pwc-archive/papers-with-abstracts huggingface dataset:
The Impact of Semantic Linguistic Features in Relation Extraction: A Logical Relational Learning Approach
Authors: Rinaldo Lima, Bernard Espinasse, Frederico Freitas
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Relation, Relational Reasoning, Relation Extraction, Sentence

Relation Extraction (RE) consists in detecting and classifying semantic relations between entities in a sentence. The vast majority of the state-of-the-art RE systems relies on morphosyntactic features and supervised machine learning algorithms. This paper tries to answer important questions concerning both the impact of semantic based features, and the integration of external linguistic knowledge resources on RE performance. For that, a RE system based on a logical and relational learning algorithm was used and evaluated on three reference datasets from two distinct domains. The yielded results confirm that the classifiers induced using the proposed richer feature set outperformed the classifiers built with morphosyntactic features in average 4{\%} (F1-measure).
Detecting Anorexia in Spanish Tweets
Authors: Pilar L{\'o}pez {\'U}beda, Flor Miriam Plaza del Arco, Manuel Carlos D{\'\i}az Galiano, L. Alfonso Urena Lopez, Maite Martin
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Anorexia Detection

Mental health is one of the main concerns of today{'}s society. Early detection of symptoms can greatly help people with mental disorders. People are using social networks more and more to express emotions, sentiments and mental states. Thus, the treatment of this information using NLP technologies can be applied to the automatic detection of mental problems such as eating disorders. However, the first step to solving the problem should be to provide a corpus in order to evaluate our systems. In this paper, we specifically focus on detecting anorexia messages on Twitter. Firstly, we have generated a new corpus of tweets extracted from different accounts including anorexia and non-anorexia messages in Spanish. The corpus is called SAD: Spanish Anorexia Detection corpus. In order to validate the effectiveness of the SAD corpus, we also propose several machine learning approaches for automatically detecting anorexia symptoms in the corpus. The good results obtained show that the application of textual classification methods is a promising option for developing this kind of system demonstrating that these tools could be used by professionals to help in the early detection of mental problems.
A type-theoretical reduction of morphological, syntactic and semantic compositionality to a single level of description
Authors: Erkki Luuk
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9

The paper presents NLC, a new formalism for modeling natural language (NL) compositionality. NLC is a functional type system (i.e. one based on mathematical functions and their types). Its main features include a close correspondence with NL and an integrated modeling of morphological, syntactic and semantic compositionality. The integration is effected with a subclass of compound types (types which are syntactic compounds of multiple types or their terms), while the correspondence is sought with function types and polymorphism. The paper also presents an implementation of NLC in Coq. The implementation formalizes a diverse fragment of NL, with NLC expressions type checking and failing to type check in exactly the same ways that NL expressions pass and fail their acceptability tests. Among other things, this demonstrates the possibility of reducing morphological, syntactic and semantic compositionality to a single level of description. The level is tentatively identified with semantic compositionality {---} an interpretation which, besides being supported by results from language processing, has interesting implications on NL structure and modeling.
v-trel: Vocabulary Trainer for Tracing Word Relations - An Implicit Crowdsourcing Approach
Authors: Verena Lyding, Christos Rodosthenous, Federico Sangati, Umair ul Hassan, Lionel Nicolas, Alex K{\"o}nig, er, Jolita Horbacauskiene, Anisia Katinskaia
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9

In this paper, we present our work on developing a vocabulary trainer that uses exercises generated from language resources such as ConceptNet and crowdsources the responses of the learners to enrich the language resource. We performed an empirical evaluation of our approach with 60 non-native speakers over two days, which shows that new entries to expand Concept-Net can efficiently be gathered through vocabulary exercises on word relations. We also report on the feedback gathered from the users and an expert from language teaching, and discuss the potential of the vocabulary trainer application from the user and language learner perspective. The feedback suggests that v-trel has educational potential, while in its current state some shortcomings could be identified.
Jointly Learning Author and Annotated Character N-gram Embeddings: A Case Study in Literary Text
Authors: Suraj Maharjan, Deepthi Mave, Prasha Shrestha, Manuel Montes, Fabio A. Gonz{\'a}lez, Thamar Solorio
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Authorship Attribution, Genre classification, Language Modeling, Language Modelling

An author{'}s way of presenting a story through his/her writing style has a great impact on whether the story will be liked by readers or not. In this paper, we learn representations for authors of literary texts together with representations for character n-grams annotated with their functional roles. We train a neural character n-gram based language model using an external corpus of literary texts and transfer learned representations for use in downstream tasks. We show that augmenting the knowledge from external works of authors produces results competitive with other style-based methods for book likability prediction, genre classification, and authorship attribution.
Sentiment Polarity Detection in Azerbaijani Social News Articles
Authors: Sevda Mammadli, Shamsaddin Huseynov, Huseyn Alkaramov, Ulviyya Jafarli, Umid Suleymanov, Samir Rustamov
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Articles, Classification, General Classification, Sentiment Analysis, text-classification, Text Classification

Text classification field of natural language processing has been experiencing remarkable growth in recent years. Especially, sentiment analysis has received a considerable attention from both industry and research community. However, only a few research examples exist for Azerbaijani language. The main objective of this research is to apply various machine learning algorithms for determining the sentiment of news articles in Azerbaijani language. Approximately, 30.000 social news articles have been collected from online news sites and labeled manually as negative or positive according to their sentiment categories. Initially, text preprocessing was implemented to data in order to eliminate the noise. Secondly, to convert text to a more machine-readable form, BOW (bag of words) model has been applied. More specifically, two methodologies of BOW model, which are tf-idf and frequency based model have been used as vectorization methods. Additionally, SVM, Random Forest, and Naive Bayes algorithms have been applied as the classification algorithms, and their combinations with two vectorization approaches have been tested and analyzed. Experimental results indicate that SVM outperforms other classification algorithms.
Inforex --- a Collaborative Systemfor Text Corpora Annotation and Analysis Goes Open
Authors: Micha{\l} Marci{\'n}czuk, Marcin Oleksy
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Attribute, Morphological Disambiguation, Morphological Tagging, text annotation

In the paper we present the latest changes introduce to Inforex {---} a web-based system for qualitative and collaborative text corpora annotation and analysis. One of the most important news is the release of source codes. Now the system is available on the GitHub repository (https://github.com/CLARIN-PL/Inforex) as an open source project. The system can be easily setup and run in a Docker container what simplifies the installation process. The major improvements include: semi-automatic text annotation, multilingual text preprocessing using CLARIN-PL web services, morphological tagging of XML documents, improved editor for annotation attribute, batch annotation attribute editor, morphological disambiguation, extended word sense annotation. This paper contains a brief description of the mentioned improvements. We also present two use cases in which various Inforex features were used and tested in real-life projects.
Semantic Language Model for Tunisian Dialect
Authors: Abir MASMOUDI, Rim Laatar, Mariem Ellouze, lamia hadrich belguith
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, model, speech-recognition, Speech Recognition

In this paper, we describe the process of creating a statistical Language Model (LM) for the Tunisian Dialect. Indeed, this work is part of the realization of Automatic Speech Recognition (ASR) system for the Tunisian Railway Transport Network. Since our eld of work has been limited, there are several words with similar behaviors (semantic for example) but they do not have the same appearance probability; their class groupings will therefore be possible. For these reasons, we propose to build an n-class LM that is based mainly on the integration of purely semantic data. Indeed, each class represents an abstraction of similar labels. In order to improve the sequence labeling task, we proposed to use a discriminative algorithm based on the Conditional Random Field (CRF) model. To better judge our choice of creating an n-class word model, we compared the created model with the 3-gram type model on the same test corpus of evaluation. Additionally, to assess the impact of using the CRF model to perform the semantic labelling task in order to construct semantic classes, we compared the n-class created model with using the CRF in the semantic labelling task and the n- class model without using the CRF in the semantic labelling task. The drawn comparison of the predictive power of the n-class model obtained by applying the CRF model in the semantic labelling is that it is better than the other two models presenting the highest value of its perplexity.
Automatic diacritization of Tunisian dialect text using Recurrent Neural Network
Authors: Abir Masmoudi, Mariem Ellouze, lamia hadrich belguith
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9

The absence of diacritical marks in the Arabic texts generally leads to morphological, syntactic and semantic ambiguities. This can be more blatant when one deals with under-resourced languages, such as the Tunisian dialect, which suffers from unavailability of basic tools and linguistic resources, like sufficient amount of corpora, multilingual dictionaries, morphological and syntactic analyzers. Thus, this language processing faces greater challenges due to the lack of these resources. The automatic diacritization of MSA text is one of the various complex problems that can be solved by deep neural networks today. Since the Tunisian dialect is an under-resourced language of MSA and as there are a lot of resemblance between both languages, we suggest to investigate a recurrent neural network (RNN) for this dialect diacritization problem. This model will be compared to our previous models models CRF and SMT (CITATION) based on the same dialect corpus. We can experimentally show that our model can achieve better outcomes (DER of 10.72{\%}), as compared to the two models CRF (DER of 20.25{\%}) and SMT (DER of 33.15{\%}).
Comparing MT Approaches for Text Normalization
Authors: Claudia Matos Veliz, Orphee De Clercq, Veronique Hoste
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Data Augmentation, Language Modeling, Language Modelling, Machine Translation, NMT, Text Normalization, Translation

