pwc-archive/papers-with-abstracts huggingface dataset:
EmoTag -- Towards an Emotion-Based Analysis of Emojis
Authors: Abu Awal Md Shoeb, Shahab Raji, Gerard de Melo
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9

Despite being a fairly recent phenomenon, emojis have quickly become ubiquitous. Besides their extensive use in social media, they are now also invoked in customer surveys and feedback forms. Hence, there is a need for techniques to understand their sentiment and emotion. In this work, we provide a method to quantify the emotional association of basic emotions such as anger, fear, joy, and sadness for a set of emojis. We collect and process a unique corpus of 20 million emoji-centric tweets, such that we can capture rich emoji semantics using a comparably small dataset. We evaluate the induced emotion profiles of emojis with regard to their ability to predict word affect intensities as well as sentiment scores.
Named Entity Recognition in Information Security Domain for Russian
Authors: Anastasiia Sirotina, Natalia Loukachevitch
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

In this paper we discuss the named entity recognition task for Russian texts related to cybersecurity. First of all, we describe the problems that arise in course of labeling unstructured texts from information security domain. We introduce guidelines for human annotators, according to which a corpus has been marked up. Then, a CRF-based system and different neural architectures have been implemented and applied to the corpus. The named entity recognition systems have been evaluated and compared to determine the most efficient one.
Cross-Family Similarity Learning for Cognate Identification in Low-Resource Languages
Authors: Eliel Soisalon-Soininen, Mark Granroth-Wilding
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9

We address the problem of cognate identification across vocabulary pairs of any set of languages. In particular, we focus on the case where the examined pair of languages are low-resource to the extent that no training data whatsoever in these languages, or even closely related ones, are available for the task. We investigate the extent to which training data from another, unrelated language family can be used instead. Our approach consists of learning a similarity metric from example cognates in Indo-European languages and applying it to low-resource Sami languages of the Uralic family. We apply two models following previous work: a Siamese convolutional neural network (S-CNN) and a support vector machine (SVM), and compare them with a Levenshtein-distance baseline. We test performance on three Sami languages and find that the S-CNN outperforms the other approaches, suggesting that it is better able to learn such general characteristics of cognateness that carry over across language families. We also experiment with fine-tuning the S-CNN model with data from within the language family in order to quantify how well this model can make use of a small amount of target-domain data to adapt.
Automatic Detection of Translation Direction
Authors: Ilia Sominsky, Shuly Wintner
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Machine Translation, Sentence, Translation

Parallel corpora are crucial resources for NLP applications, most notably for machine translation. The direction of the (human) translation of parallel corpora has been shown to have significant implications for the quality of statistical machine translation systems that are trained with such corpora. We describe a method for determining the direction of the (manual) translation of parallel corpora at the sentence-pair level. Using several linguistically-motivated features, coupled with a neural network model, we obtain high accuracy on several language pairs. Furthermore, we demonstrate that the accuracy is correlated with the (typological) distance between the two languages.
Automated Text Simplification as a Preprocessing Step for Machine Translation into an Under-resourced Language
Authors: Sanja {\v{S}}tajner, Maja Popovi{\'c}
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Machine Translation, NMT, Text Simplification, Translation

In this work, we investigate the possibility of using fully automatic text simplification system on the English source in machine translation (MT) for improving its translation into an under-resourced language. We use the state-of-the-art automatic text simplification (ATS) system for lexically and syntactically simplifying source sentences, which are then translated with two state-of-the-art English-to-Serbian MT systems, the phrase-based MT (PBMT) and the neural MT (NMT). We explore three different scenarios for using the ATS in MT: (1) using the raw output of the ATS; (2) automatically filtering out the sentences with low grammaticality and meaning preservation scores; and (3) performing a minimal manual correction of the ATS output. Our results show improvement in fluency of the translation regardless of the chosen scenario, and difference in success of the three scenarios depending on the MT approach used (PBMT or NMT) with regards to improving translation fluency and post-editing effort.
Investigating Multilingual Abusive Language Detection: A Cautionary Tale
Authors: Kenneth Steimel, Daniel Dakota, Yue Chen, S K{\"u}bler, ra
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Abusive Language

Abusive language detection has received much attention in the last years, and recent approaches perform the task in a number of different languages. We investigate which factors have an effect on multilingual settings, focusing on the compatibility of data and annotations. In the current paper, we focus on English and German. Our findings show large differences in performance between the two languages. We find that the best performance is achieved by different classification algorithms. Sampling to address class imbalance issues is detrimental for German and beneficial for English. The only similarity that we find is that neither data set shows clear topics when we compare the results of topic modeling to the gold standard. Based on our findings, we can conclude that a multilingual optimization of classifiers is not possible even in settings where comparable data sets are used.
Comparison of Machine Learning Approaches for Industry Classification Based on Textual Descriptions of Companies
Authors: Andrey Tagarev, Nikola Tulechki, Svetla Boytcheva
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Articles, BIG-bench Machine Learning, Classification, General Classification, text-classification, Text Classification

This paper addresses the task of categorizing companies within industry classification schemes. The datasets consists of encyclopedic articles about companies and their economic activities. The target classification schema is build by mapping linked open data in a semi-supervised manner. Target classes are build bottom-up from DBpedia. We apply several state of the art text classification techniques, based both on deep-learning and classical vector-space models.
A Quantum-Like Approach to Word Sense Disambiguation
Authors: Fabio Tamburini
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Sentence, Sentence Embeddings, Word Sense Disambiguation

This paper presents a novel algorithm for Word Sense Disambiguation (WSD) based on Quantum Probability Theory. The Quantum WSD algorithm requires concepts representations as vectors in the complex domain and thus we have developed a technique for computing complex word and sentence embeddings based on the Paragraph Vectors algorithm. Despite the proposed method is quite simple and that it does not require long training phases, when it is evaluated on a standardized benchmark for this task it exhibits state-of-the-art (SOTA) performances.
Text-Based Joint Prediction of Numeric and Categorical Attributes of Entities in Knowledge Bases
Authors: V Thejas, Abhijeet Gupta, Sebastian Pad{\'o}
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Attribute, Knowledge Base Completion, Multi-Task Learning, Prediction

Collaboratively constructed knowledge bases play an important role in information systems, but are essentially always incomplete. Thus, a large number of models has been developed for Knowledge Base Completion, the task of predicting new attributes of entities given partial descriptions of these entities. Virtually all of these models either concentrate on numeric attributes ({\textless}Italy,GDP,2T{\$}{\textgreater}) or they concentrate on categorical attributes ({\textless}Tim Cook,chairman,Apple{\textgreater}). In this paper, we propose a simple feed-forward neural architecture to jointly predict numeric and categorical attributes based on embeddings learned from textual occurrences of the entities in question. Following insights from multi-task learning, our hypothesis is that due to the correlations among attributes of different kinds, joint prediction improves over separate prediction. Our experiments on seven FreeBase domains show that this hypothesis is true of the two attribute types: we find substantial improvements for numeric attributes in the joint model, while performance remains largely unchanged for categorical attributes. Our analysis indicates that this is the case because categorical attributes, many of which describe membership in various classes, provide useful {`}background knowledge{'} for numeric prediction, while this is true to a lesser degree in the inverse direction.
SenZi: A Sentiment Analysis Lexicon for the Latinised Arabic (Arabizi)
Authors: Taha Tobaili, Fern, Miriam ez, Harith Alani, Sanaa Sharafeddine, Hazem Hajj, Goran Glava{\v{s}}
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Sentiment Analysis, Word Embeddings

Arabizi is an informal written form of dialectal Arabic transcribed in Latin alphanumeric characters. It has a proven popularity on chat platforms and social media, yet it suffers from a severe lack of natural language processing (NLP) resources. As such, texts written in Arabizi are often disregarded in sentiment analysis tasks for Arabic. In this paper we describe the creation of a sentiment lexicon for Arabizi that was enriched with word embeddings. The result is a new Arabizi lexicon consisting of 11.3K positive and 13.3K negative words. We evaluated this lexicon by classifying the sentiment of Arabizi tweets achieving an F1-score of 0.72. We provide a detailed error analysis to present the challenges that impact the sentiment analysis of Arabizi.
Mining the UK Web Archive for Semantic Change Detection
Authors: Adam Tsakalidis, Marya Bazzi, Mihai Cucuringu, Pierpaolo Basile, Barbara McGillivray
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Change Detection

Semantic change detection (i.e., identifying words whose meaning has changed over time) started emerging as a growing area of research over the past decade, with important downstream applications in natural language processing, historical linguistics and computational social science. However, several obstacles make progress in the domain slow and difficult. These pertain primarily to the lack of well-established gold standard datasets, resources to study the problem at a fine-grained temporal resolution, and quantitative evaluation approaches. In this work, we aim to mitigate these issues by (a) releasing a new labelled dataset of more than 47K word vectors trained on the UK Web Archive over a short time-frame (2000-2013); (b) proposing a variant of Procrustes alignment to detect words that have undergone semantic shift; and (c) introducing a rank-based approach for evaluation purposes. Through extensive numerical experiments and validation, we illustrate the effectiveness of our approach against competitive baselines. Finally, we also make our resources publicly available to further enable research in the domain.
Cross-Lingual Word Embeddings for Morphologically Rich Languages
Authors: Ahmet {\"U}st{\"u}n, Gosse Bouma, Gertjan van Noord
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Cross-Lingual Word Embeddings, Translation, Word Embeddings, Word Translation

Cross-lingual word embedding models learn a shared vector space for two or more languages so that words with similar meaning are represented by similar vectors regardless of their language. Although the existing models achieve high performance on pairs of morphologically simple languages, they perform very poorly on morphologically rich languages such as Turkish and Finnish. In this paper, we propose a morpheme-based model in order to increase the performance of cross-lingual word embeddings on morphologically rich languages. Our model includes a simple extension which enables us to exploit morphemes for cross-lingual mapping. We applied our model for the Turkish-Finnish language pair on the bilingual word translation task. Results show that our model outperforms the baseline models by 2{\%} in the nearest neighbour ranking.
Deep learning contextual models for prediction of sport event outcome from sportsman's interviews
Authors: Boris Velichkov, Ivan Koychev, Svetla Boytcheva
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9

This paper presents an approach for prediction of results for sport events. Usually the sport forecasting approaches are based on structured data. We test the hypothesis that the sports results can be predicted by using natural language processing and machine learning techniques applied over interviews with the players shortly before the sport events. The proposed method uses deep learning contextual models, applied over unstructured textual documents. Several experiments were performed for interviews with players in individual sports like boxing, martial arts, and tennis. The results from the conducted experiment confirmed our initial assumption that an interview from a sportsman before a match contains information that can be used for prediction the outcome from it. Furthermore, the results provide strong evidence in support of our research hypothesis, that is, we can predict the outcome from a sport match analyzing an interview, given before it.
Exploiting Frame-Semantics and Frame-Semantic Parsing for Automatic Extraction of Typological Information from Descriptive Grammars of Natural Languages
Authors: Shafqat Mumtaz Virk, Azam Sheikh Muhammad, Lars Borin, Muhammad Irfan Aslam, Saania Iqbal, Nazia Khurram
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Descriptive, Semantic Parsing

We describe a novel system for automatic extraction of typological linguistic information from descriptive grammars of natural languages, applying the theory of frame semantics in the form of frame-semantic parsing. The current proof-of-concept system covers a few selected linguistic features, but the methodology is general and can be extended not only to other typological features but also to descriptive grammars written in languages other than English. Such a system is expected to be a useful assistance for automatic curation of typological databases which otherwise are built manually, a very labor and time consuming as well as cognitively taxing enterprise.
Exploiting Open IE for Deriving Multiple Premises Entailment Corpus
Authors: Martin V{\'\i}ta, Jakub Kl{\'\i}mek
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Natural Language Inference, Natural Language Understanding, Open Information Extraction, Sentence

Natural language inference (NLI) is a key part of natural language understanding. The NLI task is defined as a decision problem whether a given sentence {--} hypothesis {--} can be inferred from a given text. Typically, we deal with a text consisting of just a single premise/single sentence, which is called a single premise entailment (SPE) task. Recently, a derived task of NLI from multiple premises (MPE) was introduced together with the first annotated corpus and corresponding several strong baselines. Nevertheless, the further development in MPE field requires accessibility of huge amounts of annotated data. In this paper we introduce a novel method for rapid deriving of MPE corpora from an existing NLI (SPE) annotated data that does not require any additional annotation work. This proposed approach is based on using an open information extraction system. We demonstrate the application of the method on a well known SNLI corpus. Over the obtained corpus, we provide the first evaluations as well as we state a strong baseline.
Towards Adaptive Text Summarization: How Does Compression Rate Affect Summary Readability of L2 Texts?
Authors: Tatiana Vodolazova, Elena Lloret
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Abstractive Text Summarization, Text Summarization

This paper addresses the problem of readability of automatically generated summaries in the context of second language learning. For this we experimented with a new corpus of level-annotated simplified English texts. The texts were summarized using a total of 7 extractive and abstractive summarization systems with compression rates of 20{\%}, 40{\%}, 60{\%} and 80{\%}. We analyzed the generated summaries in terms of lexical, syntactic and length-based features of readability, and concluded that summary complexity depends on the compression rate, summarization technique and the nature of the summarized corpus. Our experiments demonstrate the importance of choosing appropriate summarization techniques that align with user{'}s needs and language proficiency.
The Impact of Rule-Based Text Generation on the Quality of Abstractive Summaries
Authors: Tatiana Vodolazova, Elena Lloret
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Abstractive Text Summarization, Informativeness, Text Generation, Text Simplification, Text Summarization

