pwc-archive/papers-with-abstracts huggingface dataset:
Arabic Tweets Treebanking and Parsing: A Bootstrapping Approach
Authors: Fahad Albogamy, Allan Ramsay, Hanady Ahmed
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Domain Adaptation

In this paper, we propose using a {``}bootstrapping{''} method for constructing a dependency treebank of Arabic tweets. This method uses a rule-based parser to create a small treebank of one thousand Arabic tweets and a data-driven parser to create a larger treebank by using the small treebank as a seed training set. We are able to create a dependency treebank from unlabelled tweets without any manual intervention. Experiments results show that this method can improve the speed of training the parser and the accuracy of the resulting parsers.
Identifying Effective Translations for Cross-lingual Arabic-to-English User-generated Speech Search
Authors: Ahmad Khwileh, Haithem Afli, Gareth Jones, Andy Way
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Information Retrieval, Machine Translation, Retrieval, Translation

Cross Language Information Retrieval (CLIR) systems are a valuable tool to enable speakers of one language to search for content of interest expressed in a different language. A group for whom this is of particular interest is bilingual Arabic speakers who wish to search for English language content using information needs expressed in Arabic queries. A key challenge in CLIR is crossing the language barrier between the query and the documents. The most common approach to bridging this gap is automated query translation, which can be unreliable for vague or short queries. In this work, we examine the potential for improving CLIR effectiveness by predicting the translation effectiveness using Query Performance Prediction (QPP) techniques. We propose a novel QPP method to estimate the quality of translation for an Arabic-English Cross-lingual User-generated Speech Search (CLUGS) task. We present an empirical evaluation that demonstrates the quality of our method on alternative translation outputs extracted from an Arabic-to-English Machine Translation system developed for this task. Finally, we show how this framework can be integrated in CLUGS to find relevant translations for improved retrieval performance.
A Characterization Study of Arabic Twitter Data with a Benchmarking for State-of-the-Art Opinion Mining Models
Authors: Ramy Baly, Gilbert Badaro, Georges El-Khoury, Rawan Moukalled, Rita Aoun, Hazem Hajj, Wassim El-Hajj, Nizar Habash, Khaled Shaban
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Benchmarking, Feature Engineering, Opinion Mining, Sentiment Analysis

Opinion mining in Arabic is a challenging task given the rich morphology of the language. The task becomes more challenging when it is applied to Twitter data, which contains additional sources of noise, such as the use of unstandardized dialectal variations, the nonconformation to grammatical rules, the use of Arabizi and code-switching, and the use of non-text objects such as images and URLs to express opinion. In this paper, we perform an analytical study to observe how such linguistic phenomena vary across different Arab regions. This study of Arabic Twitter characterization aims at providing better understanding of Arabic Tweets, and fostering advanced research on the topic. Furthermore, we explore the performance of the two schools of machine learning on Arabic Twitter, namely the feature engineering approach and the deep learning approach. We consider models that have achieved state-of-the-art performance for opinion mining in English. Results highlight the advantages of using deep learning-based models, and confirm the importance of using morphological abstractions to address Arabic{'}s complex morphology.
Robust Dictionary Lookup in Multiple Noisy Orthographies
Authors: Lingliang Zhang, Nizar Habash, Godfried Toussaint
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Transliteration

We present the MultiScript Phonetic Search algorithm to address the problem of language learners looking up unfamiliar words that they heard. We apply it to Arabic dictionary lookup with noisy queries done using both the Arabic and Roman scripts. Our algorithm is based on a computational phonetic distance metric that can be optionally machine learned. To benchmark our performance, we created the ArabScribe dataset, containing 10,000 noisy transcriptions of random Arabic dictionary words. Our algorithm outperforms Google Translate{'}s {``}did you mean{''} feature, as well as the Yamli smart Arabic keyboard.
Arabic POS Tagging: Don't Abandon Feature Engineering Just Yet
Authors: Kareem Darwish, Hamdy Mubarak, Ahmed Abdelali, Mohamed Eldesouki
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Feature Engineering, Named Entity Recognition (NER), Part-Of-Speech Tagging, POS, POS Tagging, Word Embeddings

