pwc-archive/papers-with-abstracts huggingface dataset:
Distant Supervision for Relation Extraction with Multi-sense Word Embedding
Authors: Sangha Nam, Kijong Han, Eun-Kyung Kim, Key-Sun Choi
Date: 2018-01-01 00:00:00
Proceeding: GWC 2018 1
Tasks: Relation, Relation Extraction, Word Sense Disambiguation

Distant supervision can automatically generate labeled data between a large-scale corpus and a knowledge base without utilizing human efforts. Therefore, many studies have used the distant supervision approach in relation extraction tasks. However, existing studies have a disadvantage in that they do not reflect the homograph in the word embedding used as an input of the relation extraction model. Thus, it can be seen that the relation extraction model learns without grasping the meaning of the word accurately. In this paper, we propose a relation extraction model with multi-sense word embedding. We learn multi-sense word embedding using a word sense disambiguation module. In addition, we use convolutional neural network and piecewise max pooling convolutional neural network relation extraction models that efficiently grasp key features in sentences. To evaluate the performance of the proposed model, two additional methods of word embedding were learned and compared. Accordingly, our method showed the highest performance among them.
Cross-Lingual and Supervised Learning Approach for Indonesian Word Sense Disambiguation Task
Authors: Rahmad Mahendra, Heninggar Septiantri, Haryo Akbarianto Wibowo, Ruli Manurung, Mirna Adriani
Date: 2018-01-01 00:00:00
Proceeding: GWC 2018 1
Tasks: Word Sense Disambiguation

Ambiguity is a problem we frequently face in Natural Language Processing. Word Sense Disambiguation (WSD) is a task to determine the correct sense of an ambiguous word. However, research in WSD for Indonesian is still rare to find. The availability of English-Indonesian parallel corpora and WordNet for both languages can be used as training data for WSD by applying Cross-Lingual WSD method. This training data is used as an input to build a model using supervised machine learning algorithms. Our research also examines the use of Word Embedding features to build the WSD model.
Recognition of Hyponymy and Meronymy Relations in Word Embeddings for Polish
Authors: Gabriela Czachor, Maciej Piasecki, Arkadiusz Janz
Date: 2018-01-01 00:00:00
Proceeding: GWC 2018 1
Tasks: regression, Word Embeddings

Word embeddings were used for the extraction of hyponymy relation in several approaches, but also it was recently shown that they should not work, in fact. In our work we verified both claims using a very large wordnet of Polish as a gold standard for lexico-semantic relations and word embeddings extracted from a very large corpus of Polish. We showed that a hyponymy extraction method based on linear regression classifiers trained on clusters of vectors can be successfully applied on large scale. We presented also a possible explanation for contradictory findings in the literature. Moreover, in order to show the feasibility of the method we extended it to the recognition of meronymy.
Simple Embedding-Based Word Sense Disambiguation
Authors: Dieke Oele, Gertjan van Noord
Date: 2018-01-01 00:00:00
Proceeding: GWC 2018 1
Tasks: Word Sense Disambiguation

We present a simple knowledge-based WSD method that uses word and sense embeddings to compute the similarity between the gloss of a sense and the context of the word. Our method is inspired by the Lesk algorithm as it exploits both the context of the words and the definitions of the senses. It only requires large unlabeled corpora and a sense inventory such as WordNet, and therefore does not rely on annotated data. We explore whether additional extensions to Lesk are compatible with our method. The results of our experiments show that by lexically extending the amount of words in the gloss and context, although it works well for other implementations of Lesk, harms our method. Using a lexical selection method on the context words, on the other hand, improves it. The combination of our method with lexical selection enables our method to outperform state-of the art knowledge-based systems.
Multilingual Wordnet sense Ranking using nearest context
Authors: E Umamaheswari Vasanthakumar, Francis Bond
Date: 2018-01-01 00:00:00
Proceeding: GWC 2018 1
Tasks: Word Embeddings