One of the main characteristics of social media data is the use of non-standard language. Since NLP tools have been trained on traditional text material their performance drops when applied to social media data. One way to overcome this is to first perform text normalization. In this work, we apply text normalization to noisy English and Dutch text coming from different social media genres: text messages, message board posts and tweets. We consider the normalization task as a Machine Translation problem and test the two leading paradigms: statistical and neural machine translation. For SMT we explore the added value of varying background corpora for training the language model. For NMT we have a look at data augmentation since the parallel datasets we are working with are limited in size. Our results reveal that when relying on SMT to perform the normalization it is beneficial to use a background corpus that is close to the genre you are normalizing. Regarding NMT, we find that the translations - or normalizations - coming out of this model are far from perfect and that for a low-resource language like Dutch adding additional training data works better than artificially augmenting the data.
Sentiment and Emotion Based Representations for Fake Reviews Detection
Authors: Alimuddin Melleng, Anna Jurek-Loughrey, Deepak P
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Emotion Recognition

Fake reviews are increasingly prevalent across the Internet. They can be unethical as well as harmful. They can affect businesses and mislead individual customers. As the opinions on the Web are increasingly used the detection of fake reviews has become more and more critical. In this study, we explore the effectiveness of sentiment and emotions based representations for the task of building machine learning models for fake review detection. We perform empirical studies over three real world datasets and demonstrate that improved data representation can be achieved by combining sentiment and emotion extraction methods, as well as by performing sentiment and emotion analysis on a part-by-part basis by segmenting the reviews.
Turning silver into gold: error-focused corpus reannotation with active learning
Authors: Pierre Andr{\'e} M{\'e}nard, Antoine Mougeot
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Active Learning, Document Classification, Part-Of-Speech Tagging

While high quality gold standard annotated corpora are crucial for most tasks in natural language processing, many annotated corpora published in recent years, created by annotators or tools, contains noisy annotations. These corpora can be viewed as more silver than gold standards, even if they are used in evaluation campaigns or to compare systems{'} performances. As upgrading a silver corpus to gold level is still a challenge, we explore the application of active learning techniques to detect errors using four datasets designed for document classification and part-of-speech tagging. Our results show that the proposed method for the seeding step improves the chance of finding incorrect annotations by a factor of 2.73 when compared to random selection, a 14.71{\%} increase from the baseline methods. Our query method provides an increase in the error detection precision on average by a factor of 1.78 against random selection, an increase of 61.82{\%} compared to other query approaches.
Community Perspective on Replicability in Natural Language Processing
Authors: Margot Mieskes, Kar{\"e}n Fort, Aur{\'e}lie N{\'e}v{\'e}ol, Cyril Grouin, Kevin Cohen
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Survey

With recent efforts in drawing attention to the task of replicating and/or reproducing results, for example in the context of COLING 2018 and various LREC workshops, the question arises how the NLP community views the topic of replicability in general. Using a survey, in which we involve members of the NLP community, we investigate how our community perceives this topic, its relevance and options for improvement. Based on over two hundred participants, the survey results confirm earlier observations, that successful reproducibility requires more than having access to code and data. Additionally, the results show that the topic has to be tackled from the authors{'}, reviewers{'} and community{'}s side.
Unsupervised Data Augmentation for Less-Resourced Languages with no Standardized Spelling
Authors: Alice Millour, Kar{\"e}n Fort
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Data Augmentation, Diversity, Part-Of-Speech Tagging

Building representative linguistic resources and NLP tools for non-standardized languages is challenging: when spelling is not determined by a norm, multiple written forms can be encountered for a given word, inducing a large proportion of out-of-vocabulary words. To embrace this diversity, we propose a methodology based on crowdsourced alternative spellings we use to extract rules applied to match OOV words with one of their spelling variants. This virtuous process enables the unsupervised augmentation of multi-variant lexicons without expert rule definition. We apply this multilingual methodology on Alsatian, a French regional language and provide an intrinsic evaluation of the correctness of the variants pairs, and an extrinsic evaluation on a downstream task. We show that in a low-resource scenario, 145 inital pairs can lead to the generation of 876 additional variant pairs, and a diminution of OOV words improving the part-of-speech tagging performance by 1 to 4{\%}.
Neural Feature Extraction for Contextual Emotion Detection
Authors: Elham Mohammadi, Hessam Amini, Leila Kosseim
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Emotion Recognition in Conversation, POS, Word Embeddings