This paper focuses on comparing between using Support Vector Machine based ranking (SVM-Rank) and Bidirectional Long-Short-Term-Memory (bi-LSTM) neural-network based sequence labeling in building a state-of-the-art Arabic part-of-speech tagging system. Using SVM-Rank leads to state-of-the-art results, but with a fair amount of feature engineering. Using bi-LSTM, particularly when combined with word embeddings, may lead to competitive POS-tagging results by automatically deducing latent linguistic features. However, we show that augmenting bi-LSTM sequence labeling with some of the features that we used for the SVM-Rank based tagger yields to further improvements. We also show that gains that realized by using embeddings may not be additive with the gains achieved by the features. We are open-sourcing both the SVM-Rank and the bi-LSTM based systems for free.
Toward a Web-based Speech Corpus for Algerian Dialectal Arabic Varieties
Authors: Soumia Bougrine, Aicha Chorana, Abdallah Lakhdari, Hadda Cherroun
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Speech Recognition, Speech Synthesis

The success of machine learning for automatic speech processing has raised the need for large scale datasets. However, collecting such data is often a challenging task as it implies significant investment involving time and money cost. In this paper, we devise a recipe for building largescale Speech Corpora by harnessing Web resources namely YouTube, other Social Media, Online Radio and TV. We illustrate our methodology by building KALAM{'}DZ, An Arabic Spoken corpus dedicated to Algerian dialectal varieties. The preliminary version of our dataset covers all major Algerian dialects. In addition, we make sure that this material takes into account numerous aspects that foster its richness. In fact, we have targeted various speech topics. Some automatic and manual annotations are provided. They gather useful information related to the speakers and sub-dialect information at the utterance level. Our corpus encompasses the 8 major Algerian Arabic sub-dialects with 4881 speakers and more than 104.4 hours segmented in utterances of at least 6 s.
Not All Segments are Created Equal: Syntactically Motivated Sentiment Analysis in Lexical Space
Authors: Muhammad Abdul-Mageed
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: All, Classification, feature selection, General Classification, Sentiment Analysis, Sentiment Classification

Although there is by now a considerable amount of research on subjectivity and sentiment analysis on morphologically-rich languages, it is still unclear how lexical information can best be modeled in these languages. To bridge this gap, we build effective models exploiting exclusively gold- and machine-segmented lexical input and successfully employ syntactically motivated feature selection to improve classification. Our best models achieve significantly above the baselines, with 67.93{\%} and 69.37{\%} accuracies for subjectivity and sentiment classification respectively.
An enhanced automatic speech recognition system for Arabic
Authors: Mohamed Amine Menacer, Odile Mella, Dominique Fohr, Denis Jouvet, David Langlois, Kamel Smaili
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Arabic Speech Recognition, Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Automatic speech recognition for Arabic is a very challenging task. Despite all the classical techniques for Automatic Speech Recognition (ASR), which can be efficiently applied to Arabic speech recognition, it is essential to take into consideration the language specificities to improve the system performance. In this article, we focus on Modern Standard Arabic (MSA) speech recognition. We introduce the challenges related to Arabic language, namely the complex morphology nature of the language and the absence of the short vowels in written text, which leads to several potential vowelization for each graphemes, which is often conflicting. We develop an ASR system for MSA by using Kaldi toolkit. Several acoustic and language models are trained. We obtain a Word Error Rate (WER) of 14.42 for the baseline system and 12.2 relative improvement by rescoring the lattice and by rewriting the output with the right Z hamoza above or below Alif.
Universal Dependencies for Arabic
Authors: Dima Taji, Nizar Habash, Daniel Zeman
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Machine Translation, Question Answering

We describe the process of creating NUDAR, a Universal Dependency treebank for Arabic. We present the conversion from the Penn Arabic Treebank to the Universal Dependency syntactic representation through an intermediate dependency representation. We discuss the challenges faced in the conversion of the trees, the decisions we made to solve them, and the validation of our conversion. We also present initial parsing results on NUDAR.
A Layered Language Model based Hybrid Approach to Automatic Full Diacritization of Arabic
Authors: Mohamed Al-Badrashiny, Abdelati Hawwari, Mona Diab
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Arabic Text Diacritization, Form, Language Modeling, Language Modelling, Machine Translation, Morphological Analysis, Transliteration, Word Sense Disambiguation

