pwc-archive/papers-with-abstracts huggingface dataset:
Two experiments for embedding Wordnet hierarchy into vector spaces
Authors: Jean-Philippe Bernardy, Aleksandre Maskharashvili
Date: 2019-07-01 00:00:00
Proceeding: GWC 2019 7
Tasks: Vocal Bursts Valence Prediction

In this paper, we investigate mapping of the WORDNET hyponymy relation to feature vectors. Our aim is to model lexical knowledge in such a way that it can be used as input in generic machine-learning models, such as phrase entailment predictors. We propose two models. The first one leverages an existing mapping of words to feature vectors (fastText), and attempts to classify such vectors as within or outside of each class. The second model is fully supervised, using solely WORDNET as a ground truth. It maps each concept to an interval or a disjunction thereof. The first model approaches but not quite attain state of the art performance. The second model can achieve near-perfect accuracy.
Towards interpretable, data-derived distributional meaning representations for reasoning: A dataset of properties and concepts
Authors: Pia Sommerauer, Antske Fokkens, Piek Vossen
Date: 2019-07-01 00:00:00
Proceeding: GWC 2019 7

This paper proposes a framework for investigating which types of semantic properties are represented by distributional data. The core of our framework consists of relations between concepts and properties. We provide hypotheses on which properties are reflected in distributional data or not based on the type of relation. We outline strategies for creating a dataset of positive and negative examples for various semantic properties, which cannot easily be separated on the basis of general similarity (e.g. fly: seagull, penguin). This way, a distributional model can only distinguish between positive and negative examples through evidence for a target property. Once completed, this dataset can be used to test our hypotheses and work towards data-derived interpretable representations.
Connections between the semantic layer of Walenty valency dictionary and PlWordNet
Authors: Elzbieta Hajnicz, Tomasz Bartosiak
Date: 2019-07-01 00:00:00
Proceeding: GWC 2019 7

In this paper we discuss how Walenty is using PLWORDNET to represent semantic information. We decided to use PLWORDNET lexical units and synsets to describe both the predicate meaning and the semantic fields of its arguments. The original design decision required some further refinement caused by the structure of PLWORDNET and complex relations between arguments.
Estimating senses with sets of lexically related words for Polish word sense disambiguation
Authors: Szymon Rutkowski, Piotr Rychlik, Agnieszka Mykowiecka
Date: 2019-07-01 00:00:00
Proceeding: GWC 2019 7
Tasks: Language Modeling, Language Modelling, Word Sense Disambiguation

We propose a new algorithm for word sense disambiguation, exploiting data from a WordNet with many types of lexical relations, such as plWordNet for Polish. In this method, sense probabilities in context are approximated with a language model. To estimate the likelihood of a sense appearing amidst the word sequence, the token being disambiguated is substituted with words related lexically to the given sense or words appearing in its WordNet gloss. We test this approach on a set of sense-annotated Polish sentences with a number of neural language models. Our best setup achieves the accuracy score of 55.12% (72.02% when first senses are excluded), up from 51.77% of an existing PageRank-based method. While not exceeding the first (often meaning most frequent) sense baseline in the standard case, this encourages further research on combining WordNet data with neural models.
Merging DanNet with Princeton Wordnet
Authors: Bolette Sandford Pedersen, Sanni Nimb, Ida Rørmann Olsen, Sussi Olsen
Date: 2019-07-01 00:00:00
Proceeding: GWC 2019 7

In this paper we describe the merge of the Danish wordnet, DanNet, with Princeton Wordnet applying a two-step approach. We first link from the English Princeton core to Danish (5,000 base concepts) and then proceed to linking the rest of the Danish vocabulary to English, thus going from Danish to English. Since the Danish wordnet is built bottom-up from Danish lexica and corpora, all taxonomies are monolingually based and thus not necessarily directly compatible with the coverage and structure of the Princeton WordNet. This fact proves to pose some challenges to the linking procedure since a considerable number of the links cannot be realised via the preferred cross-language synonym link which implies a more or less precise correlation between the two concepts. Instead, a subpart of the links are realised through near synonym or hyponymy links to compensate for the fact that no precise translation can be found in the target resource. The tool WordnetLoom is currently used for manual linking but procedures for a more automatic procedure in future is discussed. We conclude that the two resources actually differ from each other quite more than expected, both vocabulary and structure-wise.
Development of Assamese Rule based Stemmer using WordNet
Authors: Jumi Sarmah, Shikhar Kumar Sarma, Anup Kumar Barman
Date: 2019-07-01 00:00:00
Proceeding: GWC 2019 7

