pwc-archive/papers-with-abstracts huggingface dataset:
Semantic Analysis of Verb-Noun Derivation in Princeton WordNet
Authors: Verginica Mititelu, Svetlozara Leseva, Ivelina Stoyanova
Date: 2021-01-01 00:00:00
Proceeding: EACL (GWC) 2021 1
Tasks: Relation, Specificity

We present here the results of a morphosemantic analysis of the verb-noun pairs in the Princeton WordNet as reflected in the standoff file containing pairs annotated with a set of 14 semantic relations. We have automatically distinguished between zero-derivation and affixal derivation in the data and identified the affixes and manually checked the results. The data show that for each semantic relation an affix prevails in creating new words, although we cannot talk about their specificity with respect to such a relation. Moreover, certain pairs of verb-noun semantic primes are better represented for each semantic relation, and some semantic clusters (in the form of WordNet subtrees) take shape as a result. We thus employ a large-scale data-driven linguistically motivated analysis afforded by the rich derivational and morphosemantic description in WordNet to the end of capturing finer regularities in the process of derivation as represented in the semantic properties of the words involved and as reflected in the structure of the lexicon.
Building the Turkish FrameNet
Authors: Büşra Marşan, Neslihan Kara, Merve Özçelik, Bilge Nas Arıcan, Neslihan Cesur, Aslı Kuzgun, Ezgi Sanıyar, Oğuzhan Kuyrukçu, Olcay Taner Yildiz
Date: 2021-01-01 00:00:00
Proceeding: EACL (GWC) 2021 1

FrameNet (Lowe, 1997; Baker et al., 1998; Fillmore and Atkins, 1998; Johnson et al., 2001) is a computational lexicography project that aims to offer insight into the semantic relationships between predicate and arguments. Having uses in many NLP applications, FrameNet has proven itself as a valuable resource. The main goal of this study is laying the foundation for building a comprehensive and cohesive Turkish FrameNet that is compatible with other resources like PropBank (Kara et al., 2020) or WordNet (Bakay et al., 2019; Ehsani, 2018; Ehsani et al., 2018; Parlar et al., 2019; Bakay et al., 2020) in the Turkish language.
Evaluation of Taxonomy Enrichment on Diachronic WordNet Versions
Authors: Irina Nikishina, Natalia Loukachevitch, Varvara Logacheva, Alexander Panchenko
Date: 2021-01-01 00:00:00
Proceeding: EACL (GWC) 2021 1
Tasks: Word Embeddings

The vast majority of the existing approaches for taxonomy enrichment apply word embeddings as they have proven to accumulate contexts (in a broad sense) extracted from texts which are sufficient for attaching orphan words to the taxonomy. On the other hand, apart from being large lexical and semantic resources, taxonomies are graph structures. Combining word embeddings with graph structure of taxonomy could be of use for predicting taxonomic relations. In this paper we compare several approaches for attaching new words to the existing taxonomy which are based on the graph representations with the one that relies on fastText embeddings. We test all methods on Russian and English datasets, but they could be also applied to other wordnets and languages.
A (Non)-Perfect Match: Mapping plWordNet onto PrincetonWordNet
Authors: Ewa Rudnicka, Wojciech Witkowski, Maciej Piasecki
Date: 2021-01-01 00:00:00
Proceeding: EACL (GWC) 2021 1
Tasks: Relation

The paper reports on the methodology and final results of a large-scale synset mapping between plWordNet and Princeton WordNet. Dedicated manual and semi-automatic mapping procedures as well as interlingual relation types for nouns, verbs, adjectives and adverbs are described. The statistics of all types of interlingual relations are also provided.
Persian SemCor: A Bag of Word Sense Annotated Corpus for the Persian Language
Authors: Hossein Rouhizadeh, Mehrnoush Shamsfard, Mahdi Dehghan, Masoud Rouhizadeh
Date: 2021-01-01 00:00:00
Proceeding: EACL (GWC) 2021 1
Tasks: Word Sense Disambiguation