In this paper we present a system for automatic Arabic text diacritization using three levels of analysis granularity in a layered back off manner. We build and exploit diacritized language models (LM) for each of three different levels of granularity: surface form, morphologically segmented into prefix/stem/suffix, and character level. For each of the passes, we use Viterbi search to pick the most probable diacritization per word in the input. We start with the surface form LM, followed by the morphological level, then finally we leverage the character level LM. Our system outperforms all of the published systems evaluated against the same training and test data. It achieves a 10.87{\%} WER for complete full diacritization including lexical and syntactic diacritization, and 3.0{\%} WER for lexical diacritization, ignoring syntactic diacritization.
Arabic Textual Entailment with Word Embeddings
Authors: Nada Almarwani, Mona Diab
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Machine Translation, Natural Language Inference, Question Answering, Retrieval, Word Embeddings

Determining the textual entailment between texts is important in many NLP tasks, such as summarization, question answering, and information extraction and retrieval. Various methods have been suggested based on external knowledge sources; however, such resources are not always available in all languages and their acquisition is typically laborious and very costly. Distributional word representations such as word embeddings learned over large corpora have been shown to capture syntactic and semantic word relationships. Such models have contributed to improving the performance of several NLP tasks. In this paper, we address the problem of textual entailment in Arabic. We employ both traditional features and distributional representations. Crucially, we do not depend on any external resources in the process. Our suggested approach yields state of the art performance on a standard data set, ArbTE, achieving an accuracy of 76.2 {\%} compared to state of the art of 69.3 {\%}.
Toward Pan-Slavic NLP: Some Experiments with Language Adaptation
Authors: Serge Sharoff
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Domain Adaptation, Language Modeling, Language Modelling, Machine Translation, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Opinion Mining, Part-Of-Speech Tagging, Translation

There is great variation in the amount of NLP resources available for Slavonic languages. For example, the Universal Dependency treebank (Nivre et al., 2016) has about 2 MW of training resources for Czech, more than 1 MW for Russian, while only 950 words for Ukrainian and nothing for Belorussian, Bosnian or Macedonian. Similarly, the Autodesk Machine Translation dataset only covers three Slavonic languages (Czech, Polish and Russian). In this talk I will discuss a general approach, which can be called Language Adaptation, similarly to Domain Adaptation. In this approach, a model for a particular language processing task is built by lexical transfer of cognate words and by learning a new feature representation for a lesser-resourced (recipient) language starting from a better-resourced (donor) language. More specifically, I will demonstrate how language adaptation works in such training scenarios as Translation Quality Estimation, Part-of-Speech tagging and Named Entity Recognition.
Clustering of Russian Adjective-Noun Constructions using Word Embeddings
Authors: Andrey Kutuzov, Elizaveta Kuzmenko, Lidia Pivovarova
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Clustering, Word Embeddings

This paper presents a method of automatic construction extraction from a large corpus of Russian. The term {`}construction{'} here means a multi-word expression in which a variable can be replaced with another word from the same semantic class, for example, {`}a glass of [water/juice/milk]{'}. We deal with constructions that consist of a noun and its adjective modifier. We propose a method of grouping such constructions into semantic classes via 2-step clustering of word vectors in distributional models. We compare it with other clustering techniques and evaluate it against A Russian-English Collocational Dictionary of the Human Body that contains manually annotated groups of constructions with nouns meaning human body parts. The best performing method is used to cluster all adjective-noun bigrams in the Russian National Corpus. Results of this procedure are publicly available and can be used for building Russian construction dictionary as well as to accelerate theoretical studies of constructions.
A Preliminary Study of Croatian Lexical Substitution
Authors: Domagoj Alagi{\'c}, Jan {\v{S}}najder
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Information Retrieval, