ymoslem/acl-abstracts huggingface dataset:
Using OpenWordnet-PT for Question Answering on Legal Domain (2018)

In order to practice a legal profession in Brazil, law graduates must be approved in the OAB national unified bar exam. For their topic coverage and national reach, the OAB exams provide an excellent benchmark for the performance of legal information systems, as it provides objective metrics and are challenging even for humans, as only 20{\%} of its candidates are approved. After constructing a new data set on the exams and doing shallow experiments on it, we now employ the OpenWordnet-PT to verify whether using word senses and relations we can improve previous results. We discuss the results, possible future ideas and the additions to the OpenWordnet-PT that we made.
Implementation of the Verb Model in plWordNet 4.0 (2018)

The paper presents an expansion of the verb model for plWordNet {--} the wordnet of Polish. A modified system of constitutive features (register, aspect and verb classes), synset and lexical relations is presented. A special attention is given to the proposed new relations and changes in the verb classification. We discuss also the results of its verification by application to the description of a relatively large sample of Polish verbs. The model introduces a new class of relations, namely non-constitutive synset relations that are shared among lexical units, but describe, not define synsets. The proposed model is compared to the entailment relations in other wordnets, and the description of verbs based on valency frames.
Public Apologies in India - Semantics, Sentiment and Emotion (2018)

This paper reports a pilot study related to public apologies in India, with reference to certain keywords found in them. The study is of importance as the choice of lexical items holds importance which goes beyond the surface meaning of the words. The analysis of the lexical items has been done using interlinked digital lexical resources which, in future, can lend this study to computational tasks related to opinion mining, sentiment analysis and document classification. The study attempts an in-depth psycholinguistic analysis of whether the apology conveys a sincerity of intent or is it a mere ritualistic exercise to control and repair damage.
Derivational Relations in Arabic WordNet (2018)

When derivational relations deficiency exists in a wordnet, such as the Arabic WordNet, it makes it very difficult to exploit in the natural language processing community. Such deficiency is raised when many wordnets follow the same development path of Princeton WordNet. A rule-based approach for Arabic derivational relations is proposed in this paper to deal with this deficiency. The proposed approach is explained step by step. It involves the gathering of lexical entries that share the same root, into a bag of words. Rules are then used to affect the appropriate derivational relations, i.e. to relate existing words in the AWN, involving part-of-speech switch. The method is implemented using Java. Manual verification by a lexicographer takes place to ensure good results. The described approach gave good results. It could be useful for other morphologically complex languages as well.
Extending Wordnet to Geological Times (2018)

This paper describes work extending Princeton WordNet to the domain of geological texts, associated with the time periods of the geological eras of the Earth History. We intend this extension to be considered as an example for any other domain extension that we might want to pursue. To provide this extension, we first produce a textual version of Princeton WordNet. Then we map a fragment of the International Commission on Stratigraphy (ICS) ontologies to WordNet and create the appropriate new synsets. We check the extended ontology on a small corpus of sentences from Gas and Oil technical reports and realize that more work needs to be done, as we need new words, new senses and new compounds in our extended WordNet.
Towards Emotive Annotation in plWordNet 4.0 (2018)

The paper presents an approach to building a very large emotive lexicon for Polish based on plWordNet. An expanded annotation model is discussed, in which lexical units (word senses) are annotated with basic emotions, fundamental human values and sentiment polarisation. The annotation process is performed manually in the 2+1 scheme by pairs of linguists and psychologies. Guidelines referring to the usage in corpora, substitution tests as well linguistic properties of lexical units (e.g. derivational associations) are discussed. Application of the model in a substantial extension of the emotive annotation of plWordNet is presented. The achieved high inter-annotator agreement shows that with relatively small workload a promising emotive resource can be created.
The Company They Keep: Extracting Japanese Neologisms Using Language Patterns (2018)

We describe an investigation into the identification and extraction of unrecorded potential lexical items in Japanese text by detecting text passages containing selected language patterns typically associated with such items. We identified a set of suitable patterns, then tested them with two large collections of text drawn from the WWW and Twitter. Samples of the extracted items were evaluated, and it was demonstrated that the approach has considerable potential for identifying terms for later lexicographic analysis.
Lexical-semantic resources: yet powerful resources for automatic personality classification (2018)

In this paper, we aim to reveal the impact of lexical-semantic resources, used in particular for word sense disambiguation and sense-level semantic categorization, on automatic personality classification task. While stylistic features (e.g., part-of-speech counts) have been shown their power in this task, the impact of semantics beyond targeted word lists is relatively unexplored. We propose and extract three types of lexical-semantic features, which capture high-level concepts and emotions, overcoming the lexical gap of word n-grams. Our experimental results are comparable to state-of-the-art methods, while no personality-specific resources are required.
Towards a principled approach to sense clustering -- a case study of wordnet and dictionary senses in Danish (2018)