Supervised approaches usually achieve the best performance in the Word Sense Disambiguation problem. However, the unavailability of large sense annotated corpora for many low-resource languages make these approaches inapplicable for them in practice. In this paper, we mitigate this issue for the Persian language by proposing a fully automatic approach for obtaining Persian SemCor (PerSemCor), as a Persian Bag-of-Word (BoW) sense-annotated corpus. We evaluated PerSemCor both intrinsically and extrinsically and showed that it can be effectively used as training sets for Persian supervised WSD systems. To encourage future research on Persian Word Sense Disambiguation, we release the PerSemCor in http://nlp.sbu.ac.ir.
HisNet: A Polarity Lexicon based on WordNet for Emotion Analysis
Authors: Merve Özçelik, Bilge Nas Arıcan, Özge Bakay, Elif Sarmış, Özlem Ergelen, Nilgün Güler Bayezit, Olcay Taner Yildiz
Date: 2021-01-01 00:00:00
Proceeding: EACL (GWC) 2021 1
Tasks: Emotion Recognition, Sentiment Analysis

Dictionary-based methods in sentiment analysis have received scholarly attention recently, the most comprehensive examples of which can be found in English. However, many other languages lack polarity dictionaries, or the existing ones are small in size as in the case of SentiTurkNet, the first and only polarity dictionary in Turkish. Thus, this study aims to extend the content of SentiTurkNet by comparing the two available WordNets in Turkish, namely KeNet and TR-wordnet of BalkaNet. To this end, a current Turkish polarity dictionary has been created relying on 76,825 synsets matching KeNet, where each synset has been annotated with three polarity labels, which are positive, negative and neutral. Meanwhile, the comparison of KeNet and TR-wordnet of BalkaNet has revealed their weaknesses such as the repetition of the same senses, lack of necessary merges of the items belonging to the same synset and the presence of redundant narrower versions of synsets, which are discussed in light of their potential to the improvement of the current lexical databases of Turkish.
Turkish WordNet KeNet
Authors: Özge Bakay, Özlem Ergelen, Elif Sarmış, Selin Yıldırım, Bilge Nas Arıcan, Atilla Kocabalcıoğlu, Merve Özçelik, Ezgi Sanıyar, Oğuzhan Kuyrukçu, Begüm Avar, Olcay Taner Yildiz
Date: 2021-01-01 00:00:00
Proceeding: EACL (GWC) 2021 1

Currently, there are two available wordnets for Turkish: TR-wordnet of BalkaNet and KeNet. As the more comprehensive wordnet for Turkish, KeNet includes 76,757 synsets. KeNet has both intralingual semantic relations and is linked to PWN through interlingual relations. In this paper, we present the procedure adopted in creating KeNet, give details about our approach in annotating semantic relations such as hypernymy and discuss the language-specific problems encountered in these processes.
Enriching plWordNet with morphology
Authors: Agnieszka Dziob, Wiktor Walentynowicz
Date: 2021-01-01 00:00:00
Proceeding: EACL (GWC) 2021 1
Tasks: Specificity

In the paper, we present the process of adding morphological information to the Polish WordNet (plWordNet). We describe the reasons for this connection and the intuitions behind it. We also draw attention to the specificity of the Polish morphology. We show in which tasks the morphological information is important and how the methods can be developed by extending them to include combined morphological information based on WordNet.
Towards Expanding WordNet with Conceptual Frames
Authors: Koeva Svetla
Date: 2021-01-01 00:00:00
Proceeding: EACL (GWC) 2021 1

The paper presents the project Semantic Network with a Wide Range of Semantic Relations and its main achievements. The ultimate objective of the project is to expand Princeton WordNet with conceptual frames that define the syntagmatic relations of verb synsets and the semantic classes of nouns felicitous to combine with particular verbs. At this stage of the work: a) over 5,000 WordNet verb synsets have been supplied with manually evaluated FrameNet semantic frames, b) 253 semantic types have been manually mapped to the appropriate WordNet concepts providing detailed ontological representation of the semantic classes of nouns.
OdeNet: Compiling a GermanWordNet from other Resources
Authors: Melanie Siegel, Francis Bond
Date: 2021-01-01 00:00:00
Proceeding: EACL (GWC) 2021 1

The Princeton WordNet for the English language has been used worldwide in NLP projects for many years. With the OMW initiative, wordnets for different languages of the world are being linked via identifiers. The parallel development and linking allows new multilingual application perspectives. The development of a wordnet for the German language is also in this context. To save development time, existing resources were combined and recompiled. The result was then evaluated and improved. In a relatively short time a resource was created that can be used in projects and continuously improved and extended.
Comparing Similarity of Words Based on Psychosemantic Experiment and RuWordNet
Authors: Valery Solovyev, Natalia Loukachevitch
Date: 2021-01-01 00:00:00
Proceeding: EACL (GWC) 2021 1

In the paper we compare the structure of the Russian language thesaurus RuWordNet with the data of a psychosemantic experiment to identify semantically close words. The aim of the study is to find out to what extent the structure of RuWordNet corresponds to the intuitive ideas of native speakers about the semantic proximity of words. The respondents were asked to list synonyms to a given word. As a result of the experiment, we found that the respondents mainly mentioned not only synonyms but words that are in paradigmatic relations with the stimuli. The words of the mental sphere were chosen for the experiment. In 95% of cases, the words characterized in the experiment as semantically close were also close according to the thesaurus. In other cases, additions to the thesaurus were proposed.
Text Document Clustering: Wordnet vs. TF-IDF vs. Word Embeddings
Authors: Michał Marcińczuk, Mateusz Gniewkowski, Tomasz Walkowiak, Marcin Będkowski
Date: 2021-01-01 00:00:00
Proceeding: EACL (GWC) 2021 1
Tasks: Clustering, Language Modeling, Language Modelling, Word Embeddings

In the paper, we deal with the problem of unsupervised text document clustering for the Polish language. Our goal is to compare the modern approaches based on language modeling (doc2vec and BERT) with the classical ones, i.e., TF-IDF and wordnet-based. The experiments are conducted on three datasets containing qualification descriptions. The experiments’ results showed that wordnet-based similarity measures could compete and even outperform modern embedding-based approaches.
Neural Language Models vs Wordnet-based Semantically Enriched Representation in CST Relation Recognition
Authors: Arkadiusz Janz, Maciej Piasecki, Piotr Wątorski
Date: 2021-01-01 00:00:00
Proceeding: EACL (GWC) 2021 1
Tasks: Relation, Sentence

Neural language models, including transformer-based models, that are pre-trained on very large corpora became a common way to represent text in various tasks, including recognition of textual semantic relations, e.g. Cross-document Structure Theory. Pre-trained models are usually fine tuned to downstream tasks and the obtained vectors are used as an input for deep neural classifiers. No linguistic knowledge obtained from resources and tools is utilised. In this paper we compare such universal approaches with a combination of rich graph-based linguistically motivated sentence representation and a typical neural network classifier applied to a task of recognition of CST relation in Polish. The representation describes selected levels of the sentence structure including description of lexical meanings on the basis of the wordnet (plWordNet) synsets and connected SUMO concepts. The obtained results show that in the case of difficult relations and medium size training corpus semantically enriched text representation leads to significantly better results.
What is on Social Media that is not in WordNet? A Preliminary Analysis on the TwitterAAE Corpus
Authors: Cecilia Domingo, Tatiana Gonzalez-Ferrero, Itziar Gonzalez-Dios
Date: 2021-01-01 00:00:00
Proceeding: EACL (GWC) 2021 1

Natural Language Processing tools and resources have been so far mainly created and trained for standard varieties of language. Nowadays, with the use of large amounts of data gathered from social media, other varieties and registers need to be processed, which may present other challenges and difficulties. In this work, we focus on English and we present a preliminary analysis by comparing the TwitterAAE corpus, which is annotated for ethnicity, and WordNet by quantifying and explaining the online language that WordNet misses.
Creating Domain Dependent Turkish WordNet and SentiNet
Authors: Bilge Nas Arıcan, Merve Özçelik, Deniz Baran Aslan, Elif Sarmış, Selen Parlar, Olcay Taner Yildiz
Date: 2021-01-01 00:00:00
Proceeding: EACL (GWC) 2021 1
Tasks: LEMMA, Sentiment Analysis

A WordNet is a thesaurus that has a structured list of words organized depending on their meanings. WordNet represents word senses, all meanings a single lemma may have, the relations between these senses, and their definitions. Another study within the domain of Natural Language Processing is sentiment analysis. With sentiment analysis, data sets can be scored according to the emotion they contain. In the sentiment analysis we did with the data we received on the Tourism WordNet, we performed a domain-specific sentiment analysis study by annotating the data. In this paper, we propose a method to facilitate Natural Language Processing tasks such as sentiment analysis performed in specific domains via creating a specific-domain subset of an original Turkish dictionary. As the preliminary study, we have created a WordNet for the tourism domain with 14,000 words and validated it on simple tasks.
Towards a Linking between WordNet and Wikidata
Authors: John P. McCrae, David Cillessen
Date: 2021-01-01 00:00:00
Proceeding: EACL (GWC) 2021 1
Tasks: Knowledge Graphs

