pwc-archive/papers-with-abstracts huggingface dataset:
Speculation and Negation detection in French biomedical corpora
Authors: Cl{\'e}ment Dalloux, Vincent Claveau, Natalia Grabar
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Negation, Negation Detection, Word Embeddings

In this work, we propose to address the detection of negation and speculation, and of their scope, in French biomedical documents. It has been indeed observed that they play an important role and provide crucial clues for other NLP applications. Our methods are based on CRFs and BiLSTM. We reach up to 97.21 {\%} and 91.30 {\%} F-measure for the detection of negation and speculation cues, respectively, using CRFs. For the computing of scope, we reach up to 90.81 {\%} and 86.73 {\%} F-measure on negation and speculation, respectively, using BiLSTM-CRF fed with word embeddings.
Porting Multilingual Morphological Resources to OntoLex-Lemon
Authors: Thierry Declerck, Stefania Racioppa
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9

We describe work consisting in porting various morphological resources to the OntoLex-Lemon model. A main objective of this work is to offer a uniform representation of different morphological data sets in order to be able to compare and interlink multilingual resources and to cross-check and interlink or merge the content of morphological resources of one and the same language. The results of our work will be published on the Linguistic Linked Open Data cloud.
Dependency-Based Self-Attention for Transformer NMT
Authors: Hiroyuki Deguchi, Akihiro Tamura, Takashi Ninomiya
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Decoder, Machine Translation, NMT, Semantic Role Labeling, Translation

In this paper, we propose a new Transformer neural machine translation (NMT) model that incorporates dependency relations into self-attention on both source and target sides, dependency-based self-attention. The dependency-based self-attention is trained to attend to the modifiee for each token under constraints based on the dependency relations, inspired by Linguistically-Informed Self-Attention (LISA). While LISA is originally proposed for Transformer encoder for semantic role labeling, this paper extends LISA to Transformer NMT by masking future information on words in the decoder-side dependency-based self-attention. Additionally, our dependency-based self-attention operates at sub-word units created by byte pair encoding. The experiments show that our model improves 1.0 BLEU points over the baseline model on the WAT{'}18 Asian Scientific Paper Excerpt Corpus Japanese-to-English translation task.
De-Identification of Emails: Pseudonymizing Privacy-Sensitive Data in a German Email Corpus
Authors: Elisabeth Eder, Ulrike Krieg-Holz, Udo Hahn
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: De-identification

We deal with the pseudonymization of those stretches of text in emails that might allow to identify real individual persons. This task is decomposed into two steps. First, named entities carrying privacy-sensitive information (e.g., names of persons, locations, phone numbers or dates) are identified, and, second, these privacy-bearing entities are replaced by synthetically generated surrogates (e.g., a person originally named {`}John Doe{'} is renamed as {`}Bill Powers{'}). We describe a system architecture for surrogate generation and evaluate our approach on CodeAlltag, a German email corpus.
Lexical Quantile-Based Text Complexity Measure
Authors: Maksim Eremeev, Konstantin Vorontsov
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Articles, Experimental Design

This paper introduces a new approach to estimating the text document complexity. Common readability indices are based on average length of sentences and words. In contrast to these methods, we propose to count the number of rare words occurring abnormally often in the document. We use the reference corpus of texts and the quantile approach in order to determine what words are rare, and what frequencies are abnormal. We construct a general text complexity model, which can be adjusted for the specific task, and introduce two special models. The experimental design is based on a set of thematically similar pairs of Wikipedia articles, labeled using crowdsourcing. The experiments demonstrate the competitiveness of the proposed approach.
Demo Application for LETO: Learning Engine Through Ontologies
Authors: Suilan Estevez-Velarde, Andr{\'e}s Montoyo, Yudivian Almeida-Cruz, Yoan Guti{\'e}rrez, Alej Piad-Morffis, ro, Rafael Mu{\~n}oz
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: valid

The massive amount of multi-formatted information available on the Web necessitates the design of software systems that leverage this information to obtain knowledge that is valid and useful. The main challenge is to discover relevant information and continuously update, enrich and integrate knowledge from various sources of structured and unstructured data. This paper presents the Learning Engine Through Ontologies(LETO) framework, an architecture for the continuous and incremental discovery of knowledge from multiple sources of unstructured and structured data. We justify the main design decision behind LETO{'}s architecture and evaluate the framework{'}s feasibility using the Internet Movie Data Base(IMDB) and Twitter as a practical application.
Sentence Simplification for Semantic Role Labelling and Information Extraction
Authors: Richard Evans, Constantin Orasan
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Sentence