In this paper, we combine methods to estimate sense rankings from raw text with recent work on word embeddings to provide sense ranking estimates for the entries in the Open Multilingual WordNet (OMW). The existing Word2Vec pre-trained models from Polygot2 are only built for single word entries, we, therefore, re-train them with multiword expressions from the wordnets, so that multiword expressions can also be ranked. Thus this trained model gives embeddings for both single words and multiwords. The resulting lexicon gives a WSD baseline for five languages. The results are evaluated for Semcor sense corpora for 5 languages using Word2Vec and Glove models. The Glove model achieves an average accuracy of 0.47 and Word2Vec achieves 0.31 for languages such as English, Italian, Indonesian, Chinese and Japanese. The experimentation on OMW sense ranking proves that the rank correlation is generally similar to the human ranking. Hence distributional semantics can aid in Wordnet Sense Ranking.
Grammatical Role Embeddings for Enhancements of Relation Density in the Princeton WordNet
Authors: Kiril Simov, Alexander Popov, Iliana Simova, Petya Osenova
Date: 2018-01-01 00:00:00
Proceeding: GWC 2018 1
Tasks: LEMMA, Object, Relation, Word Sense Disambiguation

In this paper we present an approach for training verb subatom embeddings. For each verb we learn several embeddings rather than only one. These embeddings include the verb itself as well as embeddings for each grammatical role of this verb. To give an example, for the verb ‘to give’ we learn four embeddings: one for the lemma ‘give’, one for the subject, one for the direct object and one for the indirect object. We have exploited these grammatical role embeddings in order to add new syntagmatic relations to WordNet. The evaluation of the new relations quality has been done extrinsically through the Knowledge-based Word Sense Disambiguation task.
An Iterative Approach for Unsupervised Most Frequent Sense Detection using WordNet and Word Embeddings
Authors: Kevin Patel, Pushpak Bhattacharyya
Date: 2018-01-01 00:00:00
Proceeding: GWC 2018 1
Tasks: Word Embeddings, Word Sense Disambiguation

Given a word, what is the most frequent sense in which it occurs in a given corpus? Most Frequent Sense (MFS) is a strong baseline for unsupervised word sense disambiguation. If we have large amounts of sense-annotated corpora, MFS can be trivially created. However, sense-annotated corpora are a rarity. In this paper, we propose a method which can compute MFS from raw corpora. Our approach iteratively exploits the semantic congruity among related words in corpus. Our method performs better compared to another similar work.
Automatic Identification of Basic-Level Categories
Authors: Chad Mills, Francis Bond, Gina-Anne Levow
Date: 2018-01-01 00:00:00
Proceeding: GWC 2018 1

Basic-level categories have been shown to be both psychologically significant and useful in a wide range of practical applications. We build a rule-based system to identify basic-level categories in WordNet, achieving 77% accuracy on a test set derived from prior psychological experiments. With additional annotations we found our system also has low precision, in part due to the existence of many categories that do not fit into the three classes (superordinate, basic-level, and subordinate) relied on in basic-level category research.
African Wordnet: facilitating language learning in African languages
Authors: Sonja Bosch, Marissa Griesel
Date: 2018-01-01 00:00:00
Proceeding: GWC 2018 1

The development of the African Wordnet (AWN) has reached a stage of maturity where the first steps towards an application can be attempted. The AWN is based on the expand method, and to compensate for the general resource scarceness of the African languages, various development strategies were used. The aim of this paper is to investigate the usefulness of the current isiZulu Wordnet in an application such as language learning. The advantage of incorporating the wordnet of a language into a language learning system is that it provides learners with an integrated application to enhance their learning experience by means of the unique sense identification features of wordnets. In this paper it will be demonstrated by means of a variety of examples within the context of a basic free online course how the isiZulu Wordnet can offer the language learner improved decision support.
Hindi Wordnet for Language Teaching: Experiences and Lessons Learnt
Authors: Hanumant Redkar, Rajita Shukla, Sandhya Singh, Jaya Saraswati, Laxmi Kashyap, Diptesh Kanojia, Preethi Jyothi, Malhar Kulkarni, Pushpak Bhattacharyya
Date: 2018-01-01 00:00:00
Proceeding: GWC 2018 1