Our aim is to develop principled methods for sense clustering which can make existing lexical resources practically useful in NLP {--} not too fine-grained to be operational and yet finegrained enough to be worth the trouble. Where traditional dictionaries have a highly structured sense inventory typically describing the vocabulary by means of mainand subsenses, wordnets are generally fine-grained and unstructured. We present a series of clustering and annotation experiments with 10 of the most polysemous nouns in Danish. We combine the structured information of a traditional Danish dictionary with the ontological types found in the Danish wordnet, DanNet. This constellation enables us to automatically cluster senses in a principled way and improve inter-annotator agreement and wsd performance.
WordnetLoom -- a Multilingual Wordnet Editing System Focused on Graph-based Presentation (2018)

The paper presents a new re-built and expanded, version 2.0 of WordnetLoom {--} an open wordnet editor. It facilitates work on a multilingual system of wordnets, is based on efficient software architecture of thin client, and offers more flexibility in enriching wordnet representation. This new version is built on the experience collected during the use of the previous one for more than 10 years of plWordNet development. We discuss its extensions motivated by the collected experience. A special focus is given to the development of a variant for the needs of MultiWordnet of Portuguese, which is based on a very different wordnet development model.
Translation Equivalence and Synonymy: Preserving the Synsets in Cross-lingual Wordnets (2018)

The Princeton WordNet for English was founded on the synonymy relation, and multilingual wordnets are primarily developed by creating equivalent synsets in the respective languages. The process would often rely on translation equivalents obtained from existing bilingual dictionaries. This paper discusses some observations from the Chinese Open Wordnet, especially from the adjective subnet, to illuminate potential blind spots of the approach which may lead to the formation of non-synsets in the new wordnet. With cross-linguistic differences duly taken into account, alternative representations of cross-lingual lexical relations are proposed to better capture the language-specific properties. It is also suggested that such cross-lingual representation encompassing the cognitive as well as linguistic aspects of meaning is beneficial for a lexical resource to be used by both humans and computers.
Lexical Perspective on Wordnet to Wordnet Mapping (2018)

The paper presents a feature-based model of equivalence targeted at (manual) sense linking between Princeton WordNet and plWordNet. The model incorporates insights from lexicographic and translation theories on bilingual equivalence and draws on the results of earlier synset-level mapping of nouns between Princeton WordNet and plWordNet. It takes into account all basic aspects of language such as form, meaning and function and supplements them with (parallel) corpus frequency and translatability. Three types of equivalence are distinguished, namely strong, regular and weak depending on the conformity with the proposed features. The presented solutions are language-neutral and they can be easily applied to language pairs other than Polish and English. Sense-level mapping is a more fine-grained mapping than the existing synset mappings and is thus of great potential to human and machine translation.
ReferenceNet: a semantic-pragmatic network for capturing reference relations. (2018)

In this paper, we present ReferenceNet: a semantic-pragmatic network of reference relations between synsets. Synonyms are assumed to be exchangeable in similar contexts and also word embeddings are based on sharing of local contexts represented as vectors. Co-referring words, however, tend to occur in the same topical context but in different local contexts. In addition, they may express different concepts related through topical coherence, and through author framing and perspective. In this paper, we describe how reference relations can be added to WordNet and how they can be acquired. We evaluate two methods of extracting event coreference relations using WordNet relations against a manual annotation of 38 documents within the same topical domain of gun violence. We conclude that precision is reasonable but recall is lower because the WordNet hierarchy does not sufficiently capture the required coherence and perspective relations.
Wordnet-based Evaluation of Large Distributional Models for Polish (2018)

The paper presents construction of large scale test datasets for word embeddings on the basis of a very large wordnet. They were next applied for evaluation of word embedding models and used to assess and compare the usefulness of different word embeddings extracted from a very large corpus of Polish. We analysed also and compared several publicly available models described in literature. In addition, several large word embeddings models built on the basis of a very large Polish corpus are presented.
Distant Supervision for Relation Extraction with Multi-sense Word Embedding (2018)

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 (2018)

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 (2018)

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 (2018)

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.
Semi-automatic WordNet Linking using Word Embeddings (2018)

Wordnets are rich lexico-semantic resources. Linked wordnets are extensions of wordnets, which link similar concepts in wordnets of different languages. Such resources are extremely useful in many Natural Language Processing (NLP) applications, primarily those based on knowledge-based approaches. In such approaches, these resources are considered as gold standard/oracle. Thus, it is crucial that these resources hold correct information. Thereby, they are created by human experts. However, manual maintenance of such resources is a tedious and costly affair. Thus techniques that can aid the experts are desirable. In this paper, we propose an approach to link wordnets. Given a synset of the source language, the approach returns a ranked list of potential candidate synsets in the target language from which the human expert can choose the correct one(s). Our technique is able to retrieve a winner synset in the top 10 ranked list for 60{\%} of all synsets and 70{\%} of noun synsets.
Multilingual Wordnet sense Ranking using nearest context (2018)

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 (2018)

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 {\textquoteleft}to give' we learn four embeddings: one for the lemma {\textquoteleft}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 (2018)

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 (2018)

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 (2018)

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 (2018)

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 (2018)

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 (2018)

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 (2018)

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 (2018)

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.