pwc-archive/papers-with-abstracts huggingface dataset:
Building Web Corpora for Minority Languages
Authors: Heidi Jauhiainen, Tommi Jauhiainen, Krister Lind{\'e}n
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Language Identification, Sentence

Web corpora creation for minority languages that do not have their own top-level Internet domain is no trivial matter. Web pages in such minority languages often contain text and links to pages in the dominant language of the country. When building corpora in specific languages, one has to decide how and at which stage to make sure the texts gathered are in the desired language. In the {``}Finno-Ugric Languages and the Internet{''} (Suki) project, we created web corpora for Uralic minority languages using web crawling combined with a language identification system in order to identify the language while crawling. In addition, we used language set identification and crowdsourcing before making sentence corpora out of the downloaded texts. In this article, we describe a strategy for collecting textual material from the Internet for minority languages. The strategy is based on the experiences we gained during the Suki project.
The ELTE.DH Pilot Corpus -- Creating a Handcrafted Gigaword Web Corpus with Metadata
Authors: Bal{\'a}zs Indig, {\'A}rp{\'a}d Knap, Zs{\'o}fia S{\'a}rk{\"o}zi-Lindner, M{\'a}ria Tim{\'a}ri, G{\'a}bor Palk{\'o}
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Articles

In this article, we present the method we used to create a middle-sized corpus using targeted web crawling. Our corpus contains news portal articles along with their metadata, that can be useful for diverse audiences, ranging from digital humanists to NLP users. The method presented in this paper applies rule-based components that allow the curation of the text and the metadata content. The curated data can thereon serve as a reference for various tasks and measurements. We designed our workflow to encourage modification and customisation. Our concept can also be applied to other genres of portals by using the discovered patterns in the architecture of the portals. We found that for a systematic creation or extension of a similar corpus, our method provides superior accuracy and ease of use compared to The Wayback Machine, while requiring minimal manpower and computational resources. Reproducing the corpus is possible if changes are introduced to the text-extraction process. The standard TEI format and Schema.org encoded metadata is used for the output format, but we stress that placing the corpus in a digital repository system is recommended in order to be able to define semantic relations between the segments and to add rich annotation.
Hypernym-LIBre: A Free Web-based Corpus for Hypernym Detection
Authors: Shaurya Rawat, Mariano Rico, Oscar Corcho
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: POS

In this paper, we describe a new web-based corpus for hypernym detection. It consists of 32 GB of high quality english paragraphs along with their part-of-speech tagged and dependency parsed versions. For hypernym detection, the current state-of-the-art uses a corpus which is not available freely. We evaluate the state-of-the-art methods on our corpus and achieve similar results. The advantage of this corpora is that it is available under an open license. Our main contribution is the corpus with POS-tags and dependency tags and the code to extract and simulate the results we have achieved using our corpus.
Streaming Language-Specific Twitter Data with Optimal Keywords
Authors: Tim Kreutz, Walter Daelemans
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

The Twitter Streaming API has been used to create language-specific corpora with varying degrees of success. Selecting a filter of frequent yet distinct keywords for German resulted in a near-complete collection of German tweets. This method is promising as it keeps within Twitter endpoint limitations and could be applied to other languages besides German. But so far no research has compared methods for selecting optimal keywords for this task. This paper proposes a method for finding optimal key phrases based on a greedy solution to the maximum coverage problem. We generate candidate key phrases for the 50 most frequent languages on Twitter. Candidates are then iteratively selected based on a variety of scoring functions applied to their coverage of target tweets. Selecting candidates based on the scoring function that exponentiates the precision of a key phrase and weighs it by recall achieved the best results overall. Some target languages yield lower results than what could be expected from their prevalence on Twitter. Upon analyzing the errors, we find that these are languages that are very close to more prevalent languages. In these cases, key phrases that limit finding the competitive language are selected, and overall recall on the target language also decreases. We publish the resulting optimized lists for each language as a resource. The code to generate lists for other research objectives is also supplied.
Part-of-Speech Annotation Challenges in Marathi
Authors: Gajanan Rane, Nilesh Joshi, Geetanjali Rane, Hanumant Redkar, Malhar Kulkarni, Pushpak Bhattacharyya
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: POS, POS Tagging

Part of Speech (POS) annotation is a significant challenge in natural language processing. The paper discusses issues and challenges faced in the process of POS annotation of the Marathi data from four domains viz., tourism, health, entertainment and agriculture. During POS annotation, a lot of issues were encountered. Some of the major ones are discussed in detail in this paper. Also, the two approaches viz., the lexical (L approach) and the functional (F approach) of POS tagging have been discussed and presented with examples. Further, some ambiguous cases in POS annotation are presented in the paper.
A Dataset for Troll Classification of TamilMemes
Authors: Shardul Suryawanshi, Bharathi Raja Chakravarthi, Pranav Verma, Mihael Arcan, John Philip McCrae, Paul Buitelaar
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Classification, General Classification, image-classification, Image Classification