This paper describes a new approach for the task of contextual emotion detection. The approach is based on a neural feature extractor, composed of a recurrent neural network with an attention mechanism, followed by a classifier, that can be neural or SVM-based. We evaluated the model with the dataset of the task 3 of SemEval 2019 (EmoContext), which includes short 3-turn conversations, tagged with 4 emotion classes. The best performing setup was achieved using ELMo word embeddings and POS tags as input, bidirectional GRU as hidden units, and an SVM as the final classifier. This configuration reached 69.93{\%} in terms of micro-average F1 score on the main 3 emotion classes, a score that outperformed the baseline system by 11.25{\%}.
A Fast and Accurate Partially Deterministic Morphological Analysis
Authors: Hajime Morita, Tomoya Iwakura
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Morphological Analysis, Sentence

This paper proposes a partially deterministic morphological analysis method for improved processing speed. Maximum matching is a fast deterministic method for morphological analysis. However, the method tends to decrease performance due to lack of consideration of contextual information. In order to use maximum matching safely, we propose the use of Context Independent Strings (CISs), which are strings that do not have ambiguity in terms of morphological analysis. Our method first identifies CISs in a sentence using maximum matching without contextual information, then analyzes the unprocessed part of the sentence using a bi-gram-based morphological analysis model. We evaluate the method on a Japanese morphological analysis task. The experimental results show a 30{\%} reduction of running time while maintaining improved accuracy.
incom.py - A Toolbox for Calculating Linguistic Distances and Asymmetries between Related Languages
Authors: Marius Mosbach, Irina Stenger, Tania Avgustinova, Dietrich Klakow
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9

Languages may be differently distant from each other and their mutual intelligibility may be asymmetric. In this paper we introduce incom.py, a toolbox for calculating linguistic distances and asymmetries between related languages. incom.py allows linguist experts to quickly and easily perform statistical analyses and compare those with experimental results. We demonstrate the efficacy of incom.py in an incomprehension experiment on two Slavic languages: Bulgarian and Russian. Using incom.py we were able to validate three methods to measure linguistic distances and asymmetries: Levenshtein distance, word adaptation surprisal, and conditional entropy as predictors of success in a reading intercomprehension experiment.
Building a Comprehensive Romanian Knowledge Base for Drug Administration
Authors: Bogdan Nicula, Mihai Dascalu, Maria-Dorinela S{\^\i}rbu, {\textcommabelow{S}}tefan Tr{\u{a}}u{\textcommabelow{s}}an-Matu, Alex Nu{\textcommabelow{t}}{\u{a}}, ru
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9

Information on drug administration is obtained traditionally from doctors and pharmacists, as well as leaflets which provide in most cases cumbersome and hard-to-follow details. Thus, the need for medical knowledge bases emerges to provide access to concrete and well-structured information which can play an important role in informing patients. This paper introduces a Romanian medical knowledge base focused on drug-drug interactions, on representing relevant drug information, and on symptom-disease relations. The knowledge base was created by extracting and transforming information using Natural Language Processing techniques from both structured and unstructured sources, together with manual annotations. The resulting Romanian ontologies are aligned with larger English medical ontologies. Our knowledge base supports queries regarding drugs (e.g., active ingredients, concentration, expiration date), drug-drug interaction, symptom-disease relations, as well as drug-symptom relations.
Dependency-Based Relative Positional Encoding for Transformer NMT
Authors: Yutaro Omote, Akihiro Tamura, Takashi Ninomiya
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Machine Translation, NMT, Position, Translation

This paper proposes a new Transformer neural machine translation model that incorporates syntactic distances between two source words into the relative position representations of the self-attention mechanism. In particular, the proposed model encodes pair-wise relative depths on a source dependency tree, which are differences between the depths of the two source words, in the encoder{'}s self-attention. The experiments show that our proposed model achieves 0.5 point gain in BLEU on the Asian Scientific Paper Excerpt Corpus Japanese-to-English translation task.
From Image to Text in Sentiment Analysis via Regression and Deep Learning
Authors: Daniela Onita, Liviu P. Dinu, Adriana Birlutiu
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Image to text, regression, Sentiment Analysis

Images and text represent types of content which are used together for conveying user emotions in online social networks. These contents are usually associated with a sentiment category. In this paper, we investigate an approach for mapping images to text for three types of sentiment categories: positive, neutral and negative. The mapping from images to text is performed using a Kernel Ridge Regression model. We considered two types of image features: i) RGB pixel-values features, and ii) features extracted with a deep learning approach. The experimental evaluation was performed on a Twitter data set containing both text and images and the sentiment associated with these. The experimental results show a difference in performance for different sentiment categories, in particular the mapping that we propose performs better for the positive sentiment category in comparison with the neutral and negative ones. Furthermore, the experimental results show that the more complex deep learning features perform better than the RGB pixel-value features for all sentiment categories and for larger training sets.