In this paper, we report on the extrinsic evaluation of an automatic sentence simplification method with respect to two NLP tasks: semantic role labelling (SRL) and information extraction (IE). The paper begins with our observation of challenges in the intrinsic evaluation of sentence simplification systems, which motivates the use of extrinsic evaluation of these systems with respect to other NLP tasks. We describe the two NLP systems and the test data used in the extrinsic evaluation, and present arguments and evidence motivating the integration of a sentence simplification step as a means of improving the accuracy of these systems. Our evaluation reveals that their performance is improved by the simplification step: the SRL system is better able to assign semantic roles to the majority of the arguments of verbs and the IE system is better able to identify fillers for all IE template slots.
OlloBot - Towards A Text-Based Arabic Health Conversational Agent: Evaluation and Results
Authors: Ahmed Fadhil, Ahmed Abura{'}ed
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9

We introduce OlloBot, an Arabic conversational agent that assists physicians and supports patients with the care process. It doesn{'}t replace the physicians, instead provides health tracking and support and assists physicians with the care delivery through a conversation medium. The current model comprises healthy diet, physical activity, mental health, in addition to food logging. Not only OlloBot tracks user daily food, it also offers useful tips for healthier living. We will discuss the design, development and testing of OlloBot, and highlight the findings and limitations arose from the testing.
Developing the Old Tibetan Treebank
Authors: Christian Faggionato, Marieke Meelen
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: POS

This paper presents a full procedure for the development of a segmented, POS-tagged and chunkparsed corpus of Old Tibetan. As an extremely low-resource language, Old Tibetan poses non-trivial problems in every step towards the development of a searchable treebank. We demonstrate, however, that a carefully developed, semisupervised method of optimising and extending existing tools for Classical Tibetan, as well as creating specific ones for Old Tibetan can address these issues. We thus also present the first very Tibetan Treebank in a variety of formats to facilitate research in the fields of NLP, historical linguistics and Tibetan Studies.
Summarizing Legal Rulings: Comparative Experiments
Authors: Diego Feijo, Viviane Moreira
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Articles, Text Summarization

In the context of text summarization, texts in the legal domain have peculiarities related to their length and to their specialized vocabulary. Recent neural network-based approaches can achieve high-quality scores for text summarization. However, these approaches have been used mostly for generating very short abstracts for news articles. Thus, their applicability to the legal domain remains an open issue. In this work, we experimented with ten extractive and four abstractive models in a real dataset of legal rulings. These models were compared with an extractive baseline based on heuristics to select the most relevant parts of the text. Our results show that abstractive approaches significantly outperform extractive methods in terms of ROUGE scores.
Entropy as a Proxy for Gap Complexity in Open Cloze Tests
Authors: Mariano Felice, Paula Buttery
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9

This paper presents a pilot study of entropy as a measure of gap complexity in open cloze tests aimed at learners of English. Entropy is used to quantify the information content in each gap, which can be used to estimate complexity. Our study shows that average gap entropy correlates positively with proficiency levels while individual gap entropy can capture contextual complexity. To the best of our knowledge, this is the first unsupervised information-theoretical approach to evaluating the quality of cloze tests.
Song Lyrics Summarization Inspired by Audio Thumbnailing
Authors: Michael Fell, Elena Cabrio, G, Fabien on, Alain Giboin
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Text Summarization

Given the peculiar structure of songs, applying generic text summarization methods to lyrics can lead to the generation of highly redundant and incoherent text. In this paper, we propose to enhance state-of-the-art text summarization approaches with a method inspired by audio thumbnailing. Instead of searching for the thumbnail clues in the audio of the song, we identify equivalent clues in the lyrics. We then show how these summaries that take into account the audio nature of the lyrics outperform the generic methods according to both an automatic evaluation and human judgments.
Comparing Automated Methods to Detect Explicit Content in Song Lyrics
Authors: Michael Fell, Elena Cabrio, Michele Corazza, G, Fabien on
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: TAG

The Parental Advisory Label (PAL) is a warning label that is placed on audio recordings in recognition of profanity or inappropriate references, with the intention of alerting parents of material potentially unsuitable for children. Since 2015, digital providers {--} such as iTunes, Spotify, Amazon Music and Deezer {--} also follow PAL guidelines and tag such tracks as {``}explicit{''}. Nowadays, such labelling is carried out mainly manually on voluntary basis, with the drawbacks of being time consuming and therefore costly, error prone and partly a subjective task. In this paper, we compare automated methods ranging from dictionary-based lookup to state-of-the-art deep neural networks to automatically detect explicit contents in English lyrics. We show that more complex models perform only slightly better on this task, and relying on a qualitative analysis of the data, we discuss the inherent hardness and subjectivity of the task.
Linguistic classification: dealing jointly with irrelevance and inconsistency
Authors: Laura Franzoi, Andrea Sgarro, Anca Dinu, Liviu P. Dinu
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Classification, General Classification

In this paper, we present new methods for language classification which put to good use both syntax and fuzzy tools, and are capable of dealing with irrelevant linguistic features (i.e. features which should not contribute to the classification) and even inconsistent features (which do not make sense for specific languages). We introduce a metric distance, based on the generalized Steinhaus transform, which allows one to deal jointly with irrelevance and inconsistency. To evaluate our methods, we test them on a syntactic data set, due to the linguist G. Longobardi and his school. We obtain phylogenetic trees which sometimes outperform the ones obtained by Atkinson and Gray.
Corpus Lexicography in a Wider Context
Authors: Chen Gafni
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Lexical Analysis

This paper describes a set of tools that offers comprehensive solutions for corpus lexicography. The tools perform a range of tasks, including construction of corpus lexicon, integrating information from external dictionaries, internal analysis of the lexicon, and lexical analysis of the corpus. The set of tools is particularly useful for creating dictionaries for under-resourced languages. The tools are integrated in a general-purpose software that includes additional tools for various research tasks, such as linguistic development analysis. Equipped with a user-friendly interface, the described system can be easily incorporated in research in a variety of fields.
A Universal System for Automatic Text-to-Phonetics Conversion
Authors: Chen Gafni
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9

This paper describes an automatic text-to-phonetics conversion system. The system was constructed to primarily serve as a research tool. It is implemented in a general-purpose linguistic software, which allows it to be incorporated in a multifaceted linguistic research in essentially any language. The system currently relies on two mechanisms to generate phonetic transcriptions from texts: (i) importing ready-made phonetic word forms from external dictionaries, and (ii) automatic generation of phonetic word forms based on a set of deterministic linguistic rules. The current paper describes the proposed system and its potential application to linguistic research.
Two Discourse Tree - Based Approaches to Indexing Answers
Authors: Boris Galitsky, Dmitry Ilvovsky
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Anatomy, Vocal Bursts Valence Prediction

We explore anatomy of answers with respect to which text fragments from an answer are worth matching with a question and which should not be matched. We apply the Rhetorical Structure Theory to build a discourse tree of an answer and select elementary discourse units that are suitable for indexing. Manual rules for selection of these discourse units as well as automated classification based on web search engine mining are evaluated con-cerning improving search accuracy. We form two sets of question-answer pairs for FAQ and community QA search domains and use them for evaluation of the proposed indexing methodology, which delivers up to 16 percent improvement in search recall.
Discourse-Based Approach to Involvement of Background Knowledge for Question Answering
Authors: Boris Galitsky, Dmitry Ilvovsky
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Question Answering, Sentence

We introduce a concept of a virtual discourse tree to improve question answering (Q/A) recall for complex, multi-sentence questions. Augmenting the discourse tree of an answer with tree fragments obtained from text corpora playing the role of ontology, we obtain on the fly a canonical discourse representation of this answer that is independent of the thought structure of a given author. This mechanism is critical for finding an answer that is not only relevant in terms of questions entities but also in terms of inter-relations between these entities in an answer and its style. We evaluate the Q/A system enabled with virtual discourse trees and observe a substantial increase of performance answering complex questions such as Yahoo! Answers and www.2carpros.com.
On a Chatbot Providing Virtual Dialogues
Authors: Boris Galitsky, Dmitry Ilvovsky, Elizaveta Goncharova
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Chatbot, Form

We present a chatbot that delivers content in the form of virtual dialogues automatically produced from the plain texts that are extracted and selected from the documents. This virtual dialogue content is provided in the form of answers derived from the found and selected documents split into fragments, and questions that are automatically generated for these answers based on the initial text.
Assessing socioeconomic status of Twitter users: A survey
Authors: Dhouha GHAZOUANI, Luigi LANCIERI, Habib Ounelli, Chaker JEBARI
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Sociology, Survey

Every day, the emotion and opinion of different people across the world are reflected in the form of short messages using microblogging platforms. Despite the existence of enormous potential introduced by this data source, the Twitter community is still ambiguous and is not fully explored yet. While there are a huge number of studies examining the possibilities of inferring gender and age, there exist hardly researches on socioeconomic status (SES) inference of Twitter users. As socioeconomic status is essential to treating diverse questions linked to human behavior in several fields (sociology, demography, public health, etc.), we conducted a comprehensive literature review of SES studies, inference methods, and metrics. With reference to the research on literature{'}s results, we came to outline the most critical challenges for researchers. To the best of our knowledge, this paper is the first review that introduces the different aspects of SES inference. Indeed, this article provides the benefits for practitioners who aim to process and explore Twitter SES inference.
Divide and Extract -- Disentangling Clause Splitting and Proposition Extraction
Authors: Darina Gold, Torsten Zesch
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9

