts from different languages and resources. The approach couples a graph-based disambiguation method Moro et al. (2014) with a refinement based on distributional similarity Camacho-Collados et al. (2016b). The proposed method was evaluated on four European languages (English, Spanish, French and Italian) with an estimated precision of over 80%.
|Resource||Type||#Langs||#Annotations||#Tokens (EN)||#Annot (EN)||Amb (EN)|
The construction of EuroSense888http://lcl.uniroma1.it/eurosense Delli Bovi et al. (2017) follows a similar approach to SenseDefs. In this case, parallel corpora is exploited for a single multilingual disambiguation. The output is a sense-annotated corpus for 21 languages for the Europarl parallel corpus Koehn (2005). The estimated precision for four languages with figures over 80% on average, with a peak of almost 90% for German.
Similarly to the previous approach, Train-o-Matic999http://trainomatic.org (Pasini and Navigli, 2017, T-o-M) aims at automatically annotating words from a raw corpus with senses. The main difference with respect to EuroSense and OMSTI lies in the fact that T-o-M does not need parallel data in order to annotate the input corpus. Being language independent and fully automatic, it has been proved to lead supervised systems to high performance Pasini et al. (2018), close or even better than those achieved when a manually annotated corpus is used for the training (e.g. SemCor Miller et al. (1993)). Moreover, it has also proved effective in languages other than English (i.e. Italian, Spanish, French, German and Chinese), as shown with the results of IMS Zhong and Ng (2010) trained on data produced by T-o-M on recent evaluation datasets.
In order to have a global overview of each of the corpora, we display the main features of each of the sense-annotated resources, including lexical and language coverage, in Table 1. For each dataset we display its underlying resource, number of languages covered and total number of sense annotations. In addition to these global statistics, Table 1 shows local statistics (i.e. number of tokens, number of sense annotations and ambiguity level) for English, which is the only language covered by all corpora. The ambiguity level of each dataset is computed as the average number of candidate senses per instance (i.e., senses with the same surface form of a target word).
As can be seen, the datasets are quite heterogeneous in nature, with three different resources and constructed via four different strategies: manual, semi-automatic, automatic and collaborative. The number of sense annotations also varies depending on the resource, with Wikipedia- and BabelNet-based corpora contributing with the highest number of annotations. This is coherent with the coverage of these resources: Wikipedia and BabelNet are two orders of magnitude higher than WordNet.
In this paper we have given an overview of available sense-annotated datasets for WordNet, Wikipedia and BabelNet, and for various languages. These datasets correspond to a wide variety of approaches, from manual construction to automatic or semi-automatic methods. By providing the list and statistics we are pursuing two main goals: (1) motivating and providing information about sense-annotated corpora to be used for research purposes, and (2) highlighting benefits and potential weaknesses of the various sense-annotated corpora, either manually or automatically constructed.
Moreover, this paper represents a first step for obtaining a fully-integrated repository of sense-annotated corpora which can be easily leveraged for research and evaluation purposes. Our goal is to integrate in the future a large number of resources shared in a unified multilingual repository, following the lines of raganatoetal:17 for WordNet sense-annotated corpora in English.
Jose Camacho-Collados is supported by a Google PhD Fellowship in Natural Language Processing.
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