Social media are interactive platforms that facilitate the creation or sharing of information, ideas or other forms of expression among people. This exchange is not free from offensive, trolling or malicious contents targeting users or communities. One way of trolling is by making memes, which in most cases combines an image with a concept or catchphrase. The challenge of dealing with memes is that they are region-specific and their meaning is often obscured in humour or sarcasm. To facilitate the computational modelling of trolling in the memes for Indian languages, we created a meme dataset for Tamil (TamilMemes). We annotated and released the dataset containing suspected trolls and not-troll memes. In this paper, we use the a image classification to address the difficulties involved in the classification of troll memes with the existing methods. We found that the identification of a troll meme with such an image classifier is not feasible which has been corroborated with precision, recall and F1-score.
OdiEnCorp 2.0: Odia-English Parallel Corpus for Machine Translation
Authors: Shantipriya Parida, Satya Ranjan Dash, Ond{\v{r}}ej Bojar, Petr Motlicek, Priyanka Pattnaik, Debasish Kumar Mallick
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Machine Translation, NMT, Optical Character Recognition, Optical Character Recognition (OCR), Translation

The preparation of parallel corpora is a challenging task, particularly for languages that suffer from under-representation in the digital world. In a multi-lingual country like India, the need for such parallel corpora is stringent for several low-resource languages. In this work, we provide an extended English-Odia parallel corpus, OdiEnCorp 2.0, aiming particularly at Neural Machine Translation (NMT) systems which will help translate Englishâ†”Odia. OdiEnCorp 2.0 includes existing English-Odia corpora and we extended the collection by several other methods of data acquisition: parallel data scraping from many websites, including Odia Wikipedia, but also optical character recognition (OCR) to extract parallel data from scanned images. Our OCR-based data extraction approach for building a parallel corpus is suitable for other low resource languages that lack in online content. The resulting OdiEnCorp 2.0 contains 98,302 sentences and 1.69 million English and 1.47 million Odia tokens. To the best of our knowledge, OdiEnCorp 2.0 is the largest Odia-English parallel corpus covering different domains and available freely for non-commercial and research purposes.
Handling Noun-Noun Coreference in Tamil
Authors: Vijay Sundar Ram, Sobha Lalitha Devi
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Natural Language Understanding

Natural language understanding by automatic tools is the vital requirement for document processing tools. To achieve it, automatic system has to understand the coherence in the text. Co-reference chains bring coherence to the text. The commonly occurring reference markers which bring cohesiveness are Pronominal, Reflexives, Reciprocals, Distributives, One-anaphors, Noun{--}noun reference. Here in this paper, we deal with noun-noun reference in Tamil. We present the methodology to resolve these noun-noun anaphors and also present the challenges in handling the noun-noun anaphoric relations in Tamil.
Malayalam Speech Corpus: Design and Development for Dravidian Language
Authors: Lekshmi K R, Jithesh V S, Elizabeth Sherly
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

To overpass the disparity between theory and applications in language-related technology in the text as well as speech and several other areas, a well-designed and well-developed corpus is essential. Several problems and issues encountered while developing a corpus, especially for low resource languages. The Malayalam Speech Corpus (MSC) is one of the first open speech corpora for Automatic Speech Recognition (ASR) research to the best of our knowledge. It consists of 250 hours of Agricultural speech data. We are providing a transcription file, lexicon and annotated speech along with the audio segment. It is available in future for public use upon request at {``}www.iiitmk.ac.in/vrclc/utilities/ml{\_}speechcorpus{''}. This paper details the development and collection process in the domain of agricultural speech corpora in the Malayalam Language.
Multilingual Neural Machine Translation involving Indian Languages
Authors: Pulkit Madaan, Fatiha Sadat
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Data Augmentation, Machine Translation, NMT, Translation

Neural Machine Translations (NMT) models are capable of translating a single bilingual pair and require a new model for each new language pair. Multilingual Neural Machine Translation models are capable of translating multiple language pairs, even pairs which it hasn{'}t seen before in training. Availability of parallel sentences is a known problem in machine translation. Multilingual NMT model leverages information from all the languages to improve itself and performs better. We propose a data augmentation technique that further improves this model profoundly. The technique helps achieve a jump of more than 15 points in BLEU score from the multilingual NMT model. A BLEU score of 36.2 was achieved for Sindhi{--}English translation, which is higher than any score on the leaderboard of the LoResMT SharedTask at MT Summit 2019, which provided the data for the experiments.
Universal Dependency Treebanks for Low-Resource Indian Languages: The Case of Bhojpuri
Authors: Atul Kr. Ojha, Daniel Zeman
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

This paper presents the first dependency treebank for Bhojpuri, a resource-poor language that belongs to the Indo-Aryan language family. The objective behind the Bhojpuri Treebank (BHTB) project is to create a substantial, syntactically annotated treebank which not only acts as a valuable resource in building language technological tools, also helps in cross-lingual learning and typological research. Currently, the treebank consists of 4,881 annotated tokens in accordance with the annotation scheme of Universal Dependencies (UD). A Bhojpuri tagger and parser were created using machine learning approach. The accuracy of the model is 57.49{\%} UAS, 45.50{\%} LAS, 79.69{\%} UPOS accuracy and 77.64{\%} XPOS accuracy. The paper describes the details of the project including a discussion on linguistic analysis and annotation process of the Bhojpuri UD treebank.
A Fully Expanded Dependency Treebank for Telugu
Authors: Sneha Nallani, Manish Shrivastava, Dipti Sharma
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Sentence

Treebanks are an essential resource for syntactic parsing. The available Paninian dependency treebank(s) for Telugu is annotated only with inter-chunk dependency relations and not all words of a sentence are part of the parse tree. In this paper, we automatically annotate the intra-chunk dependencies in the treebank using a Shift-Reduce parser based on Context Free Grammar rules for Telugu chunks. We also propose a few additional intra-chunk dependency relations for Telugu apart from the ones used in Hindi treebank. Annotating intra-chunk dependencies finally provides a complete parse tree for every sentence in the treebank. Having a fully expanded treebank is crucial for developing end to end parsers which produce complete trees. We present a fully expanded dependency treebank for Telugu consisting of 3220 sentences. In this paper, we also convert the treebank annotated with Anncorra part-of-speech tagset to the latest BIS tagset. The BIS tagset is a hierarchical tagset adopted as a unified part-of-speech standard across all Indian Languages. The final treebank is made publicly available.
Determination of Idiomatic Sentences in Paragraphs Using Statement Classification and Generalization of Grammar Rules
Authors: Naziya Shaikh
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: General Classification, Sentence, Translation, Word Translation

The translation systems are often not able to determine the presence of an idiom in a given paragraph. Due to this many systems tend to return the word-for-word translation of such statements leading to loss in the flavor of the idioms in the paragraph. This paper suggests a novel approach to efficiently determine probability of any statement in a given English paragraph to be an idiom. This approach combines the rule-based generalization of idioms in English language and classification of statements based on the context to determine the idioms in the sentence. The context based classification method can be used further for determination of idioms in regional Indian languages such as Marathi, Konkani and Hindi as the difference in the semantic context of the proverb as compared to the context in a paragraph is also evident in these other languages.
Polish Lexicon-Grammar Development Methodology as an Example for Application to other Languages
Authors: Zygmunt Vetulani, Gra{\.z}yna Vetulani
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

In the paper we present our methodology with the intention to propose it as a reference for creating lexicon-grammars. We share our long-term experience gained during research projects (past and on-going) concerning the description of Polish using this approach. The above-mentioned methodology, linking semantics and syntax, has revealed useful for various IT applications. Among other, we address this paper to researchers working on {``}less{''} or {``}middle-resourced{''} Indo-European languages as a proposal of a long term academic cooperation in the field. We believe that the confrontation of our lexicon-grammar methodology with other languages {--} Indo-European, but also Non-Indo-European languages of India, Ugro-Finish or Turkic languages in Eurasia {--} will allow for better understanding of the level of versatility of our approach and, last but not least, will create opportunities to intensify comparative studies. The reason of presenting some our works on language resources within the Wildre workshop is the intention not only to take up the challenge thrown down in the CFP of this workshop which is: {``}To provide opportunity for researchers from India to collaborate with researchers from other parts of the world{''}, but also to generalize this challenge to other languages.
Abstractive Text Summarization for Sanskrit Prose: A Study of Methods and Approaches
Authors: Shagun Sinha, Girish Jha
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Abstractive Text Summarization, Extractive Text Summarization, Information Retrieval, Retrieval, Text Summarization