This paper reports the work related to making Hindi Wordnet1 available as a digital resource for language learning and teaching, and the experiences and lessons that were learnt during the process. The language data of the Hindi Wordnet has been suitably modified and enhanced to make it into a language learning aid. This aid is based on modern pedagogical axioms and is aligned to the learning objectives of the syllabi of the school education in India. To make it into a comprehensive language tool, grammatical information has also been encoded, as far as these can be marked on the lexical items. The delivery of information is multi-layered, multi-sensory and is available across multiple digital platforms. The front end has been designed to offer an eye-catching user-friendly interface which is suitable for learners starting from age six onward. Preliminary testing of the tool has been done and it has been modified as per the feedbacks that were received. Above all, the entire exercise has offered gainful insights into learning based on associative networks and how knowledge based on such networks can be made available to modern learners.
An Experiment: Using Google Translate and Semantic Mirrors to Create Synsets with Many Lexical Units
Authors: Ahti Lohk, Mati Tombak, Kadri Vare
Date: 2018-01-01 00:00:00
Proceeding: GWC 2018 1

One of the fundamental building blocks of a wordnet is synonym sets or synsets, which group together similar word meanings or synonyms. These synsets can consist either one or more synonyms. This paper describes an automatic method for composing synsets with multiple synonyms by using Google Translate and Semantic Mirrors’ method. Also, we will give an overview of the results and discuss the advantages of the proposed method from wordnet’s point of view.
Context-sensitive Sentiment Propagation in WordNet
Authors: Jan Kocoń, Arkadiusz Janz, Maciej Piasecki
Date: 2018-01-01 00:00:00
Proceeding: GWC 2018 1

In this paper we present a comprehensive overview of recent methods of the sentiment propagation in a wordnet. Next, we propose a fully automated method called Classifier-based Polarity Propagation, which utilises a very rich set of features, where most of them are based on wordnet relation types, multi-level bag-of-synsets and bag-of-polarities. We have evaluated our solution using manually annotated part of plWordNet 3.1 emo, which contains more than 83k manual sentiment annotations, covering more than 41k synsets. We have demonstrated that in comparison to existing rule-based methods using a specific narrow set of semantic relations our method has achieved statistically significant and better results starting with the same seed synsets.
ELEXIS - a European infrastructure fostering cooperation and information exchange among lexicographical research communities
Authors: Bolette Pedersen, John McCrae, Carole Tiberius, Simon Krek
Date: 2018-01-01 00:00:00
Proceeding: GWC 2018 1

The paper describes objectives, concept and methodology for ELEXIS, a European infrastructure fostering cooperation and information exchange among lexicographical research communities. The infrastructure is a newly granted project under the Horizon 2020 INFRAIA call, with the topic Integrating Activities for Starting Communities. The project is planned to start in January 2018.
Enchancing the Collaborative Interlingual Index for Digital Humanities: Cross-linguistic Analysis in the Domain of Theology
Authors: Laura Slaughter, Wenjie Wang, Luis Morgado Da Costa, Francis Bond
Date: 2018-01-01 00:00:00
Proceeding: GWC 2018 1
Tasks: Translation

We aim to support digital humanities work related to the study of sacred texts. To do this, we propose to build a cross-lingual wordnet within the do-main of theology. We target the Collaborative Interlingual Index (CILI) directly instead of each individual wordnet. The paper presents background for this proposal: (1) an overview of concepts relevant to theology and (2) a summary of the domain-associated issues observed in the Princeton WordNet (PWN). We have found that definitions for concepts in this domain can be too restrictive, inconsistent, and unclear. Necessary synsets are missing, with the PWN being skewed towards Christianity. We argue that tackling problems in a single domain is a better method for improving CILI. By focusing on a single topic rather than a single language, this will result in the proper construction of definitions, romanization/translation of lemmas, and also improvements in use of/creation of a cross-lingual domain hierarchy.
Estonian Wordnet: Current State and Future Prospects
Authors: Heili Orav, Kadri Vare, Sirli Zupping
Date: 2018-01-01 00:00:00
Proceeding: GWC 2018 1

This paper presents Estonian Wordnet (EstWN) with its latest developments. We are focusing on the time period of 2011–2017 because during this time EstWN project was supported by the National Programme for Estonian Language Technology (NPELT1). We describe which were the goals at the beginning of 2011 and what are the accomplishments today. This paper serves as a summarizing report about the progress of EstWN during this programme. While building EstWN we have been concentrating on the fact, that EstWN as a valuable Estonian resource would also be compatible in a common multilingual framework.
Further expansion of the Croatian WordNet
Authors: Krešimir Šojat, Matea Filko, Antoni Oliver
Date: 2018-01-01 00:00:00
Proceeding: GWC 2018 1

In this paper a semi-automatic procedure for the expansion of the Croatian Wordnet (CroWN) is presented. An English-Croatian dictionary was used in order to translate monosemous PWN 3.0 English variants. The precision values of the automatic process is low (about 30%), but the results proved valuable for the enlargment of CroWN. After manual validation, 10,884 new synset-variant pairs were added to CroWN, achieving a total of 62,075 synset-variant pairs.
Linking WordNet to 3D Shapes
Authors: Angel X Chang, Rishi Mago, Pranav Krishna, Manolis Savva, Christiane Fellbaum
Date: 2018-01-01 00:00:00
Proceeding: GWC 2018 1

We describe a project to link the Princeton WordNet to 3D representations of real objects and scenes. The goal is to establish a dataset that helps us to understand how people categorize everyday common objects via their parts, attributes, and context. This paper describes the annotation and data collection effort so far as well as ideas for future work.
Multisłownik: Linking plWordNet-based Lexical Data for Lexicography and Educational Purposes
Authors: Maciej Ogrodniczuk, Joanna Bilińska, Zbigniew Bronk, Witold Kieraś
Date: 2018-01-01 00:00:00
Proceeding: GWC 2018 1

Multisłownik is an automated integrator of Polish lexical data retrieved from multiple available online sources intended to be used in various scenarios requiring access to such data, most prominently dictionary creation, linguistic studies and education. In contrast to many available internet dictionaries Multisłownik is WordNet-centric, capturing the core definitions from Słowosiec ́, the Polish WordNet, and linking external resources to particular synsets. The paper provides details of construction of the resource, discussed the difficulties related to linking different logical structures of underlying data and investigates two sample scenarios for using the resulting platform.
Putting Figures on Influences on Moroccan Darija from Arabic, French and Spanish using the WordNet
Authors: Khalil Mrini, Francis Bond
Date: 2018-01-01 00:00:00
Proceeding: GWC 2018 1
Tasks: Translation, Transliteration

Moroccan Darija is a variant of Arabic with many influences. Using the Open Multilingual WordNet (OMW), we compare the lemmas in the Moroccan Darija Wordnet (MDW) with the standard Arabic, French and Spanish ones. We then compared the lemmas in each synset with their translation equivalents. Transliteration is used to bridge alphabet differences and match lemmas in the closest phonological way. The results put figures on the similarity Moroccan Darija has with Arabic, French and Spanish: respectively 42.0%, 2.8% and 2.2%.
pyiwn: A Python based API to access Indian Language WordNets
Authors: Ritesh Panjwani, Diptesh Kanojia, Pushpak Bhattacharyya
Date: 2018-01-01 00:00:00
Proceeding: GWC 2018 1
Tasks: Speech Synthesis

Indian language WordNets have their individual web-based browsing interfaces along with a common interface for IndoWordNet. These interfaces prove to be useful for language learners and in an educational domain, however, they do not provide the functionality of connecting to them and browsing their data through a lucid application programming interface or an API. In this paper, we present our work on creating such an easy-to-use framework which is bundled with the data for Indian language WordNets and provides NLTK WordNet interface like core functionalities in Python. Additionally, we use a pre-built speech synthesis system for Hindi language and augment Hindi data with audios for words, glosses, and example sentences. We provide a detailed usage of our API and explain the functions for ease of the user. Also, we package the IndoWordNet data along with the source code and provide it openly for the purpose of research. We aim to provide all our work as an open source framework for further development.
Sinitic Wordnet: Laying the Groundwork with Chinese Varieties Written in Traditional Characters
Authors: Chih-Yao Lee, Shu-Kai Hsieh
Date: 2018-01-01 00:00:00
Proceeding: GWC 2018 1

The present work seeks to make the logographic nature of Chinese script a relevant research ground in wordnet studies. While wordnets are not so much about words as about the concepts represented in words, synset formation inevitably involves the use of orthographic and/or phonetic representations to serve as headword for a given concept. For wordnets of Chinese languages, if their synsets are mapped with each other, the connection from logographic forms to lexicalized concepts can be explored backwards to, for instance, help trace the development of cognates in different varieties of Chinese. The Sinitic Wordnet project is an attempt to construct such an integrated wordnet that aggregates three Chinese varieties that are widely spoken in Taiwan and all written in traditional Chinese characters.
Synthesizing Audio for Hindi WordNet
Authors: Diptesh Kanojia, Preethi Jyothi, Pushpak Bhattacharyya
Date: 2018-01-01 00:00:00
Proceeding: GWC 2018 1
Tasks: Speech Synthesis

In this paper, we describe our work on the creation of a voice model using a speech synthesis system for the Hindi Language. We use pre-existing “voices”, use publicly available speech corpora to create a “voice” using the Festival Speech Synthesis System (Black, 1997). Our contribution is two-fold: (1) We scrutinize multiple speech synthesis systems and provide an extensive report on the currently available state-of-the-art systems. We also develop voices using the existing implementations of the aforementioned systems, and (2) We use these voices to generate sample audios for randomly chosen words; manually evaluate the audio generated, and produce audio for all WordNet words using the winner voice model. We also produce audios for the Hindi WordNet Glosses and Example sentences. We describe our efforts to use pre-existing implementations for WaveNet - a model to generate raw audio using neural nets (Oord et al., 2016) and generate speech for Hindi. Our lexicographers perform a manual evaluation of the audio generated using multiple voices. A qualitative and quantitative analysis reveals that the voice model generated by us performs the best with an accuracy of 0.44.
Toward Constructing the National Cancer Institute Thesaurus Derived WordNet (ncitWN)
Authors: Amanda Hicks, Selja Seppälä, Francis Bond
Date: 2018-01-01 00:00:00
Proceeding: GWC 2018 1

We describe preliminary work in the creation of the first specialized vocabulary to be integrated into the Open Multilingual Wordnet (OMW). The NCIt Derived WordNet (ncitWN) is based on the National Cancer Institute Thesaurus (NCIt), a controlled biomedical terminology that includes formal class restrictions and English definitions developed by groups of clinicians and terminologists. The ncitWN is created by converting the NCIt to the WordNet Lexical Markup Framework and adding semantic types. We report the development of a prototype ncitWN and first steps towards integrating it into the OMW.
Towards a Crowd-Sourced WordNet for Colloquial English
Authors: John P. McCrae, Ian Wood, Amanda Hicks
Date: 2018-01-01 00:00:00
Proceeding: GWC 2018 1

Princeton WordNet is one of the most widely-used resources for natural language processing, but is updated only infrequently and cannot keep up with the fast-changing usage of the English language on social media platforms such as Twitter. The Colloquial WordNet aims to provide an open platform whereby anyone can contribute, while still following the structure of WordNet. Many crowd-sourced lexical resources often have significant quality issues, and as such care must be taken in the design of the interface to ensure quality. In this paper, we present the development of a platform that can be opened on the Web to any lexicographer who wishes to contribute to this resource and the lexicographic methodology applied by this interface.
WordNet Troponymy and Extraction of “Manner-Result” Relations
Authors: Aliaksandr Huminski, Hao Zhang
Date: 2018-01-01 00:00:00
Proceeding: GWC 2018 1

Commonsense knowledge bases need to have relations that allow to predict the consequences of specific actions (say, if John stabbed Peter, Peter might be killed) and to unfold the possible actions for the specific results (Peter was killed. It could happen because of poisoning, stabbing, shooting, etc.) This kind of causal relations are established between manner verbs and result verbs: manner-result relations. We offer a procedure on how to extract manner-result relations from WordNet through the analysis of the troponym glosses. The procedure of extraction includes three steps and the results are based on the analysis of the whole set of verbs in WordNet.
SardaNet: a Linguistic Resource for Sardinian Language
Authors: Manuela Angioni, Franco Tuveri, Maurizio Virdis, Laura Lucia Lai, Micol Elisa Maltesi
Date: 2018-01-01 00:00:00
Proceeding: GWC 2018 1

This paper describes the process of building SardaNet, a linguistic resource for Sardinian language including the different linguistic varieties in Sardinia. SardaNet aims at identifying the semantic relations between Sardinian terms, by manually mapping existing WordNet entries to Sardinian word senses. The work, still in progress, has been developed in collaboration with the University of Cagliari. After discussing some linguistic peculiarities, the paper presents the basic steps of the construction process, the method and the tools involved, the issues encountered during the development and the current version of SardaNet.