pwc-archive/papers-with-abstracts huggingface dataset:
SiNER: A Large Dataset for Sindhi Named Entity Recognition
Authors: Wazir Ali, Junyu Lu, Zenglin Xu
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Articles, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

We introduce the SiNER: a named entity recognition (NER) dataset for low-resourced Sindhi language with quality baselines. It contains 1,338 news articles and more than 1.35 million tokens collected from Kawish and Awami Awaz Sindhi newspapers using the begin-inside-outside (BIO) tagging scheme. The proposed dataset is likely to be a significant resource for statistical Sindhi language processing. The ultimate goal of developing SiNER is to present a gold-standard dataset for Sindhi NER along with quality baselines. We implement several baseline approaches of conditional random field (CRF) and recent popular state-of-the-art bi-directional long-short term memory (Bi-LSTM) models. The promising F1-score of 89.16 outputted by the Bi-LSTM-CRF model with character-level representations demonstrates the quality of our proposed SiNER dataset.
Construct a Sense-Frame Aligned Predicate Lexicon for Chinese AMR Corpus
Authors: Li Song, Yuling Dai, Yihuan Liu, Bin Li, Weiguang Qu
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Abstract Meaning Representation, Event Extraction, Sentence, Word Sense Disambiguation

The study of predicate frame is an important topic for semantic analysis. Abstract Meaning Representation (AMR) is an emerging graph based semantic representation of a sentence. Since core semantic roles defined in the predicate lexicon compose the backbone in an AMR graph, the construction of the lexicon becomes the key issue. The existing lexicons blur senses and frames of predicates, which needs to be refined to meet the tasks like word sense disambiguation and event extraction. This paper introduces the on-going project on constructing a novel predicate lexicon for Chinese AMR corpus. The new lexicon includes 14,389 senses and 10,800 frames of 8,470 words. As some senses can be aligned to more than one frame, and vice versa, we found the alignment between senses is not just one frame per sense. Explicit analysis is given for multiple aligned relations, which proves the necessity of the proposed lexicon for AMR corpus, and supplies real data for linguistic theoretical studies.
A Myanmar (Burmese)-English Named Entity Transliteration Dictionary
Authors: Aye Myat Mon, Chenchen Ding, Hour Kaing, Khin Mar Soe, Masao Utiyama, Eiichiro Sumita
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Transliteration

Transliteration is generally a phonetically based transcription across different writing systems. It is a crucial task for various downstream natural language processing applications. For the Myanmar (Burmese) language, robust automatic transliteration for borrowed English words is a challenging task because of the complex Myanmar writing system and the lack of data. In this study, we constructed a Myanmar-English named entity dictionary containing more than eighty thousand transliteration instances. The data have been released under a CC BY-NC-SA license. We evaluated the automatic transliteration performance using statistical and neural network-based approaches based on the prepared data. The neural network model outperformed the statistical model significantly in terms of the BLEU score on the character level. Different units used in the Myanmar script for processing were also compared and discussed.
Building Semantic Grams of Human Knowledge
Authors: Valentina Leone, Giovanni Siragusa, Luigi di Caro, Roberto Navigli
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Semantic Similarity, Semantic Textual Similarity, Word Embeddings

Word senses are typically defined with textual definitions for human consumption and, in computational lexicons, put in context via lexical-semantic relations such as synonymy, antonymy, hypernymy, etc. In this paper we embrace a radically different paradigm that provides a slot-filler structure, called {``}semagram{''}, to define the meaning of words in terms of their prototypical semantic information. We propose a semagram-based knowledge model composed of 26 semantic relationships which integrates features from a range of different sources, such as computational lexicons and property norms. We describe an annotation exercise regarding 50 concepts over 10 different categories and put forward different automated approaches for extending the semagram base to thousands of concepts. We finally evaluated the impact of the proposed resource on a semantic similarity task, showing significant improvements over state-of-the-art word embeddings.
Automatically Building a Multilingual Lexicon of False Friends With No Supervision
Authors: Ana Sabina Uban, Liviu P. Dinu
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Cross-Lingual Word Embeddings, Language Acquisition, Word Embeddings

Cognate words, defined as words in different languages which derive from a common etymon, can be useful for language learners, who can leverage the orthographical similarity of cognates to more easily understand a text in a foreign language. Deceptive cognates, or false friends, do not share the same meaning anymore; these can be instead deceiving and detrimental for language acquisition or text understanding in a foreign language. We use an automatic method of detecting false friends from a set of cognates, in a fully unsupervised fashion, based on cross-lingual word embeddings. We implement our method for English and five Romance languages, including a low-resource language (Romanian), and evaluate it against two different gold standards. The method can be extended easily to any language pair, requiring only large monolingual corpora for the involved languages and a small bilingual dictionary for the pair. We additionally propose a measure of {``}falseness{''} of a false friends pair. We publish freely the database of false friends in the six languages, along with the falseness scores for each cognate pair. The resource is the largest of the kind that we are aware of, both in terms of languages covered and number of word pairs.
A Parallel WordNet for English, Swedish and Bulgarian
Authors: Krasimir Angelov
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Articles, Machine Translation, Text Generation, Translation

We present the parallel creation of a WordNet resource for Swedish and Bulgarian which is tightly aligned with the Princeton WordNet. The alignment is not only on the synset level, but also on word level, by matching words with their closest translations in each language. We argue that the tighter alignment is essential in machine translation and natural language generation. About one-fifth of the lexical entries are also linked to the corresponding Wikipedia articles. In addition to the traditional semantic relations in WordNet, we also integrate morphological and morpho-syntactic information. The resource comes with a corpus where examples from Princeton WordNet are translated to Swedish and Bulgarian. The examples are aligned on word and phrase level. The new resource is open-source and in its development we used only existing open-source resources.
ENGLAWI: From Human- to Machine-Readable Wiktionary
Authors: Franck Sajous, Basilio Calderone, Nabil Hathout
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Articles, Word Embeddings

This paper introduces ENGLAWI, a large, versatile, XML-encoded machine-readable dictionary extracted from Wiktionary. ENGLAWI contains 752,769 articles encoding the full body of information included in Wiktionary: simple words, compounds and multiword expressions, lemmas and inflectional paradigms, etymologies, phonemic transcriptions in IPA, definition glosses and usage examples, translations, semantic and morphological relations, spelling variants, etc. It is fully documented, released under a free license and supplied with G-PeTo, a series of scripts allowing easy information extraction from ENGLAWI. Additional resources extracted from ENGLAWI, such as an inflectional lexicon, a lexicon of diatopic variants and the inclusion dates of headwords in Wiktionary{'}s nomenclature are also provided. The paper describes the content of the resource and illustrates how it can be - and has been - used in previous studies. We finally introduce an ongoing work that computes lexicographic word embeddings from ENGLAWI{'}s definitions.
Opening the Romance Verbal Inflection Dataset 2.0: A CLDF lexicon
Authors: Sacha Beniamine, Martin Maiden, Erich Round
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

We introduce the Romance Verbal Inflection Dataset 2.0, a multilingual lexicon of Romance inflection covering 74 varieties. The lexicon provides verbal paradigm forms in broad IPA phonemic notation. Both lexemes and paradigm cells are organized to reflect cognacy. Such multi-lingual inflected lexicons annotated for two dimensions of cognacy are necessary to study the evolution of inflectional paradigms, and test linguistic hypotheses systematically. However, these resources seldom exist, and when they do, they are not usually encoded in computationally usable ways. The Oxford Online Database of Romance Verb Morphology provides this kind of information, however, it is not maintained anymore and is only available as a web service without interfaces for machine-readability. We collect its data and clean and correct it for consistency using both heuristics and expert annotator judgements. Most resources used to study language evolution computationally rely strictly on multilingual contemporary information, and lack information about prior stages of the languages. To provide such information, we augment the database with Latin paradigms from the LatInFlexi lexicon. Finally, to make it widely avalable, the resource is released under a GPLv3 license in CLDF format.
Introducing Lexical Masks: a New Representation of Lexical Entries for Better Evaluation and Exchange of Lexicons
Authors: Bruno Cartoni, Daniel Calvelo Aros, Vr, Denny ecic, Saran Lertpradit
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

The evaluation and exchange of large lexicon databases remains a challenge in many NLP applications. Despite the existence of commonly accepted standards for the format and the features used in a lexicon, there is still a lack of precise and interoperable specification requirements about how lexical entries of a particular language should look like, both in terms of the numbers of forms and in terms of features associated with these forms. This paper presents the notion of {``}lexical masks{''}, a powerful tool used to evaluate and exchange lexicon databases in many languages.
A Large-Scale Leveled Readability Lexicon for Standard Arabic
Authors: Muhamed Al Khalil, Nizar Habash, Zhengyang Jiang
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: LEMMA

We present a large-scale 26,000-lemma leveled readability lexicon for Modern Standard Arabic. The lexicon was manually annotated in triplicate by language professionals from three regions in the Arab world. The annotations show a high degree of agreement; and major differences were limited to regional variations. Comparing lemma readability levels with their frequencies provided good insights in the benefits and pitfalls of frequency-based readability approaches. The lexicon will be publicly available.
Preserving Semantic Information from Old Dictionaries: Linking Senses of the `Altfranz\"osisches W\"orterbuch' to WordNet
Authors: Achim Stein
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Optical Character Recognition (OCR)

Historical dictionaries of the pre-digital period are important resources for the study of older languages. Taking the example of the {`}Altfranz{\"o}sisches W{\"o}rterbuch{'}, an Old French dictionary published from 1925 onwards, this contribution shows how the printed dictionaries can be turned into a more easily accessible and more sustainable lexical database, even though a full-text retro-conversion is too costly. Over 57,000 German sense definitions were identified in uncorrected OCR output. For verbs and nouns, 34,000 senses of more than 20,000 lemmas were matched with GermaNet, a semantic network for German, and, in a second step, linked to synsets of the English WordNet. These results are relevant for the automatic processing of Old French, for the annotation and exploitation of Old French text corpora, and for the philological study of Old French in general.
Cifu: a Frequency Lexicon of Hong Kong Cantonese
Authors: Regine Lai, Gr{\'e}goire Winterstein
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Diversity

This paper introduces Cifu, a lexical database for Hong Kong Cantonese (HKC) that offers phonological and orthographic information, frequency measures, and lexical neighborhood information for lexical items in HKC. Cifu is of use for NLP applications and the design and analysis of psycholinguistics experiments on HKC. We elaborate on the characteristics and challenges specific to HKC that were relevant in the design of Cifu. This includes lexical, orthographic and phonological aspects of HKC, word segmentation issues, the place of HKC in written media, and the availability of data. We discuss the measure of Neighborhood Density (ND), highlighting how the analytic nature of Cantonese and its writing system affect that measure. We justify using six different variations of ND, based on the possibility of inserting or deleting phonemes when searching for neighbors and on the choice of data for retrieving frequencies. Statistics about the four genres (written, adult spoken, children spoken and child-directed) within the dataset are discussed. We find that the lexical diversity of the child-directed speech genre is particularly low, compared to a size-matched written corpus. The correlations of word frequencies of different genres are all high, but in generally decrease as word length increases.
Odi et Amo. Creating, Evaluating and Extending Sentiment Lexicons for Latin.
Authors: Rachele Sprugnoli, Marco Passarotti, Daniela Corbetta, Andrea Peverelli
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Cultural Vocal Bursts Intensity Prediction, Sentiment Analysis

Sentiment lexicons are essential for developing automatic sentiment analysis systems, but the resources currently available mostly cover modern languages. Lexicons for ancient languages are few and not evaluated with high-quality gold standards. However, the study of attitudes and emotions in ancient texts is a growing field of research which poses specific issues (e.g., lack of native speakers, limited amount of data, unusual textual genres for the sentiment analysis task, such as philosophical or documentary texts) and can have an impact on the work of scholars coming from several disciplines besides computational linguistics, e.g. historians and philologists. The work presented in this paper aims at providing the research community with a set of sentiment lexicons built by taking advantage of manually-curated resources belonging to the long tradition of Latin corpora and lexicons creation. Our interdisciplinary approach led us to release: i) two automatically generated sentiment lexicons; ii) a gold standard developed by two Latin language and culture experts; iii) a silver standard in which semantic and derivational relations are exploited so to extend the list of lexical items of the gold standard. In addition, the evaluation procedure is described together with a first application of the lexicons to a Latin tragedy.
WordWars: A Dataset to Examine the Natural Selection of Words
Authors: Saif M. Mohammad
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

There is a growing body of work on how word meaning changes over time: mutation. In contrast, there is very little work on how different words compete to represent the same meaning, and how the degree of success of words in that competition changes over time: natural selection. We present a new dataset, WordWars, with historical frequency data from the early 1800s to the early 2000s for monosemous English words in over 5000 synsets. We explore three broad questions with the dataset: (1) what is the degree to which predominant words in these synsets have changed, (2) how do prominent word features such as frequency, length, and concreteness impact natural selection, and (3) what are the differences between the predominant words of the 2000s and the predominant words of early 1800s. We show that close to one third of the synsets undergo a change in the predominant word in this time period. Manual annotation of these pairs shows that about 15{\%} of these are orthographic variations, 25{\%} involve affix changes, and 60{\%} have completely different roots. We find that frequency, length, and concreteness all impact natural selection, albeit in different ways.
Development of a Japanese Personality Dictionary based on Psychological Methods
Authors: Ritsuko Iwai, Daisuke Kawahara, Takatsune Kumada, Sadao Kurohashi
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Word Embeddings

We propose a new approach to constructing a personality dictionary with psychological evidence. In this study, we collect personality words, using word embeddings, and construct a personality dictionary with weights for Big Five traits. The weights are calculated based on the responses of the large sample (N=1,938, female = 1,004, M=49.8years old:20-78, SD=16.3). All the respondents answered a 20-item personality questionnaire and 537 personality items derived from word embeddings. We present the procedures to examine the qualities of responses with psychological methods and to calculate the weights. These result in a personality dictionary with two sub-dictionaries. We also discuss an application of the acquired resources.
A Lexicon-Based Approach for Detecting Hedges in Informal Text
Authors: Jumayel Islam, Lu Xiao, Robert E. Mercer
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Management, Sentence

Hedging is a commonly used strategy in conversational management to show the speaker{'}s lack of commitment to what they communicate, which may signal problems between the speakers. Our project is interested in examining the presence of hedging words and phrases in identifying the tension between an interviewer and interviewee during a survivor interview. While there have been studies on hedging detection in the natural language processing literature, all existing work has focused on structured texts and formal communications. Our project thus investigated a corpus of eight unstructured conversational interviews about the Rwanda Genocide and identified hedging patterns in the interviewees{'} responses. Our work produced three manually constructed lists of hedge words, booster words, and hedging phrases. Leveraging these lexicons, we developed a rule-based algorithm that detects sentence-level hedges in informal conversations such as survivor interviews. Our work also produced a dataset of 3000 sentences having the categories Hedge and Non-hedge annotated by three researchers. With experiments on this annotated dataset, we verify the efficacy of our proposed algorithm. Our work contributes to the further development of tools that identify hedges from informal conversations and discussions.
Word Complexity Estimation for Japanese Lexical Simplification
Authors: Daiki Nishihara, Tomoyuki Kajiwara
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Benchmarking, Lexical Simplification

We introduce three language resources for Japanese lexical simplification: 1) a large-scale word complexity lexicon, 2) the first synonym lexicon for converting complex words to simpler ones, and 3) the first toolkit for developing and benchmarking Japanese lexical simplification system. Our word complexity lexicon is expanded to a broader vocabulary using a classifier trained on a small, high-quality word complexity lexicon created by Japanese language teachers. Based on this word complexity estimator, we extracted simplified word pairs from a large-scale synonym lexicon and constructed a simplified synonym lexicon useful for lexical simplification. In addition, we developed a Python library that implements automatic evaluation and key methods in each subtask to ease the construction of a lexical simplification pipeline. Experimental results show that the proposed method based on our lexicon achieves the highest performance of Japanese lexical simplification. The current lexical simplification is mainly studied in English, which is rich in language resources such as lexicons and toolkits. The language resources constructed in this study will help advance the lexical simplification system in Japanese.
Inducing Universal Semantic Tag Vectors
Authors: Da Huo, Gerard de Melo
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: TAG

Given the well-established usefulness of part-of-speech tag annotations in many syntactically oriented downstream NLP tasks, the recently proposed notion of semantic tagging (Bjerva et al. 2016) aims at tagging words with tags informed by semantic distinctions, which are likely to be useful across a range of semantic tasks. To this end, their annotation scheme distinguishes, for instance, privative attributes from subsective ones. While annotated corpora exist, their size is limited and thus many words are out-of-vocabulary words. In this paper, we study to what extent we can automatically predict the tags associated with unseen words. We draw on large-scale word representation data to derive a large new Semantic Tag lexicon. Our experiments show that we can infer semantic tags for words with high accuracy both monolingually and cross-lingually.
LexiDB: Patterns \& Methods for Corpus Linguistic Database Management
Authors: Matthew Coole, Paul Rayson, John Mariani
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Management, Retrieval

LexiDB is a tool for storing, managing and querying corpus data. In contrast to other database management systems (DBMSs), it is designed specifically for text corpora. It improves on other corpus management systems (CMSs) because data can be added and deleted from corpora on the fly with the ability to add live data to existing corpora. LexiDB sits between these two categories of DBMSs and CMSs, more specialised to language data than a general purpose DBMS but more flexible than a traditional static corpus management system. Previous work has demonstrated the scalability of LexiDB in response to the growing need to be able to scale out for ever growing corpus datasets. Here, we present the patterns and methods developed in LexiDB for storage, retrieval and querying of multi-level annotated corpus data. These techniques are evaluated and compared to an existing CMS (Corpus Workbench CWB - CQP) and indexer (Lucene). We find that LexiDB consistently outperforms existing tools for corpus queries. This is particularly apparent with large corpora and when handling queries with large result sets