The wordnet contains part-of-speech categories such as noun, verb, adjective and adverb. In Sanskrit, there is no formal distinction among nouns, adjectives and adverbs. This poses the question, is an adverb a separate category in Sanskrit? If not, then how do we accommodate it in a lexical resource? To investigate the issue, we attempt to study the complex nature of adverbs in Sanskrit and the policies adopted by Sanskrit lexicographers that would guide us in storing them in the Sanskrit wordnet.

Russian Language is currently poorly supported with WordNet-like resources. One of the new efforts for building Russian WordNet involves mining the monolingual dictionaries. While most steps of the building process are straightforward, word sense disambiguation (WSD) is a source of problems. Due to limited word context specific WSD mechanism is required for each kind of relations mined. This paper describes the WSD method used for mining hypernym relations. First part of the paper explains the main reasons for choosing monolingual dictionaries as the primary source of information for Russian language WordNet and states some problems faced during the information extraction. The second part defines algorithm used to extract hyponym-hypernym pair. The third part describes the algorithm used for WSD.

This paper describes an electronic variant of popular word game Alias where people have to guess words according to their associations via synonyms, opposites, hyperonyms etc. Lexical data comes from the Estonian Wordnet. The computer game Alias which draws information from Estonian Wordnet is useful at least for two reasons: it creates an opportunity to learn language through play, and it helps to evaluate and improve the quality of Estonian Wordnet.

Since the inception of the SENSEVAL evaluation exercises there has been a great deal of recent research into Word Sense Disambiguation (WSD). Over the years, various supervised, unsupervised and knowledge based WSD systems have been proposed. Beating the first sense heuristics is a challenging task for these systems. In this paper, we present our work on Most Frequent Sense (MFS) detection using Word Embeddings and BabelNet features. The semantic features from BabelNet viz., synsets, gloss, relations, etc. are used for generating sense embeddings. We compare word embedding of a word with its sense embeddings to obtain the MFS with the highest similarity. The MFS is detected for six languages viz., English, Spanish, Russian, German, French and Italian. However, this approach can be applied to any language provided that word embeddings are available for that language.

The data compiled through many Wordnet projects can be a rich source of seed information for a multilingual dictionary. However, the original Princeton WordNet was not intended as a dictionary per se, and spawning other languages from it introduces inherent ambiguity that confounds precise inter-lingual linking. This paper discusses a new presentation of existing Wordnet data that displays joints (distance between predicted links) and substitution (degree of equivalence between confirmed pairs) as a two-tiered horizontal ontology. Improvements to make Wordnet data function as lexicography include term-specific English definitions where the topical synset glosses are inadequate, validation of mappings between each member of an English synset and each member of the synsets from other languages, removal of erroneous translation terms, creation of own-language definitions for the many languages where those are absent, and validation of predicted links between non-English pairs. The paper describes the current state and future directions of a system to crowdsource human review and expansion of Wordnet data, using gamification to build consensus validated, dictionary caliber data for languages now in the Global WordNet as well as new languages that do not have formal Wordnet projects of their own.

The Ancient Greek WordNet (AGWN) and the Dynamic Lexicon (DL) are multilingual resources to study the lexicon of Ancient Greek texts and their translations. Both AGWN and DL are works in progress that need accuracy improvement and manual validation. After a detailed description of the current state of each work, this paper illustrates a methodology to cross AGWN and DL data, in order to mutually score the items of each resource according to the evidence provided by the other resource. The training data is based on the corpus of the Digital Fragmenta Historicorum Graecorum (DFHG), which includes ancient Greek texts with Latin translations.

Semantic similarity and relatedness measures play an important role in natural language processing applications. In this paper, we present the IndoWordNet::Similarity tool and interface, designed for computing the semantic similarity and relatedness between two words in IndoWordNet. A java based tool and a web interface have been developed to compute this semantic similarity and relatedness. Also, Java API has been developed for this purpose. This tool, web interface and the API are made available for the research purpose.

Supervised methods for Word Sense Disambiguation (WSD) benefit from high-quality sense-annotated resources, which are lacking for many languages less common than English. There are, however, several multilingual parallel corpora that can be inexpensively annotated with senses through cross-lingual methods. We test the effectiveness of such an approach by attempting to disambiguate English texts through their translations in Italian, Romanian and Japanese. Specifically, we try to find the appropriate word senses for the English words by comparison with all the word senses associated to their translations. The main advantage of this approach is in that it can be applied to any parallel corpus, as long as large, high-quality inter-linked sense inventories exist for all the languages considered.

This paper introduces the motivation for and design of the Collaborative InterLingual Index (CILI). It is designed to make possible coordination between multiple loosely coupled wordnet projects. The structure of the CILI is based on the Interlingual index first proposed in the EuroWordNet project with several pragmatic extensions: an explicit open license, definitions in English and links to wordnets in the Global Wordnet Grid.

YARN (Yet Another RussNet), a project started in 2013, aims at creating a large open WordNet-like thesaurus for Russian by means of crowdsourcing. The first stage of the project was to create noun synsets. Currently, the resource comprises 48K+ word entries and 44K+ synsets. More than 200 people have taken part in assembling synsets throughout the project. The paper describes the linguistic, technical, and organizational principles of the project, as well as the evaluation results, lessons learned, and the future plans.

We describe the implementation of a short answer extraction system. It consists of a simple sentence selection front-end and a two phase approach to answer extraction from a sentence. In the first phase sentence classification is performed with a classifier trained with the passive aggressive algorithm utilizing the UIUC dataset and taxonomy and a feature set including word vectors. This phase outperforms the current best published results on that dataset. In the second phase, a sieve algorithm consisting of a series of increasingly general extraction rules is applied, using WordNet to find word types aligned with the UIUC classifications determined in the first phase. Some very preliminary performance metrics are presented.

Semantic relations between words are key to building systems that aim to understand and manipulate language. For English, the “de facto” standard for representing this kind of knowledge is Princeton’s WordNet. Here, we describe the wordnet-like resources currently available for Portuguese: their origins, methods of creation, sizes, and usage restrictions. We start tackling the problem of comparing them, but only in quantitative terms. Finally, we sketch ideas for potential collaboration between some of the projects that produce Portuguese wordnets.

In the context of a student software project we are investigating the use of WordNet for improving the automatic detection and classification of actors (or characters) mentioned in folktales. Our starting point is the book “Classification of International Folktales”, out of which we extract text segments that name the different actors involved in tales, taking advantage of patterns used by its author, Hans-Jo ̈rg Uther. We apply on those text segments functions that are implemented in the NLTK interface to WordNet in order to obtain lexical semantic information to enrich the original naming of characters proposed in the “Classification of International Folktales” and to support their translation in other languages.

In this paper, we present methods of extraction of multi-word lexical units (MWLUs) from large text corpora and their description in plWordNet 3.0. MWLUs are filtered from collocations of the structural type Noun+Adjective (NA).

This paper aims at a morpho-semantic analysis of 2461 Persian derived nouns, documented in FarsNet addressing computational codification via formulating specific morpho-semantic relations between classes of derived nouns and their bases. Considering the ultimate aim of the study, FarsNet derived nouns included 12 most productive suffixes have been analysed and as a consequence 45 morpho-semantic patterns were distinguished leading to creation of 17 morpho-semantic relations. The approach includes a close examination of beginners, grammatical category and part of speech shifts of bases undergoing the derivation process. In this research the morpho-semantic relations are considered at the word level and not at the synset level which will represent a cross-lingual validity, even if the morphological aspect of the relation is not the same in the studied languages. The resulting morpho-semantic formulations notably increase linguistic and operative competence and performance of FarsNet while is considered an achievement in Persian descriptive morphology and its codification.

We present a methodology for building lexical sets for argument slots of Italian verbs. We start from an inventory of semantically typed Italian verb frames and through a mapping to WordNet we automatically annotate the sets of fillers for the argument positions in a corpus of sentences. We evaluate both a baseline algorithm and a syntax driven algorithm and show that the latter performs significantly better in terms of precision.

WordNet represents polysemous terms by capturing the different meanings of these terms at the lexical level, but without giving emphasis on the polysemy types such terms belong to. The state of the art polysemy approaches identify several polysemy types in WordNet but they do not explain how to classify and organize them. In this paper, we present a novel approach for classifying the polysemy types which exploits taxonomic principles which in turn, allow us to discover a set of polysemy structural patterns.

Although there are currently several versions of Princeton WordNet for different languages, the lack of development of some of these versions does not make it possible to use them in different Natural Language Processing applications. So is the case of the Spanish Wordnet contained in the Multilingual Central Repository (MCR), which we tried unsuccessfully to incorporate into an anaphora resolution application and also in search terms expansion. In this situation, different strategies to improve MCR Spanish WordNet coverage were put forward and tested, obtaining encouraging results. A specific process was conducted to increase the number of adverbs, and a few simple processes were applied which made it possible to increase, at a very low cost, the number of terms in the Spanish WordNet. Finally, a more complex method based on distributional semantics was proposed, using the relations between English Wordnet synsets, also returning positive results.