In this paper we describe how an abstractive text summarization method improved the informativeness of automatic summaries by integrating syntactic text simplification, subject-verb-object concept frequency scoring and a set of rules that transform text into its semantic representation. We analyzed the impact of each component of our approach on the quality of generated summaries and tested it on DUC 2002 dataset. Our experiments showed that our approach outperformed other state-of-the-art abstractive methods while maintaining acceptable linguistic quality and redundancy rate.
Tagger for Polish Computer Mediated Communication Texts
Authors: Wiktor Walentynowicz, Maciej Piasecki, Marcin Oleksy
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Clustering, Sentence

In this paper we present a morpho-syntactic tagger dedicated to Computer-mediated Communication texts in Polish. Its construction is based on an expanded RNN-based neural network adapted to the work on noisy texts. Among several techniques, the tagger utilises fastText embedding vectors, sequential character embedding vectors, and Brown clustering for the coarse-grained representation of sentence structures. In addition a set of manually written rules was proposed for post-processing. The system was trained to disambiguate descriptions of words in relation to Parts of Speech tags together with the full morphological information in terms of values for the different grammatical categories. We present also evaluation of several model variants on the gold standard annotated CMC data, comparison to the state-of-the-art taggers for Polish and error analysis. The proposed tagger shows significantly better results in this domain and demonstrates the viability of adaptation.
Evaluation of vector embedding models in clustering of text documents
Authors: Tomasz Walkowiak, Mateusz Gniewkowski
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Clustering

The paper presents an evaluation of word embedding models in clustering of texts in the Polish language. Authors verified six different embedding models, starting from widely used word2vec, across fastText with character n-grams embedding, to deep learning-based ELMo and BERT. Moreover, four standardisation methods, three distance measures and four clustering methods were evaluated. The analysis was performed on two corpora of texts in Polish classified into subjects. The Adjusted Mutual Information (AMI) metric was used to verify the quality of clustering results. The performed experiments show that Skipgram models with n-grams character embedding, built on KGR10 corpus and provided by Clarin-PL, outperforms other publicly available models for Polish. Moreover, presented results suggest that Yeo{--}Johnson transformation for document vectors standardisation and Agglomerative Clustering with a cosine distance should be used for grouping of text documents.
Bigger versus Similar: Selecting a Background Corpus for First Story Detection Based on Distributional Similarity
Authors: Fei Wang, Robert J. Ross, John D. Kelleher
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9

The current state of the art for First Story Detection (FSD) are nearest neighbour-based models with traditional term vector representations; however, one challenge faced by FSD models is that the document representation is usually defined by the vocabulary and term frequency from a background corpus. Consequently, the ideal background corpus should arguably be both large-scale to ensure adequate term coverage, and similar to the target domain in terms of the language distribution. However, given these two factors cannot always be mutually satisfied, in this paper we examine whether the distributional similarity of common terms is more important than the scale of common terms for FSD. As a basis for our analysis we propose a set of metrics to quantitatively measure the scale of common terms and the distributional similarity between corpora. Using these metrics we rank different background corpora relative to a target corpus. We also apply models based on different background corpora to the FSD task. Our results show that term distributional similarity is more predictive of good FSD performance than the scale of common terms; and, thus we demonstrate that a smaller recent domain-related corpus will be more suitable than a very large-scale general corpus for FSD.
Predicting Sentiment of Polish Language Short Texts
Authors: Aleks Wawer, er, Julita Sobiczewska
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Sentiment Analysis

The goal of this paper is to use all available Polish language data sets to seek the best possible performance in supervised sentiment analysis of short texts. We use text collections with labelled sentiment such as tweets, movie reviews and a sentiment treebank, in three comparison modes. In the first, we examine the performance of models trained and tested on the same text collection using standard cross-validation (in-domain). In the second we train models on all available data except the given test collection, which we use for testing (one vs rest cross-domain). In the third, we train a model on one data set and apply it to another one (one vs one cross-domain). We compare wide range of methods including machine learning on bag-of-words representation, bidirectional recurrent neural networks as well as the most recent pre-trained architectures ELMO and BERT. We formulate conclusions as to cross-domain and in-domain performance of each method. Unsurprisingly, BERT turned out to be a strong performer, especially in the cross-domain setting. What is surprising however, is solid performance of the relatively simple multinomial Naive Bayes classifier, which performed equally well as BERT on several data sets.