WordNet is the most widely used lexical resource for English, while Wikidata is one of the largest knowledge graphs of entity and concepts available. While, there is a clear difference in the focus of these two resources, there is also a significant overlap and as such a complete linking of these resources would have many uses. We propose the development of such a linking, first by means of the hapax legomenon links and secondly by the use of natural language processing techniques. We show that these can be done with high accuracy but that human validation is still necessary. This has resulted in over 9,000 links being added between these two resources.
Toward the creation of WordNets for ancient Indo-European languages
Authors: Erica Biagetti, Chiara Zanchi, William Michael Short
Date: 2021-01-01 00:00:00
Proceeding: EACL (GWC) 2021 1

This paper presents the work in progress toward the creation of a family of WordNets for Sanskrit, Ancient Greek, and Latin. Building on previous attempts in the field, we elaborate these efforts bridging together WordNet relational semantics with theories of meaning from Cognitive Linguistics. We discuss some of the innovations we have introduced to the WordNet architecture, to better capture the polysemy of words, as well as Indo-European language family-specific features. We conclude the paper framing our work within the larger picture of resources available for ancient languages and showing that WordNet-backed search tools have the potential to re-define the kinds of questions that can be asked of ancient language corpora.
DanNet2: Extending the coverage of adjectives in DanNet based on thesaurus data (project presentation)
Authors: Sanni Nimb, Bolette Pedersen, Sussi Olsen
Date: 2021-01-01 00:00:00
Proceeding: EACL (GWC) 2021 1

The paper describes work in progress in the DanNet2 project financed by the Carlsberg Foundation. The project aim is to extend the original Danish wordnet, DanNet, in several ways. Main focus is on extension of the coverage and description of the adjectives, a part of speech that was rather sparsely described in the original wordnet. We describe the methodology and initial work of semi-automatically transferring adjectives from the Danish Thesaurus to the wordnet with the aim of easily enlarging the coverage from 3,000 to approx. 13,000 adjectival synsets. Transfer is performed by manually encoding all missing adjectival subsection headwords from the thesaurus and thereafter employing a semi-automatic procedure where adjectives from the same subsection are transferred to the wordnet as either 1) near synonyms to the section’s headword, 2) hyponyms to the section’s headword, or 3) as members of the same synset as the headword. We also discuss how to deal with the problem of multiple representations of the same sense in the thesaurus, and present other types of information from the thesaurus that we plan to integrate, such as thematic and sentiment information.
Teaching Through Tagging — Interactive Lexical Semantics
Authors: Francis Bond, Andrew Devadason, Melissa Rui Lin Teo, Luís Morgado da Costa
Date: 2021-01-01 00:00:00
Proceeding: EACL (GWC) 2021 1

In this paper we discuss an ongoing effort to enrich students’ learning by involving them in sense tagging. The main goal is to lead students to discover how we can represent meaning and where the limits of our current theories lie. A subsidiary goal is to create sense tagged corpora and an accompanying linked lexicon (in our case wordnets). We present the results of tagging several texts and suggest some ways in which the tagging process could be improved. Two authors of this paper present their own experience as students. Overall, students reported that they found the tagging an enriching experience. The annotated corpora and changes to the wordnet are made available through the NTU multilingual corpus and associated wordnets (NTU-MC).
Towards the Addition of Pronunciation Information to Lexical Semantic Resources
Authors: Thierry Declerck, Lenka Bajčetić
Date: 2021-01-01 00:00:00
Proceeding: EACL (GWC) 2021 1

This paper describes ongoing work aiming at adding pronunciation information to lexical semantic resources, with a focus on open wordnets. Our goal is not only to add a new modality to those semantic networks, but also to mark heteronyms listed in them with the pronunciation information associated with their different meanings. This work could contribute in the longer term to the disambiguation of multi-modal resources, which are combining text and speech.
Testing agreement between lexicographers: A case of homonymy and polysemy
Authors: Marek Maziarz, Francis Bond, Ewa Rudnicka
Date: 2021-01-01 00:00:00
Proceeding: EACL (GWC) 2021 1

In this paper we compare Oxford Lexico and Merriam Webster dictionaries with Princeton WordNet with respect to the description of semantic (dis)similarity between polysemous and homonymous senses that could be inferred from them. WordNet lacks any explicit description of polysemy or homonymy, but as a network of linked senses it may be used to compute semantic distances between word senses. To compare WordNet with the dictionaries, we transformed sample entry microstructures of the latter into graphs and cross-linked them with the equivalent senses of the former. We found that dictionaries are in high agreement with each other, if one considers polysemy and homonymy altogether, and in moderate concordance, if one focuses merely on polysemy descriptions. Measuring the shortest path lengths on WordNet gave results comparable to those on the dictionaries in predicting semantic dissimilarity between polysemous senses, but was less felicitous while recognising homonymy.
Inferring Morphological Complexity from Syntactic Dependency Networks: A Test
Authors: Guglielmo Inglese, Luca Brigada Villa
Date: 2021-06-01 00:00:00
Proceeding: NAACL (SIGTYP) 2021 6

Research in linguistic typology has shown that languages do not fall into the neat morphological types (synthetic vs. analytic) postulated in the 19th century. Instead, analytic and synthetic must be viewed as two poles of a continuum and languages may show a mix analytic and synthetic strategies to different degrees. Unfortunately, empirical studies that offer a more fine-grained morphological classification of languages based on these parameters remain few. In this paper, we build upon previous research by Liu & Xu (2011) and investigate the possibility of inferring information on morphological complexity from syntactic dependency networks.
A Universal Dependencies Corpora Maintenance Methodology Using Downstream Application
Authors: Ran Iwamoto, Hiroshi Kanayama, Alexandre Rademaker, Takuya Ohko
Date: 2021-06-01 00:00:00
Proceeding: NAACL (SIGTYP) 2021 6
Tasks: POS

This paper investigates updates of Universal Dependencies (UD) treebanks in 23 languages and their impact on a downstream application. Numerous people are involved in updating UD’s annotation guidelines and treebanks in various languages. However, it is not easy to verify whether the updated resources maintain universality with other language resources. Thus, validity and consistency of multilingual corpora should be tested through application tasks involving syntactic structures with PoS tags, dependency labels, and universal features. We apply the syntactic parsers trained on UD treebanks from multiple versions (2.0 to 2.7) to a clause-level sentiment extractor. We then analyze the relationships between attachment scores of dependency parsers and performance in application tasks. For future UD developments, we show examples of outputs that differ depending on version.
Improving Cross-Lingual Sentiment Analysis via Conditional Language Adversarial Nets
Authors: Hemanth Kandula, Bonan Min
Date: 2021-06-01 00:00:00
Proceeding: NAACL (SIGTYP) 2021 6
Tasks: Sentiment Analysis

Sentiment analysis has come a long way for high-resource languages due to the availability of large annotated corpora. However, it still suffers from lack of training data for low-resource languages. To tackle this problem, we propose Conditional Language Adversarial Network (CLAN), an end-to-end neural architecture for cross-lingual sentiment analysis without cross-lingual supervision. CLAN differs from prior work in that it allows the adversarial training to be conditioned on both learned features and the sentiment prediction, to increase discriminativity for learned representation in the cross-lingual setting. Experimental results demonstrate that CLAN outperforms previous methods on the multilingual multi-domain Amazon review dataset. Our source code is released at https://github.com/hemanthkandula/clan.
Improving the Performance of UDify with Linguistic Typology Knowledge
Authors: Chinmay Choudhary
Date: 2021-06-01 00:00:00
Proceeding: NAACL (SIGTYP) 2021 6

UDify is the state-of-the-art language-agnostic dependency parser which is trained on a polyglot corpus of 75 languages. This multilingual modeling enables the model to generalize over unknown/lesser-known languages, thus leading to improved performance on low-resource languages. In this work we used linguistic typology knowledge available in URIEL database, to improve the cross-lingual transferring ability of UDify even further.
FrameNet and Typology
Authors: Michael Ellsworth, Collin Baker, Miriam R. L. Petruck
Date: 2021-06-01 00:00:00
Proceeding: NAACL (SIGTYP) 2021 6
Tasks: Diversity

FrameNet and the Multilingual FrameNet project have produced multilingual semantic annotations of parallel texts that yield extremely fine-grained typological insights. Moreover, frame semantic annotation of a wide cross-section of languages would provide information on the limits of Frame Semantics (Fillmore 1982, Fillmore1985). Multilingual semantic annotation offers critical input for research on linguistic diversity and recurrent patterns in computational typology. Drawing on results from FrameNet annotation of parallel texts, this paper proposes frame semantic annotation as a new component to complement the state of the art in computational semantic typology.