While gender identities in the Western world are typically regarded as binary, our previous work (Hicks et al., 2015) shows that there is more lexical variety of gender identity and the way people identify their gender. There is also a growing need to lexically represent this variety of gender identities. In our previous work, we developed a set of tools and approaches for analyzing Twitter data as a basis for generating hypotheses on language used to identify gender and discuss gender-related issues across geographic regions and population groups in the U.S.A. In this paper we analyze the coverage and relative frequency of the word forms in our Twitter analysis with respect to the National Transgender Discrimination Survey data set, one of the most comprehensive data sets on transgender, gender non-conforming, and gender variant people in the U.S.A. We then analyze the coverage of WordNet, a widely used lexical database, with respect to these identities and discuss some key considerations and next steps for adding gender identity words and their meanings to WordNet.

Here we report the construction of a wordnet for Mansi, an endangered minority language spoken in Russia. We will pay special attention to challenges that we encountered during the building process, among which the most important ones are the low number of native speakers, the lack of thesauri and the bear language. We will discuss our solutions to these issues, which might have some theoretical implications for the methodology of wordnet building in general.

This paper presents a standalone spell corrector, WNSpell, based on and written for WordNet. It is aimed at generating the best possible suggestion for a mistyped query but can also serve as an all-purpose spell corrector. The spell corrector consists of a standard initial correction system, which evaluates word entries using a multifaceted approach to achieve the best results, and a semantic recognition system, wherein given a related word input, the system will adjust the spelling suggestions accordingly. Both feature significant performance improvements over current context-free spell correctors.

India is a country with 22 officially recognized languages and 17 of these have WordNets, a crucial resource. Web browser based interfaces are available for these WordNets, but are not suited for mobile devices which deters people from effectively using this resource. We present our initial work on developing mobile applications and browser extensions to access WordNets for Indian Languages. Our contribution is two fold: (1) We develop mobile applications for the Android, iOS and Windows Phone OS platforms for Hindi, Marathi and Sanskrit WordNets which allow users to search for words and obtain more information along with their translations in English and other Indian languages. (2) We also develop browser extensions for English, Hindi, Marathi, and Sanskrit WordNets, for both Mozilla Firefox, and Google Chrome. We believe that such applications can be quite helpful in a classroom scenario, where students would be able to access the WordNets as dictionaries as well as lexical knowledge bases. This can help in overcoming the language barrier along with furthering language understanding.

WordNet has proved to be immensely useful for Word Sense Disambiguation, and thence Machine translation, Information Retrieval and Question Answering. It can also be used as a dictionary for educational purposes. The semantic nature of concepts in a WordNet motivates one to try to express this meaning in a more visual way. In this paper, we describe our work of enriching IndoWordNet with image acquisitions from the OpenClipArt library. We describe an approach used to enrich WordNets for eighteen Indian languages. Our contribution is three fold: (1) We develop a system, which, given a synset in English, finds an appropriate image for the synset. The system uses the OpenclipArt library (OCAL) to retrieve images and ranks them. (2) After retrieving the images, we map the results along with the linkages between Princeton WordNet and Hindi WordNet, to link several synsets to corresponding images. We choose and sort top three images based on our ranking heuristic per synset. (3) We develop a tool that allows a lexicographer to manually evaluate these images. The top images are shown to a lexicographer by the evaluation tool for the task of choosing the best image representation. The lexicographer also selects the number of relevant images. Using our system, we obtain an Average Precision (P @ 3) score of 0.30.

We propose the use of WordNet synsets in a syntax-based reordering model for hierarchical statistical machine translation (HPB-SMT) to enable the model to generalize to phrases not seen in the training data but that have equivalent meaning. We detail our methodology to incorporate synsets’ knowledge in the reordering model and evaluate the resulting WordNet-enhanced SMT systems on the English-to-Farsi language direction. The inclusion of synsets leads to the best BLEU score, outperforming the baseline (standard HPB-SMT) by 0.6 points absolute.

Collaboratively created lexical resources is a trending approach to creating high quality thesauri in a short time span at a remarkably low price. The key idea is to invite non-expert participants to express and share their knowledge with the aim of constructing a resource. However, this approach tends to be noisy and error-prone, thus making data cleansing a highly topical task to perform. In this paper, we study different techniques for synset deduplication including machine- and crowd-based ones. Eventually, we put forward an approach that can solve the deduplication problem fully automatically, with the quality comparable to the expert-based approach.

This paper presents a machine learning method for automatic identification and classification of morphosemantic relations (MSRs) between verb and noun synset pairs in the Bulgarian WordNet (BulNet). The core training data comprise 6,641 morphosemantically related verb–noun literal pairs from BulNet. The core dataset were preprocessed quality-wise by applying validation and reorganisation procedures. Further, the data were supplemented with negative examples of literal pairs not linked by an MSR. The designed supervised machine learning method uses the RandomTree algorithm and is implemented in Java with the Weka package. A set of experiments were performed to test various approaches to the task. Future work on improving the classifier includes adding more training data, employing more features, and fine-tuning. Apart from the language specific information about derivational processes, the proposed method is language independent.

Many new wordnets in the world are constantly created and most take the original Princeton WordNet (PWN) as their starting point. This arguably central position imposes a responsibility on PWN to ensure that its structure is clean and consistent. To validate PWN hierarchical structures we propose the application of a system of test patterns. In this paper, we report on how to validate the PWN hierarchies using the system of test patterns. In sum, test patterns provide lexicographers with a very powerful tool, which we hope will be adopted by the global wordnet community.

New concepts and semantic relations are constantly added to Estonian Wordnet (EstWN) to increase its size. In addition to this, with the use of test patterns, the validation of EstWN hierarchies is also performed. This parallel work was carried out over the past four years (2011-2014) with 10 different EstWN versions (60-70). This has been a collaboration between the creators of test patterns and the lexicographers currently working on EstWN. This paper describes the usage of test patterns from the points of views of information scientists (the creators of test patterns) as well as the users (lexicographers). Using EstWN as an example, we illustrate how the continuous use of test patterns has led to significant improvement of the semantic hierarchies in EstWN.

In promoting a multilingual South Africa, the government is encouraging people to speak more than one language. In order to comply with this initiative, people choose to learn the languages which they do not speak as home language. The African languages are mostly chosen because they are spoken by the majority of the country’s population. Most words in these languages have many possible senses. This phenomenon tends to pose problems to people who want to learn these languages. This article argues that the African WordNet may the best tool to address the problem of sense discrimination. The focus of the argument will be on the primary sense of the word ‘hand’, which is part of the body, as lexicalized in three indigenous languages spoken in South Africa, namely, Tshivenḓa, Sesotho sa Leboa and isiZulu. A brief historical background of the African WordNet will be provided, followed by the definition of the word ‘hand’ in the three languages and the analysis of the word in context. Lastly, the primary sense of the word ‘hand’ across the three languages will be discussed.

In this article we present an expansion of the supersense inventory. All new super-senses are extensions of members of the current inventory, which we postulate by identifying semantically coherent groups of synsets. We cover the expansion of the already-established supernsense inventory for nouns and verbs, the addition of coarse supersenses for adjectives in absence of a canonical supersense inventory, and super-senses for verbal satellites. We evaluate the viability of the new senses examining the annotation agreement, frequency and co-ocurrence patterns.

Adverbs are seldom well represented in wordnets. Princeton WordNet, for example, derives from adjectives practically all its adverbs and whatever involvement they have. GermaNet stays away from this part of speech. Adverbs in plWordNet will be emphatically present in all their semantic and syntactic distinctness. We briefly discuss the linguistic background of the lexical system of Polish adverbs. We describe an automated generator of accurate candidate adverbs, and introduce the lexicographic procedures which will ensure high consistency of wordnet editors’ decisions about adverbs.

The aim of this paper is to show a language-independent process of creating a new semantic relation between adjectives and nouns in wordnets. The existence of such a relation is expected to improve the detection of figurative language and sentiment analysis (SA). The proposed method uses an annotated corpus to explore the semantic knowledge contained in linguistic constructs performing as the rhetorical figure Simile. Based on the frequency of occurrence of similes in an annotated corpus, we propose a new relation, which connects the noun synset with the synset of an adjective representing that noun’s specific attribute. We elaborate on adding this new relation in the case of the Serbian WordNet (SWN). The proposed method is evaluated by human judgement in order to determine the relevance of automatically selected relation items. The evaluation has shown that 84% of the automatically selected and the most frequent linguistic constructs, whose frequency threshold was equal to 3, were also selected by humans.

This paper describes our attempts to add Indonesian definitions to synsets in the Wordnet Bahasa (Nurril Hirfana Mohamed Noor et al., 2011; Bond et al., 2014), to extract semantic relations between lemmas and definitions for nouns and verbs, such as synonym, hyponym, hypernym and instance hypernym, and to generally improve Wordnet. The original, somewhat noisy, definitions for Indonesian came from the Asian Wordnet project (Riza et al., 2010). The basic method of extracting the relations is based on Bond et al. (2004). Before the relations can be extracted, the definitions were cleaned up and tokenized. We found that the definitions cannot be completely cleaned up because of many misspellings and bad translations. However, we could identify four semantic relations in 57.10% of noun and verb definitions. For the remaining 42.90%, we propose to add 149 new Indonesian lemmas and make some improvements to Wordnet Bahasa and Wordnet in general.