Stemming is a technique that reduces any inflected word to its root form. Assamese is a morphologically rich, scheduled Indian language. There are various forms of suffixes applied to a word in various contexts. Such inflected words if normalized will help improve the performance of various Natural Language Processing applications. This paper basically tries to develop a Look-up and rule-based suffix stripping approach for the Assamese language using WordNet. The authors prepare the dictionary with the root words extracted from Assamese WordNet and Named Entities. Appropriate stemming rules for the inflected nouns, verbs have been set to the rule engine and later tested the stemmed output with the morphological root words of Assamese WordNet and Named Entities by computing hamming distance. This developed stemmer for the Assamese language achieves accuracy of 85%. Also, the authors reported the IR system’s performance on applying the Assamese stemmer and proved its efficiency by retrieving sense oriented results based on the fired query. Thus, Morphological Analyzer will embark the research wing for developing various Assamese NLP applications.
Synthetic, yet natural: Properties of WordNet random walk corpora and the impact of rare words on embedding performance
Authors: Filip Klubička, Alfredo Maldonado, Abhijit Mahalunkar, John Kelleher
Date: 2019-07-01 00:00:00
Proceeding: GWC 2019 7
Tasks: Word Embeddings, Word Similarity

Creating word embeddings that reflect semantic relationships encoded in lexical knowledge resources is an open challenge. One approach is to use a random walk over a knowledge graph to generate a pseudo-corpus and use this corpus to train embeddings. However, the effect of the shape of the knowledge graph on the generated pseudo-corpora, and on the resulting word embeddings, has not been studied. To explore this, we use English WordNet, constrained to the taxonomic (tree-like) portion of the graph, as a case study. We investigate the properties of the generated pseudo-corpora, and their impact on the resulting embeddings. We find that the distributions in the psuedo-corpora exhibit properties found in natural corpora, such as Zipf’s and Heaps’ law, and also observe that the proportion of rare words in a pseudo-corpus affects the performance of its embeddings on word similarity.
Augmenting Chinese WordNet semantic relations with contextualized embeddings
Authors: Yu-Hsiang Tseng, Shu-Kai Hsieh
Date: 2019-07-01 00:00:00
Proceeding: GWC 2019 7

Constructing semantic relations in WordNet has been a labour-intensive task, especially in a dynamic and fast-changing language environment. Combined with recent advancements of contextualized embeddings, this paper proposes the concept of morphology-guided sense vectors, which can be used to semi-automatically augment semantic relations in Chinese Wordnet (CWN). This paper (1) built sense vectors with pre-trained contextualized embedding models; (2) demonstrated the sense vectors computed were consistent with the sense distinctions made in CWN; and (3) predicted the potential semantically-related sense pairs with high accuracy by sense vectors model.
Visualising WordNet Embeddings: some preliminary results
Authors: Csaba Veres
Date: 2019-07-01 00:00:00
Proceeding: GWC 2019 7
Tasks: Semantic Similarity, Semantic Textual Similarity, Word Embeddings

AutoExtend is a method for learning unambiguous vector embeddings for word senses. We visualise these word embeddings with t-SNE, which further compresses the vectors to the x,y plane. We show that the t-SNE co-ordinates can be used to reveal interesting semantic relations between word senses, and propose a new method that uses the simple x,y coordinates to compute semantic similarity. This can be used to propose new links and alterations to existing ones in WordNet. We plan to add this approach to the existing toolbox of methods in an attempt to understand learned semantic relations in word embeddings.
The Making of Coptic Wordnet
Authors: Laura Slaughter, Luis Morgado Da Costa, So Miyagawa, Marco Büchler, Amir Zeldes, Heike Behlmer
Date: 2019-07-01 00:00:00
Proceeding: GWC 2019 7

With the increasing availability of wordnets for ancient languages, such as Ancient Greek and Latin, gaps remain in the coverage of less studied languages of antiquity. This paper reports on the construction and evaluation of a new wordnet for Coptic, the language of Late Roman, Byzantine and Early Islamic Egypt in the first millenium CE. We present our approach to constructing the wordnet which uses multilingual Coptic dictionaries and wordnets for five different languages. We further discuss the results of this effort and outline our on-going/future work.
Evaluating the Wordnet and CoRoLa-based Word Embedding Vectors for Romanian as Resources in the Task of Microworlds Lexicon Expansion
Authors: Elena Irimia, Maria Mitrofan, Verginica Mititelu
Date: 2019-07-01 00:00:00
Proceeding: GWC 2019 7

Within a larger frame of facilitating human-robot interaction, we present here the creation of a core vocabulary to be learned by a robot. It is extracted from two tokenised and lemmatized scenarios pertaining to two imagined microworlds in which the robot is supposed to play an assistive role. We also evaluate two resources for their utility for expanding this vocabulary so as to better cope with the robot’s communication needs. The language under study is Romanian and the resources used are the Romanian wordnet and word embedding vectors extracted from the large representative corpus of contemporary Romanian, CoRoLa. The evaluation is made for two situations: one in which the words are not semantically disambiguated before expanding the lexicon, and another one in which they are disambiguated with senses from the Romanian wordnet. The appropriateness of each resource is discussed.
Towards linking synonymous expressions of compound verbs to Japanese WordNet
Authors: Kyoko Kanzaki, Hitoshi Isahara
Date: 2019-07-01 00:00:00
Proceeding: GWC 2019 7

This paper describes our project on Japanese compound verbs. Japanese “Verb (adnominal form) + Verb” compounds, which are treated as single verbs, frequently appear in daily communication. They are not sufficiently registered in Japanese dictionaries or thesauri. We are now compiling a list of the synonymous expressions of compound verbs in “compound verb lexicon” built by the National Institute of Japanese Language and Linguistics. We extracted synonymous words and phrases of compound verbs from five hundred million Japanese web corpora. As a result, synonymous expressions of 1800 compound verbs were obtained automatically among 2700 in the “compound verb lexicon”. From our data, we observed that some compound verbs represent not only motion but also additional nuances such as an emotional one. In order to reflect the abundant meanings that compound verbs own, we will try to think of a link of synonymous expressions to Japanese wordnet. Concretely, in the case of synonymous phrases, we try to link adverbial expressions which are a part of phrases to the adverbial synset in Japanese wordnet.
Thinking globally, acting locally – Progress in the African Wordnet Project
Authors: Marissa Griesel, Sonja Bosch, Mampaka Lydia Mojapelo
Date: 2019-07-01 00:00:00
Proceeding: GWC 2019 7

The African Wordnet Project (AWN) includes all nine indigenous South African languages, namely isiZulu, isiXhosa, Setswana, Sesotho sa Leboa, Tshivenda, Siswati, Sesotho, isiNdebele and Xitsonga. The AWN currently includes 61 000 synsets as well as definitions and usage examples for a large part of the synsets. The project recently received extended funding from the South African Centre for Digital Language Resources (SADiLaR) and aims to update all aspects of the current resource, including the seed list used for new development, software tools used and mapping the AWN to the latest version of PWN 3.1. As with any resource development project, it is essential to also include phases of focused quality assurance and updating of the basis on which the resource is built. The African languages remain under-resourced. This paper describes progress made in the development of the AWN as well as recent technical improvements.
Building the Cantonese Wordnet
Authors: Joanna Ut-Seong Sio, Luis Morgado Da Costa
Date: 2019-07-01 00:00:00
Proceeding: GWC 2019 7

This paper reports on the development of the Cantonese Wordnet, a new wordnet project based on Hong Kong Cantonese. It is built using the expansion approach, leveraging on the existing Chinese Open Wordnet, and the Princeton Wordnet’s semantic hierarchy. The main goal of our project was to produce a high quality, human-curated resource – and this paper reports on the initial efforts and steady progress of our building method. It is our belief that the lexical data made available by this wordnet, including Jyutping romanization, will be useful for a variety of future uses, including many language processing tasks and linguistic research on Cantonese and its interactions with other Chinese dialects.
Deep Learning in Event Detection in Polish
Authors: Łukasz Kobyliński, Michał Wasiluk
Date: 2019-07-01 00:00:00
Proceeding: GWC 2019 7
Tasks: Deep Learning, Event Detection, Machine Translation, Question Answering, Translation

Event detection is an important NLP task that has been only recently tackled in the context of Polish, mostly due to lack of language resources. The available annotated corpora are still relatively small and supervised learning approaches are limited by the size of training datasets. Event detection tools are very much needed, as they can be used to annotate more language resources automatically and to improve the accuracy of other NLP tasks, which rely on the detection of events, such as question answering or machine translation. In this paper we present a deep learning based approach to this task, which proved to capture the knowledge contained in the training data most effectively and outperform previously proposed methods. We show a direct comparison to previously published results, using the same data and experimental setup.
Textual genre based approach to use WordNet in language-for-specific-purpose classroom as dictionary
Authors: Itziar Gonzalez-Dios
Date: 2019-07-01 00:00:00
Proceeding: GWC 2019 7

When teaching language for specific purposes (LSP) linguistic resources are needed to help students understand and write specialised texts. As building a lexical resource is costly, we explore the use of wordnets to represent the terms that can be found in particular textual domains. In order to gather the terms to be included in wordnets, we propose a textual genre approach, that leads us to introduce a new relation term used in to link all the possible terms/synsets that can appear in a text to the synset of the textual genre. This way, students can use wordnet as dictionary or thesaurus when writing specialised texts. We explain our approach by means of the logbooks and terms in Basque. A side effect of this works is also enriching the wordnets with new variants and synsets.
Fitting Semantic Relations to Word Embeddings
Authors: Eric Kafe
Date: 2019-07-01 00:00:00
Proceeding: GWC 2019 7
Tasks: Word Embeddings

We fit WordNet relations to word embeddings, using 3CosAvg and LRCos, two set-based methods for analogy resolution, and introduce 3CosWeight, a new, weighted variant of 3CosAvg. We test the performance of the resulting semantic vectors in lexicographic semantics tests, and show that none of the tested classifiers can learn symmetric relations like synonymy and antonymy, since the source and target words of these relations are the same set. By contrast, with the asymmetric relations (hyperonymy / hyponymy and meronymy), both 3CosAvg and LRCos clearly outperform the baseline in all cases, while 3CosWeight attained the best scores with hyponymy and meronymy, suggesting that this new method could provide a useful alternative to previous approaches.
Building the Mongolian WordNet
Authors: Khuyagbaatar Batsuren, Amarsanaa Ganbold, Altangerel Chagnaa, Fausto Giunchiglia
Date: 2019-07-01 00:00:00
Proceeding: GWC 2019 7

This paper presents the Mongolian Wordnet (MOW), and a general methodology of how to construct it from various sources e.g. lexical resources and expert translations. As of today, the MOW contains 23,665 synsets, 26,875 words, 2,979 glosses, and 213 examples. The manual evaluation of the resource1 estimated its quality at 96.4%.
English WordNet 2019 – An Open-Source WordNet for English
Authors: John P. McCrae, Alexandre Rademaker, Francis Bond, Ewa Rudnicka, Christiane Fellbaum
Date: 2019-07-01 00:00:00
Proceeding: GWC 2019 7

We describe the release of a new wordnet for English based on the Princeton WordNet, but now developed under an open-source model. In particular, this version of WordNet, which we call English WordNet 2019, which has been developed by multiple people around the world through GitHub, fixes many errors in previous wordnets for English. We give some details of the changes that have been made in this version and give some perspectives about likely future changes that will be made as this project continues to evolve.
Assessing Wordnets with WordNet Embeddings
Authors: Ruben Branco, João Rodrigues, Chakaveh Saedi, António Branco
Date: 2019-07-01 00:00:00
Proceeding: GWC 2019 7
Tasks: Semantic Similarity, Semantic Textual Similarity, Word Embeddings