The authors present a work-in-progress in the field of Abstractive Text Summarization (ATS) for Sanskrit Prose {--} a first attempt at ATS for Sanskrit (SATS). We will evaluate recent approaches and methods used for ATS and argue for the ones to be adopted for Sanskrit prose considering the unique properties of the language. There are three goals of SATS - to make manuscript summaries, to enrich the semantic processing of Sanskrit, and to improve the information retrieval systems in the language. While Extractive Text Summarization (ETS) is an important method, the summaries it generates are not always coherent. For qualitative coherent summaries, ATS is considered a better option by scholars. This paper reviews various ATS/ETS approaches for Sanskrit and other Indian Languages done till date. In the preliminary overview, authors conclude that of the two available approaches - structure-based and semantic-based - the latter would be viable owing to the rich morphology of Sanskrit. Moreover, a graph-based method may also be suitable. The second suggested method is the supervised-learning method. The authors also suggest attempting cross-lingual summarization as an extension to this work in future.
A Deeper Study on Features for Named Entity Recognition
Authors: Malarkodi C.S, Sobha Lalitha Devi
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: BIG-bench Machine Learning, feature selection, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

This paper deals with the various features used for the identification of named entities. The performance of the machine learning system heavily depends on the feature selection criteria. The intention to trace the essential features required for the development of named entity system across languages motivated us to conduct this study. The linguistic analysis was done to find out the part of speech patterns surrounding the context of named entities and from the observation linguistic oriented features are identified for both Indian and European languages. The Indian languages belongs to Dravidian language family such as Tamil, Telugu, Malayalam, Indo-Aryan language family such as Hindi, Punjabi, Bengali and Marathi, European languages such as English, Spanish, Dutch, German and Hungarian are used in this work. The machine learning technique CRFs was used for the system development. The experiments were conducted using the linguistic features and the results obtained for each languages are comparable with state-of-art systems.
Sequence Labeling Approach to the Task of Sentence Boundary Detection
Authors: The Anh Le
Date: 2020-01-20 00:00:00
Proceeding: ICMLSC 2020: Proceedings of the 4th International Conference on Machine Learning and Soft Computing 2020 1
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Boundary Detection, Chatbot, Intent Detection, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Part-Of-Speech Tagging, Sentence, Sentiment Analysis, speech-recognition, Speech Recognition, Structured Prediction, Topic Classification

One of the keys to enable chatbots to communicate with human in a more natural way is the ability to handle long and complex user's utterances. In order to achieve this goal, we propose to integrate the Sentence Boundary Detection (SBD) module into the chatbot architecture, whose role is to take as input a user's utterance from an automatic speech recognition device, in which sentence boundaries are not available, and output the corresponding list of punctuated sentences for downstream modules such as Intent Detection, Topic Classification, Sentiment Analysis, Named Entity Recognition, as well as Coreference Recognition. To address the SBD task, we reformulate it as a sequence labeling task. In this way, both deep neural network models (e.g., Bi-directional Long Short-Term Memory, Convolutional Neural Network) and structured prediction models (e.g., Hidden Markov Model, Maximum Entropy Model, Conditional Random Field) can be leveraged. After reformulating the SBD task, we built a hybrid deep neural network model and achieved good performance on both CornellMovie-Dialog and DailyDialog datasets.
Mandarinograd: A Chinese Collection of Winograd Schemas
Authors: Timoth{\'e}e Bernard, Ting Han
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Common Sense Reasoning, Natural Language Inference, Natural Language Understanding

This article introduces Mandarinograd, a corpus of Winograd Schemas in Mandarin Chinese. Winograd Schemas are particularly challenging anaphora resolution problems, designed to involve common sense reasoning and to limit the biases and artefacts commonly found in natural language understanding datasets. Mandarinograd contains the schemas in their traditional form, but also as natural language inference instances (ENTAILMENT or NO ENTAILMENT pairs) as well as in their fully disambiguated candidate forms. These two alternative representations are often used by modern solvers but existing datasets present automatically converted items that sometimes contain syntactic or semantic anomalies. We detail the difficulties faced when building this corpus and explain how weavoided the anomalies just mentioned. We also show that Mandarinograd is resistant to a statistical method based on a measure of word association.
On the Influence of Coreference Resolution on Word Embeddings in Lexical-semantic Evaluation Tasks
Authors: Alex Henlein, Alex Mehler, er
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: coreference-resolution, Coreference Resolution, Word Embeddings

Coreference resolution (CR) aims to find all spans of a text that refer to the same entity. The F1-Scores on these task have been greatly improved by new developed End2End-approaches and transformer networks. The inclusion of CR as a pre-processing step is expected to lead to improvements in downstream tasks. The paper examines this effect with respect to word embeddings. That is, we analyze the effects of CR on six different embedding methods and evaluate them in the context of seven lexical-semantic evaluation tasks and instantiation/hypernymy detection. Especially in the last tasks we hoped for a significant increase in performance. We show that all word embedding approaches do not benefit significantly from pronoun substitution. The measurable improvements are only marginal (around 0.5{\%} in most test cases). We explain this result with the loss of contextual information, reduction of the relative occurrence of rare words and the lack of pronouns to be replaced.