This paper presents a linguistic account of the lexical semantics of body parts in African WordNet, with special reference to Northern Sotho. It focuses on external human body parts synsets in Northern Sotho. The paper seeks to support the effectiveness of African WordNet as a resource for services such as in the healthcare and medical field in South Africa. It transpired from this exploration that there is either a one-to-one correspondence or some form of misalignment of lexicalisation with regard to the sample of examined synsets. The paper concludes by making suggestions on how African WordNet can deal with such semantic misalignments in order to improve its efficiency as a resource for the targeted purpose.

In order to overcome the lack of medical corpora, we have developed a WordNet for Medical Events (WME) for identifying medical terms and their sense related information using a seed list. The initial WME resource contains 1654 medical terms or concepts. In the present research, we have reported the enhancement of WME with 6415 number of medical concepts along with their conceptual features viz. Parts-of-Speech (POS), gloss, semantics, polarity, sense and affinity. Several polarity lexicons viz. SentiWordNet, SenticNet, Bing Liu’s subjectivity list and Taboda’s adjective list were introduced with WordNet synonyms and hyponyms for expansion. The semantics feature guided us to build a semantic co-reference relation based network between the related medical concepts. These features help to prepare a medical concept network for better sense relation based visualization. Finally, we evaluated with respect to Adaptive Lesk Algorithm and conducted an agreement analysis for validating the expanded WME resource.

In languages such as Chinese, classifiers (CLs) play a central role in the quantification of noun-phrases. This can be a problem when generating text from input that does not specify the classifier, as in machine translation (MT) from English to Chinese. Many solutions to this problem rely on dictionaries of noun-CL pairs. However, there is no open large-scale machine-tractable dictionary of noun-CL associations. Many published resources exist, but they tend to focus on how a CL is used (e.g. what kinds of nouns can be used with it, or what features seem to be selected by each CL). In fact, since nouns are open class words, producing an exhaustive definite list of noun-CL associations is not possible, since it would quickly get out of date. Our work tries to address this problem by providing an algorithm for automatic building of a frequency based dictionary of noun-CL pairs, mapped to concepts in the Chinese Open Wordnet (Wang and Bond, 2013), an open machine-tractable dictionary for Chinese. All results will released under an open license.

WordNet plays a significant role in Linked Open Data (LOD) cloud. It has numerous application ranging from ontology annotation to ontology mapping. IndoWordNet is a linked WordNet connecting 18 Indian language WordNets with Hindi as a source WordNet. The Hindi WordNet was initially developed by linking it to English WordNet. In this paper, we present a data representation of IndoWordNet in Web Ontology Language (OWL). The schema of Princeton WordNet has been enhanced to support the representation of IndoWordNet. This IndoWordNet representation in OWL format is now available to link other web resources. This representation is implemented for eight Indian languages.

Wordnets play an important role not only in linguistics but also in natural language processing (NLP). This paper reports major results of a project which aims to construct a wordnet for Vietnamese language. We propose a two-phase approach to the construction of Vietnamese WordNet employing available language resources and ensuring Vietnamese specific linguistic and cultural characteristics. We also give statistical results and analyses to show characteristics of the wordnet.

In this paper we present an extension of the dictionary-based strategy for wordnet construction implemented in the WN-Toolkit. This strategy allows the extraction of information for polysemous English words if definitions and/or semantic relations are present in the dictionary. The WN-Toolkit is a freely available set of programs for the creation and expansion of wordnets using dictionary-based and parallel-corpus based strategies. In previous versions of the toolkit the dictionary-based strategy was only used for translating monosemous English variants. In the experiments we have used Omegawiki and Wiktionary and we present automatic evaluation results for 24 languages that have wordnets in the Open Multilingual Wordnet project. We have used these existing versions of the wordnet to perform an automatic evaluation.

This paper describes a language- independent LESK based approach to Word Sense Disambiguation (WSD), involving also Vector Space Models applied to the Distributional Semantics Hypotesis. In particular this approach tries to solve some issues that come up with less-resourced languages. The approach also addresses the inadequacy of the Most Frequent Sense (MFS) heuristics to fit specific domain corpora.

The paper explores the application of plWordNet, a very large wordnet of Polish, in weakly supervised Word Sense Disambiguation (WSD). Because plWordNet provides only partial descriptions by glosses and usage examples, and does not include sense-disambiguated glosses, PageRank-based WSD methods perform slightly worse than for English. However, we show that the use of weights for the relation types and the order in which lexical units have been added for sense re-ranking can significantly improve WSD precision. The evaluation was done on two Polish corpora (KPWr and Składnica) including manual WSD. We discuss the fundamental difference in the construction of both corpora and very different test results.

It took us nearly ten years to get from no wordnet for Polish to the largest wordnet ever built. We started small but quickly learned to dream big. Now we are about to release plWordNet 3.0-emo – complete with sentiment and emotions annotated – and a domestic version of Princeton WordNet, larger than WordNet 3.1 by nearly ten thousand newly added words. The paper retraces the road we travelled and talks a little about the future.

We describe Open Dutch WordNet, which has been derived from the Cornetto database, the Princeton WordNet and open source resources. We exploited existing equivalence relations between Cornetto synsets and WordNet synsets in order to move the open source content from Cornetto into WordNet synsets. Currently, Open Dutch Wordnet contains 117,914 synsets, of which 51,588 synsets contain at least one Dutch synonym, which leaves 66,326 synsets still to obtain a Dutch synonym. The average polysemy is 1.5. The resource is currently delivered in XML under the CC BY-SA 4.0 license1 and it has been linked to the Global Wordnet Grid. In order to use the resource, we refer to: https: //github.com/MartenPostma/OpenDutchWordnet.

This paper presents our first attempt at verifying integrity constraints of our openWordnet-PT against the ontology for Wordnets encoding. Our wordnet is distributed in Resource Description Format (RDF) and we want to guarantee not only the syntax correctness but also its semantics soundness.

The semantic network editor DEBVisDic has been used by different development teams to create more than 20 national wordnets. The editor was recently re-developed as a multi-platform web-based application for general semantic networks editing. One of the main advantages, when compared to the previous implementation, lies in the fact that no client-side installation is needed now. Following the successful first phase in building the Open Dutch Wordnet, DEBVisDic was extended with features that allow users to easily create, edit, and share a new (usually national) wordnet without the need of any complicated configuration or advanced technical skills. The DEBVisDic editor provides advanced features for wordnet browsing, editing, and visualization. Apart from the user-friendly web-based application, DEBVisDic also provides an API interface to integrate the semantic network data into external applications.

Samāsa or compounds are a regular feature of Indian Languages. They are also found in other languages like German, Italian, French, Russian, Spanish, etc. Compound word is constructed from two or more words to form a single word. The meaning of this word is derived from each of the individual words of the compound. To develop a system to generate, identify and interpret compounds, is an important task in Natural Language Processing. This paper introduces a web based tool - Samāsa-Kartā for producing compound words. Here, the focus is on Sanskrit language due to its richness in usage of compounds; however, this approach can be applied to any Indian language as well as other languages. IndoWordNet is used as a resource for words to be compounded. The motivation behind creating compound words is to create, to improve the vocabulary, to reduce sense ambiguity, etc. in order to enrich the WordNet. The Samāsa-Kartā can be used for various applications viz., compound categorization, sandhi creation, morphological analysis, paraphrasing, synset creation, etc.

The Arabic WordNet project has provided the Arabic Natural Language Processing (NLP) community with the first WordNet-compliant resource. It allowed new possibilities in terms of building sophisticated NLP applications related to this Semitic language. In this paper, we present the new content added to this resource, using semi-automatic techniques, and validated by Arabic native-speaker lexicographers. We also present how this content helps in the implementation of new Arabic NLP applications, especially for Question Answering (QA) systems. The obtained results show the contribution of the added content. The resource, fully transformed into the standard Lexical Markup Framework (LMF), is made available for the community.

This paper presents a web interface for wordnets named Hydra for Web which is built on top of Hydra – an open source tool for wordnet development – by means of modern web technologies. It is a Single Page Application with simple but powerful and convenient GUI. It has two modes for visualisation of the language correspondences of searched (and found) wordnet synsets – single and parallel modes. Hydra for web is available at: http://dcl.bas.bg/bulnet/.

This paper presents the results of large-scale noun synset mapping between plWordNet, the wordnet of Polish, and Princeton WordNet, the wordnet of English, which have shown high predominance of inter-lingual hyponymy relation over inter-synonymy relation. Two main sources of such effect are identified in the paper: differences in the methodologies of construction of plWN and PWN and cross-linguistic differences in lexicalization of concepts and grammatical categories between English and Polish. Next, we propose a typology of specific gaps and mismatches across wordnets and a rule-based system of filters developed specifically to scan all I(inter-lingual)-hyponymy links between plWN and PWN. The proposed system, it should be stressed, also enables one to pinpoint the frequencies of the identified gaps and mismatches.

This paper presents a method to compute similarity of folktales based on conceptual overlap at various levels of abstraction as defined in Dutch WordNet. The method is applied on a corpus of Dutch folktales and evaluated using a comparison to traditional folktale similarity analysis based on the Aarne–Thompson–Uther (ATU) classification system. Document similarity computed by the presented method is in agreement with traditional analysis for a certain amount of folktale pairs, but differs for other pairs. However, it can be argued that the current approach computes an alternative, data-driven type of similarity. Using WordNet instead of a domain-specific ontology or classification system ensures applicability of the method outside of the folktale domain.

This paper presents the Event and Implied Situation Ontology (ESO), a resource which formalizes the pre and post situations of events and the roles of the entities affected by an event. The ontology reuses and maps across existing resources such as WordNet, SUMO, VerbNet, PropBank and FrameNet. We describe how ESO is injected into a new version of the Predicate Matrix and illustrate how these resources are used to detect information in large document collections that otherwise would have remained implicit. The model targets interpretations of situations rather than the semantics of verbs per se. The event is interpreted as a situation using RDF taking all event components into account. Hence, the ontology and the linked resources need to be considered from the perspective of this interpretation model.

We present preliminary work on the mapping of WordNet 3.0 to the Basic Formal Ontology (BFO 2.0). WordNet is a large, widely used semantic network. BFO is a domain-neutral upper-level ontology that represents the types of things that exist in the world and relations between them. BFO serves as an integration hub for more specific ontologies, such as the Ontology for Biomedical Investigations (OBI) and Ontology for Biobanking (OBIB). This work aims at creating a lexico-semantic resource that can be used in NLP tools to perform ontology-related text manipulation tasks. Our semi-automatic mapping method consists in using existing mappings between WordNet and the KYOTO Ontology. The latter allows machines to reason over texts by providing interpretations of the words in ontological terms. Our working hypothesis is that a large portion of WordNet synsets can be semi-automatically mapped to BFO using simple mapping rules from KYOTO to BFO. We evaluate the method on a randomized subset of synsets, examine preliminary results, and discuss challenges related to the method. We conclude with suggestions for future work.

This paper discusses the semantic augmentation of FarsNet - the Persian WordNet - with new relations and structures for verbs. FarsNet1.0, the first Persian WordNet obeys the Structure of Princeton WordNet 2.1. In this paper we discuss FarsNet 2.0 in which new inter-POS relations and verb frames are added. In fact FarsNet2.0 is a combination of WordNet and VerbNet for Persian. It includes more than 30,000 lexical entries arranged in about 20,000 synsets with about 18000 mappings to Princeton WordNet synsets. There ae about 43000 relations between synsets and senses in FarsNet 2.0. It includes verb frames in two levels (syntactic and thematic) for about 200 simple Persian verbs.

For fine-grained sentiment analysis, we need to go beyond zero-one polarity and find a way to compare adjectives (synonyms) that share the same sense. Choice of a word from a set of synonyms, provides a way to select the exact polarity-intensity. For example, choosing to describe a person as benevolent rather than kind1 changes the intensity of the expression. In this paper, we present a sense based lexical resource, where synonyms are assigned intensity levels, viz., high, medium and low. We show that the measure P (s|w) (probability of a sense s given the word w) can derive the intensity of a word within the sense. We observe a statistically significant positive correlation between P(s|w) and intensity of synonyms for three languages, viz., English, Marathi and Hindi. The average correlation scores are 0.47 for English, 0.56 for Marathi and 0.58 for Hindi.

In this paper we present an analysis of different semantic relations extracted from WordNet, Extended WordNet and SemCor, with respect to their role in the task of knowledge-based word sense disambiguation. The experiments use the same algorithm and the same test sets, but different variants of the knowledge graph. The results show that different sets of relations have different impact on the results: positive or negative. The beneficial ones are discussed with respect to the combination of relations and with respect to the test set. The inclusion of inference has only a modest impact on accuracy, while the addition of syntactic relations produces stable improvement over the baselines.

Detection of MultiWord Expressions (MWEs) is one of the fundamental problems in Natural Language Processing. In this paper, we focus on two categories of MWEs - Compound Nouns and Light Verb Constructions. These two categories can be tackled using knowledge bases, rather than pure statistics. We investigate usability of IndoWordNet for the detection of MWEs. Our IndoWordNet based approach uses semantic and ontological features of words that can be extracted from IndoWordNet. This approach has been tested on Indian languages viz., Assamese, Bengali, Hindi, Konkani, Marathi, Odia and Punjabi. Results show that ontological features are found to be very useful for the detection of light verb constructions, while use of semantic properties for the detection of compound nouns is found to be satisfactory. This approach can be easily adapted by other Indian languages. Detected MWEs can be interpolated into WordNets as they help in representing semantic knowledge.

This paper reports the work of creating bilingual mappings in English for certain synsets of Hindi wordnet, the need for doing this, the methods adopted and the tools created for the task. Hindi wordnet, which forms the foundation for other Indian language wordnets, has been linked to the English WordNet. To maximize linkages, an important strategy of using direct and hypernymy linkages has been followed. However, the hypernymy linkages were found to be inadequate in certain cases and posed a challenge due to sense granularity of language. Thus, the idea of creating bilingual mappings was adopted as a solution. A bilingual mapping means a linkage between a concept in two different languages, with the help of translation and/or transliteration. Such mappings retain meaningful representations, while capturing semantic similarity at the same time. This has also proven to be a great enhancement of Hindi wordnet and can be a crucial resource for multilingual applications in natural language processing, including machine translation and cross language information retrieval.

Le and Fokkens (2015) recently showed that taxonomy-based approaches are more reliable than corpus-based approaches in estimating human similarity ratings. On the other hand, distributional models provide much better coverage. The lack of an established similarity metric for adjectives in WordNet is a case in point. I present initial work to establish such a metric, and propose ways to move forward by looking at extensions to WordNet. I show that the shortest path distance between derivationally related forms provides a reliable estimate of adjective similarity. Furthermore, I find that a hybrid method combining this measure with vector-based similarity estimations gives us the best of both worlds: more reliable similarity estimations than vectors alone, but with the same coverage as corpus-based methods.

In this paper, we describe a new and improved Global Wordnet Grid that takes advantage of the Collaborative InterLingual Index (CILI). Currently, the Open Multilingal Wordnet has made many wordnets accessible as a single linked wordnet, but as it used the Princeton Wordnet of English (PWN) as a pivot, it loses concepts that are not part of PWN. The technical solution to this, a central registry of concepts, as proposed in the EuroWordnet project through the InterLingual Index, has been known for many years. However, the practical issues of how to host this index and who decides what goes in remained unsolved. Inspired by current practice in the Semantic Web and the Linked Open Data community, we propose a way to solve this issue. In this paper we define the principles and protocols for contributing to the Grid. We tested them on two use cases, adding version 3.1 of the Princeton WordNet to a CILI based on 3.0 and adding the Open Dutch Wordnet, to validate the current set up. This paper aims to be a call for action that we hope will be further discussed and ultimately taken up by the whole wordnet community.

Writing intended to inform frequently contains references to document entities (DEs), a mixed class that includes orthographically structured items (e.g., illustrations, sections, lists) and discourse entities (arguments, suggestions, points). Such references are vital to the interpretation of documents, but they often eschew identifiers such as “Figure 1” for inexplicit phrases like “in this figure” or “from these premises”. We examine inexplicit references to DEs, termed DE references, and recast the problem of their automatic detection into the determination of relevant word senses. We then show the feasibility of machine learning for the detection of DE-relevant word senses, using a corpus of human-labeled synsets from WordNet. We test cross-domain performance by gathering lemmas and synsets from three corpora: website privacy policies, Wikipedia articles, and Wikibooks textbooks. Identifying DE references will enable language technologies to use the information encoded by them, permitting the automatic generation of finely-tuned descriptions of DEs and the presentation of richly-structured information to readers.

For humans the main functions of a dictionary is to store information concerning words and to reveal it when needed. While readers are interested in the meaning of words, writers look for answers concerning usage, spelling, grammar or word forms (lemma). We will focus here on this latter task: help authors to find the word they are looking for, word they may know but whose form is eluding them. Put differently, we try to build a resource helping authors to overcome the tip-of-the-tongue problem (ToT). Obviously, in order to access a word, it must be stored somewhere (brain, resource). Yet this is by no means sufficient. We will illustrate this here by comparing WordNet (WN) to an equivalent lexical resource bootstrapped from Wikipedia (WiPi). Both may contain a given word, but ease and success of access may be different depending on other factors like quality of the query, proximity, type of connections, etc. Next we will show under what conditions WN is suitable for word access, and finally we will present a roadmap showing the obstacles to be overcome to build a resource allowing the text producer to find the word s/he is looking for.