Proposition extraction from sentences is an important task for information extraction systems Evaluation of such systems usually conflates two aspects: splitting complex sentences into clauses and the extraction of propositions. It is thus difficult to independently determine the quality of the proposition extraction step. We create a manually annotated proposition dataset from sentences taken from restaurant reviews that distinguishes between clauses that need to be split and those that do not. The resulting proposition evaluation dataset allows us to independently compare the performance of proposition extraction systems on simple and complex clauses. Although performance drastically drops on more complex sentences, we show that the same systems perform best on both simple and complex clauses. Furthermore, we show that specific kinds of subordinate clauses pose difficulties to most systems.
Sparse Coding in Authorship Attribution for Polish Tweets
Authors: Piotr Grzybowski, Ewa Juralewicz, Maciej Piasecki
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Authorship Attribution

The study explores application of a simple Convolutional Neural Network for the problem of authorship attribution of tweets written in Polish. In our solution we use two-step compression of tweets using Byte Pair Encoding algorithm and vectorisation as an input to the distributional model generated for the large corpus of Polish tweets by word2vec algorithm. Our method achieves results comparable to the state-of-the-art approaches for the similar task on English tweets and expresses a very good performance in the classification of Polish tweets. We tested the proposed method in relation to the number of authors and tweets per author. We also juxtaposed results for authors with different topic backgrounds against each other.
Automatic Question Answering for Medical MCQs: Can It go Further than Information Retrieval?
Authors: Le An Ha, Victoria Yaneva
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Information Retrieval, Multiple-choice, Question Answering, Retrieval

We present a novel approach to automatic question answering that does not depend on the performance of an information retrieval (IR) system and does not require that the training data come from the same source as the questions. We evaluate the system performance on a challenging set of university-level medical science multiple-choice questions. Best performance is achieved when combining a neural approach with an IR approach, both of which work independently. Unlike previous approaches, the system achieves statistically significant improvement over the random guess baseline even for questions that are labeled as challenging based on the performance of baseline solvers.
Investigating Terminology Translation in Statistical and Neural Machine Translation: A Case Study on English-to-Hindi and Hindi-to-English
Authors: Rejwanul Haque, Md Hasanuzzaman, Andy Way
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Machine Translation, NMT, Translation

Terminology translation plays a critical role in domain-specific machine translation (MT). In this paper, we conduct a comparative qualitative evaluation on terminology translation in phrase-based statistical MT (PB-SMT) and neural MT (NMT) in two translation directions: English-to-Hindi and Hindi-to-English. For this, we select a test set from a legal domain corpus and create a gold standard for evaluating terminology translation in MT. We also propose an error typology taking the terminology translation errors into consideration. We evaluate the MT systems{'} performance on terminology translation, and demonstrate our findings, unraveling strengths, weaknesses, and similarities of PB-SMT and NMT in the area of term translation.
Tweaks and Tricks for Word Embedding Disruptions
Authors: Amir Hazem, Hern, Nicolas ez
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Descriptive, Sentence, Sentence Embedding, Sentence-Embedding, Word Embeddings

Word embeddings are established as very effective models used in several NLP applications. If they differ in their architecture and training process, they often exhibit similar properties and remain vector space models with continuously-valued dimensions describing the observed data. The complexity resides in the developed strategies for learning the values within each dimensional space. In this paper, we introduce the concept of disruption which we define as a side effect of the training process of embedding models. Disruptions are viewed as a set of embedding values that are more likely to be noise than effective descriptive features. We show that dealing with disruption phenomenon is of a great benefit to bottom-up sentence embedding representation. By contrasting several in-domain and pre-trained embedding models, we propose two simple but very effective tweaking techniques that yield strong empirical improvements on textual similarity task.
Meta-Embedding Sentence Representation for Textual Similarity
Authors: Amir Hazem, Hern, Nicolas ez
Date: 2019-09-01 00:00:00
Proceeding: RANLP 2019 9
Tasks: Question Similarity, Sentence, Sentence Embedding, Sentence-Embedding, Sentence Embeddings

Word embedding models are now widely used in most NLP applications. Despite their effectiveness, there is no clear evidence about the choice of the most appropriate model. It often depends on the nature of the task and on the quality and size of the used data sets. This remains true for bottom-up sentence embedding models. However, no straightforward investigation has been conducted so far. In this paper, we propose a systematic study of the impact of the main word embedding models on sentence representation. By contrasting in-domain and pre-trained embedding models, we show under which conditions they can be jointly used for bottom-up sentence embeddings. Finally, we propose the first bottom-up meta-embedding representation at the sentence level for textual similarity. Significant improvements are observed in several tasks including question-to-question similarity, paraphrasing and next utterance ranking.