An effective conversion method was proposed in the literature to obtain a lexical semantic space from a lexical semantic graph, thus permitting to obtain WordNet embeddings from WordNets. In this paper, we propose the exploitation of this conversion methodology as the basis for the comparative assessment of WordNets: given two WordNets, their relative quality in terms of capturing the lexical semantics of a given language, can be assessed by (i) converting each WordNet into the corresponding semantic space (i.e. into WordNet embeddings), (ii) evaluating the resulting WordNet embeddings under the typical semantic similarity prediction task used to evaluate word embeddings in general; and (iii) comparing the performance in that task of the two word embeddings, extracted from the two WordNets. A better performance in that evaluation task results from the word embeddings that are better at capturing the semantic similarity of words, which, in turn, result from the WordNet that is of higher quality at capturing the semantics of words.
Spoken WordNet
Authors: Kishore Kashyap, Shikhar Kr Sarma, Kumari Sweta
Date: 2019-07-01 00:00:00
Proceeding: GWC 2019 7
Tasks: Information Retrieval, Retrieval, Sentence

WordNets have been used in a wide variety of applications, including in design and development of intelligent and human assisting systems. Although WordNet was initially developed as an online lexical database, (Miller, 1995 and Fellbaum, 1998) later developments have inspired using WordNet database as resources in NLP applications, Language Technology developments, and as sources of structured learned materials. This paper proposes, conceptualizes, designs, and develops a voice enabled information retrieval system, facilitating WordNet knowledge presentation in a spoken format, based on a spoken query. In practice, the work converts the WordNet resource into a structured voiced based knowledge extraction system, where a spoken query is processed in a pipeline, and then extracting the relevant WordNet resources, structuring through another process pipeline, and then presented in spoken format. Thus the system facilitates a speech interface to the existing WordNet and we named the system as “Spoken WordNet”. The system interacts with two interfaces, one designed and developed for Web, and the other as an App interface for smartphone. This is also a kind of restructuring the WordNet as a friendly version for visually challenged users. User can input query string in the form of spoken English sentence or word. Jaccard Similarity is calculated between the input sentence and the synset definitions. The one with highest similarity score is taken as the synset of interest among multiple available synsets. User is also prompted to choose a contextual synset, in case of ambiguities.
OntoLex as a possible Bridge between WordNets and full lexical Descriptions
Authors: Thierry Declerck, Melanie Siegel
Date: 2019-07-01 00:00:00
Proceeding: GWC 2019 7

In this paper we describe our current work on representing a recently created German lexical semantics resource in OntoLex-Lemon and in conformance with WordNet specifications. Besides presenting the representation effort, we show the utilization of OntoLex-Lemon to bridge from WordNet-like resources to full lexical descriptions and extend the coverage of WordNets to other types of lexical data, such as decomposition results, exemplified for German data, and inflectional phenomena, here outlined for English data.
Semi-automatic Annotation of Event Structure, Argument Structure, and Opposition Structure to WordNet by Using Event Structure Frame
Authors: Seohyun Im
Date: 2019-07-01 00:00:00
Proceeding: GWC 2019 7
Tasks: Sentence

In this paper, we present semi-automatic annotation of the Event Structure Frames to synsets of English verbs in WordNet. The Event Structure Frame is a sub-eventual structure frame which combines event structure (lexical aspect) with argument structure represented by semantic roles and opposition structure which represents the presupposed and entailed sub-events of a matrix event. Our annotation work is done semi-automatically by GESL-based automatic annotation and manual error-correction. GESL is an automatic annotation tool of the Event Structure Frame to verbs in a sentence. We apply GESL to the example sentence given for each synset of a verb in WordNet. We expect that our work will make WordNet much more useful for any NLP and its applications which require lexical semantic information of English verbs.
Enhancing Conceptual Description through Resource Linking and Exploration of Semantic Relations
Authors: Ivelina Stoyanova, Svetlozara Leseva
Date: 2019-07-01 00:00:00
Proceeding: GWC 2019 7

The paper presents current efforts towards linking two large lexical semantic resources – WordNet and FrameNet – to the end of their mutual enrichment and the facilitation of the access, extraction and analysis of various types of semantic and syntactic information. In the second part of the paper, we go on to examine the relation of inheritance and other semantic relations as represented in WordNet and FrameNet and how they correspond to each other when the resources are aligned. We discuss the implications with respect to the enhancement of the two resources through the definition of new relations and the detailisation of conceptual frames.