pwc-archive/papers-with-abstracts huggingface dataset:
Coarse-To-Fine Parsing for Expressive Grammar Formalisms
Authors: Christoph Teichmann, Alex Koller, er, Jonas Groschwitz
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9

We generalize coarse-to-fine parsing to grammar formalisms that are more expressive than PCFGs and/or describe languages of trees or graphs. We evaluate our algorithm on PCFG, PTAG, and graph parsing. While we achieve the expected performance gains on PCFGs, coarse-to-fine does not help for PTAG and can even slow down parsing for graphs. We discuss the implications of this finding.
Evaluating LSTM models for grammatical function labelling
Authors: Bich-Ngoc Do, Ines Rehbein
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Dependency Parsing

To improve grammatical function labelling for German, we augment the labelling component of a neural dependency parser with a decision history. We present different ways to encode the history, using different LSTM architectures, and show that our models yield significant improvements, resulting in a LAS for German that is close to the best result from the SPMRL 2014 shared task (without the reranker).
Deception Detection for the Russian Language: Lexical and Syntactic Parameters
Authors: Dina Pisarevskaya, Tatiana Litvinova, Olga Litvinova
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Binary text classification, Deception Detection, Information Retrieval, POS, text-classification, Text Classification

The field of automated deception detection in written texts is methodologically challenging. Different linguistic levels (lexics, syntax and semantics) are basically used for different types of English texts to reveal if they are truthful or deceptive. Such parameters as POS tags and POS tags n-grams, punctuation marks, sentiment polarity of words, psycholinguistic features, fragments of syntaÑ�tic structures are taken into consideration. The importance of different types of parameters was not compared for the Russian language before and should be investigated before moving to complex models and higher levels of linguistic processing. On the example of the Russian Deception Bank Corpus we estimate the impact of three groups of features (POS features including bigrams, sentiment and psycholinguistic features, syntax and readability features) on the successful deception detection and find out that POS features can be used for binary text classification, but the results should be double-checked and, if possible, improved.
oIQa: An Opinion Influence Oriented Question Answering Framework with Applications to Marketing Domain
Authors: Dumitru-Clementin Cercel, Cristian Onose, Stefan Trausan-Matu, Florin Pop
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Chatbot, Information Retrieval, Marketing, Opinion Mining, Question Answering, Sentiment Analysis

Understanding questions and answers in QA system is a major challenge in the domain of natural language processing. In this paper, we present a question answering system that influences the human opinions in a conversation. The opinion words are quantified by using a lexicon-based method. We apply Latent Semantic Analysis and the cosine similarity measure between candidate answers and each question to infer the answer of the chatbot.
A Game with a Purpose for Automatic Detection of Children's Speech Disabilities using Limited Speech Resources
Authors: Reem Salem, Mohamed Elmahdy, Slim Abdennadher, Injy Hamed
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Information Retrieval

Speech therapists and researchers are becoming more concerned with the use of computer-based systems in the therapy of speech disorders. In this paper, we propose a computer-based game with a purpose (GWAP) for speech therapy of Egyptian speaking children suffering from Dyslalia. Our aim is to detect if a certain phoneme is pronounced correctly. An Egyptian Arabic speech corpus has been collected. A baseline acoustic model was trained using the Egyptian corpus. In order to benefit from existing large amounts of Modern Standard Arabic (MSA) resources, MSA acoustic models were adapted with the collected Egyptian corpus. An independent testing set that covers common speech disorders has been collected for Egyptian speakers. Results show that adapted acoustic models give better recognition accuracy which could be relied on in the game and that children show more interest in playing the game than in visiting the therapist. A noticeable progress in children Dyslalia appeared with the proposed system.
Connecting people digitally - a semantic web based approach to linking heterogeneous data sets
Authors: Katalin Lejtovicz, Amelie Dorn
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Entity Linking, Word Sense Disambiguation

In this paper we present a semantic enrichment approach for linking two distinct data sets: the {\"O}BL (Austrian Biographical Dictionary) and the DB{\"O} (Database of Bavarian Dialects in Austria). Although the data sets are different in their content and in the structuring of data, they contain similar common {``}entities{''} such as names of persons. Here we describe the semantic enrichment process of how these data sets can be inter-linked through URIs (Uniform Resource Identifiers) taking person names as a concrete example. Moreover, we also point to societal benefits of applying such semantic enrichment methods in order to open and connect our resources to various services.
A Multiform Balanced Dependency Treebank for Romanian
Authors: Mihaela Colhon, C{\u{a}}t{\u{a}}lina M{\u{a}}r{\u{a}}nduc, C{\u{a}}t{\u{a}}lin Mititelu
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Machine Translation

The UAIC-RoDia-DepTb is a balanced treebank, containing texts in non-standard language: 2,575 chats sentences, old Romanian texts (a Gospel printed in 1648, a codex of laws printed in 1818, a novel written in 1910), regional popular poetry, legal texts, Romanian and foreign fiction, quotations. The proportions are comparable; each of these types of texts is represented by subsets of at least 1,000 phrases, so that the parser can be trained on their peculiarities. The annotation of the treebank started in 2007, and it has classical tags, such as those in school grammar, with the intention of using the resource for didactic purposes. The classification of circumstantial modifiers is rich in semantic information. We present in this paper the development in progress of this resource which has been automatically annotated and entirely manually corrected. We try to add new texts, and to make it available in more formats, by keeping all the morphological and syntactic information annotated, and adding logical-semantic information. We will describe here two conversions, from the classic syntactic format into Universal Dependencies format and into a logical-semantic layer, which will be shortly presented.
GRaSP: Grounded Representation and Source Perspective
Authors: Antske Fokkens, Piek Vossen, Marco Rospocher, Rinke Hoekstra, Willem Robert van Hage
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9

When people or organizations provide information, they make choices regarding what information they include and how they present it. The combination of these two aspects (the content and stance provided by the source) represents a perspective. Investigating differences in perspective can provide various useful insights in the reliability of information, the way perspectives change over time, shared beliefs among groups of a similar social or political background and contrasts between other groups, etc. This paper introduces GRaSP, a generic framework for modeling perspectives and their sources.
Educational Content Generation for Business and Administration FL Courses with the NBU PLT Platform
Authors: Maria Stambolieva
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Language Acquisition, Management

The paper presents part of an ongoing project of the Laboratory for Language Technologies of New Bulgarian University {--} {``}An e-Platform for Language Teaching (PLT){''} {--} the development of corpus-based teaching content for Business English courses. The presentation offers information on: 1/ corpus creation and corpus management with PLT; 2/ PLT corpus annotation; 3/ language task generation and the Language Task Bank (LTB); 4/ content transfer to the NBU Moodle platform, test generation and feedback on student performance.
Machine Learning Models of Universal Grammar Parameter Dependencies
Authors: Dimitar Kazakov, Guido Cordoni, Andrea Ceolin, Monica-Alex Irimia, rina, Shin-Sook Kim, Dimitris Michelioudakis, Nina Radkevich, Cristina Guardiano, Giuseppe Longobardi
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: BIG-bench Machine Learning, Descriptive

The use of parameters in the description of natural language syntax has to balance between the need to discriminate among (sometimes subtly different) languages, which can be seen as a cross-linguistic version of Chomsky{'}s (1964) descriptive adequacy, and the complexity of the acquisition task that a large number of parameters would imply, which is a problem for explanatory adequacy. Here we present a novel approach in which a machine learning algorithm is used to find dependencies in a table of parameters. The result is a dependency graph in which some of the parameters can be fully predicted from others. These empirical findings can be then subjected to linguistic analysis, which may either refute them by providing typological counter-examples of languages not included in the original dataset, dismiss them on theoretical grounds, or uphold them as tentative empirical laws worth of further study.
Enhancing Machine Translation of Academic Course Catalogues with Terminological Resources
Authors: R Scansani, y, Silvia Bernardini, Adriano Ferraresi, Federico Gaspari, Marcello Soffritti
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Machine Translation, Translation

This paper describes an approach to translating course unit descriptions from Italian and German into English, using a phrase-based machine translation (MT) system. The genre is very prominent among those requiring translation by universities in European countries in which English is a non-native language. For each language combination, an in-domain bilingual corpus including course unit and degree program descriptions is used to train an MT engine, whose output is then compared to a baseline engine trained on the Europarl corpus. In a subsequent experiment, a bilingual terminology database is added to the training sets in both engines and its impact on the output quality is evaluated based on BLEU and post-editing score. Results suggest that the use of domain-specific corpora boosts the engines quality for both language combinations, especially for German-English, whereas adding terminological resources does not seem to bring notable benefits.
Experiments in Non-Coherent Post-editing
Authors: Cristina Toledo B{\'a}ez, Moritz Schaeffer, Michael Carl
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Active Learning, Machine Translation, Translation

Market pressure on translation productivity joined with technological innovation is likely to fragment and decontextualise translation jobs even more than is cur-rently the case. Many different translators increasingly work on one document at different places, collaboratively working in the cloud. This paper investigates the effect of decontextualised source texts on behaviour by comparing post-editing of sequentially ordered sentences with shuffled sentences from two different texts. The findings suggest that there is little or no effect of the decontextualised source texts on behaviour.
Comparing Machine Translation and Human Translation: A Case Study
Authors: Lars Ahrenberg
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Descriptive, Machine Translation, Translation

As machine translation technology improves comparisons to human performance are often made in quite general and exaggerated terms. Thus, it is important to be able to account for differences accurately. This paper reports a simple, descriptive scheme for comparing translations and applies it to two translations of a British opinion article published in March, 2017. One is a human translation (HT) into Swedish, and the other a machine translation (MT). While the comparison is limited to one text, the results are indicative of current limitations in MT.
TransBank: Metadata as the Missing Link between NLP and Traditional Translation Studies
Authors: Michael Ustaszewski, Andy Stauder
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Machine Translation, Sentence, Translation

Despite the growing importance of data in translation, there is no data repository that equally meets the requirements of translation industry and academia alike. Therefore, we plan to develop a freely available, multilingual and expandable bank of translations and their source texts aligned at the sentence level. Special emphasis will be placed on the labelling of metadata that precisely describe the relations between translated texts and their originals. This metadata-centric approach gives users the opportunity to compile and download custom corpora on demand. Such a general-purpose data repository may help to bridge the gap between translation theory and the language industry, including translation technology providers and NLP.
Interpreting Strategies Annotation in the WAW Corpus
Authors: Irina Temnikova, Ahmed Abdelali, Samy Hedaya, Stephan Vogel, Aishah Al Daher
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Machine Translation, Speech-to-Text, Speech-to-Text Translation, Translation

With the aim to teach our automatic speech-to-text translation system human interpreting strategies, our first step is to identify which interpreting strategies are most often used in the language pair of our interest (English-Arabic). In this article we run an automatic analysis of a corpus of parallel speeches and their human interpretations, and provide the results of manually annotating the human interpreting strategies in a sample of the corpus. We give a glimpse of the corpus, whose value surpasses the fact that it contains a high number of scientific speeches with their interpretations from English into Arabic, as it also provides rich information about the interpreters. We also discuss the difficulties, which we encountered on our way, as well as our solutions to them: our methodology for manual re-segmentation and alignment of parallel segments, the choice of annotation tool, and the annotation procedure. Our annotation findings explain the previously extracted specific statistical features of the interpreted corpus (compared with a translation one) as well as the quality of interpretation provided by different interpreters.
Translation Memory Systems Have a Long Way to Go
Authors: Andrea Silvestre Baquero, Ruslan Mitkov
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Machine Translation, Sentence, Translation

The TM memory systems changed the work of translators and now the translators not benefiting from these tools are a tiny minority. These tools operate on fuzzy (surface) matching mostly and cannot benefit from already translated texts which are synonymous to (or paraphrased versions of) the text to be translated. The match score is mostly based on character-string similarity, calculated through Levenshtein distance. The TM tools have difficulties with detecting similarities even in sentences which represent a minor revision of sentences already available in the translation memory. This shortcoming of the current TM systems was the subject of the present study and was empirically proven in the experiments we conducted. To this end, we compiled a small translation memory (English-Spanish) and applied several lexical and syntactic transformation rules to the source sentences with both English and Spanish being the source language. The results of this study show that current TM systems have a long way to go and highlight the need for TM systems equipped with NLP capabilities which will offer the translator the advantage of he/she not having to translate a sentence again if an almost identical sentence has already been already translated.
Building Dialectal Arabic Corpora
Authors: Hani Elgabou, Dimitar Kazakov
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Dialect Identification, Information Retrieval, Language Identification, Machine Translation

The aim of this research is to identify local Arabic dialects in texts from social media (Twitter) and link them to specific geographic areas. Dialect identification is studied as a subset of the task of language identification. The proposed method is based on unsupervised learning using simultaneously lexical and geographic distance. While this study focusses on Libyan dialects, the approach is general, and could produce resources to support human translators and interpreters when dealing with vernaculars rather than standard Arabic.
Towards Producing Human-Validated Translation Resources for the Fula language through WordNet Linking
Authors: Khalil Mrini, Martin Benjamin
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Machine Translation, Translation

We propose methods to link automatically parsed linguistic data to the WordNet. We apply these methods on a trilingual dictionary in Fula, English and French. Dictionary entry parsing is used to collect the linguistic data. Then we connect it to the Open Multilingual WordNet (OMW) through two attempts, and use confidence scores to quantify accuracy. We obtained 11,000 entries in parsing and linked about 58{\%} to the OMW on the first attempt, and an additional 14{\%} in the second one. These links are due to be validated by Fula speakers before being added to the Kamusi Project{'}s database.
Document retrieval and question answering in medical documents. A large-scale corpus challenge.
Authors: Curea Eric
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Document Classification, General Classification, Information Retrieval, Question Answering, Retrieval

Whenever employed on large datasets, information retrieval works by isolating a subset of documents from the larger dataset and then proceeding with low-level processing of the text. This is usually carried out by means of adding index-terms to each document in the collection. In this paper we deal with automatic document classification and index-term detection applied on large-scale medical corpora. In our methodology we employ a linear classifier and we test our results on the BioASQ training corpora, which is a collection of 12 million MeSH-indexed medical abstracts. We cover both term-indexing, result retrieval and result ranking based on distributed word representations.
Adapting the TTL Romanian POS Tagger to the Biomedical Domain
Authors: Maria Mitrofan, Radu Ion
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Chunking, Domain Adaptation, Lemmatization, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Part-Of-Speech Tagging, POS, POS Tagging, Sentence, Word Sense Disambiguation

This paper presents the adaptation of the Hidden Markov Models-based TTL part-of-speech tagger to the biomedical domain. TTL is a text processing platform that performs sentence splitting, tokenization, POS tagging, chunking and Named Entity Recognition (NER) for a number of languages, including Romanian. The POS tagging accuracy obtained by the TTL POS tagger exceeds 97{\%} when TTL{'}s baseline model is updated with training information from a Romanian biomedical corpus. This corpus is developed in the context of the CoRoLa (a reference corpus for the contemporary Romanian language) project. Informative description and statistics of the Romanian biomedical corpus are also provided.
Discourse-Wide Extraction of Assay Frames from the Biological Literature
Authors: Dayne Freitag, Paul Kalmar, Eric Yeh
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Reading Comprehension, Relation Extraction, Sentence

We consider the problem of populating multi-part knowledge frames from textual information distributed over multiple sentences in a document. We present a corpus constructed by aligning papers from the cellular signaling literature to a collection of approximately 50,000 reference frames curated by hand as part of a decade-long project. We present and evaluate two approaches to the challenging problem of reconstructing these frames, which formalize biological assays described in the literature. One approach is based on classifying candidate records nominated by sentence-local entity co-occurrence. In the second approach, we introduce a novel virtual register machine traverses an article and generates frames, trained on our reference data. Our evaluations show that success in the task ultimately hinges on an integration of evidence spread across the discourse.
Classification based extraction of numeric values from clinical narratives
Authors: Maximilian Zubke
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Classification, General Classification, text-classification, Text Classification

The robust extraction of numeric values from clinical narratives is a well known problem in clinical data warehouses. In this paper we describe a dynamic and domain-independent approach to deliver numerical described values from clinical narratives. In contrast to alternative systems, we neither use manual defined rules nor any kind of ontologies or nomenclatures. Instead we propose a topic-based system, that tackles the information extraction as a text classification problem. Hence we use machine learning to identify the crucial context features of a topic-specific numeric value by a given set of example sentences, so that the manual effort reduces to the selection of appropriate sample sentences. We describe context features of a certain numeric value by term frequency vectors which are generated by multiple document segmentation procedures. Due to this simultaneous segmentation approaches, there can be more than one context vector for a numeric value. In those cases, we choose the context vector with the highest classification confidence and suppress the rest. To test our approach, we used a dataset from a german hospital containing 12,743 narrative reports about laboratory results of Leukemia patients. We used Support Vector Machines (SVM) for classification and achieved an average accuracy of 96{\%} on a manually labeled subset of 2073 documents, using 10-fold cross validation. This is a significant improvement over an alternative rule based system.
Understanding of unknown medical words
Authors: Natalia Grabar, Thierry Hamon
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9

We assume that unknown words with internal structure (affixed words or compounds) can provide speakers with linguistic cues as for their meaning, and thus help their decoding and understanding. To verify this hypothesis, we propose to work with a set of French medical words. These words are annotated by five annotators. Then, two kinds of analysis are performed: analysis of the evolution of understandable and non-understandable words (globally and according to some suffixes) and analysis of clusters created with unsupervised algorithms on basis of linguistic and extra-linguistic features of the studied words. Our results suggest that, according to linguistic sensitivity of annotators, technical words can be decoded and become understandable. As for the clusters, some of them distinguish between understandable and non-understandable words. Resources built in this work will be made freely available for the research purposes.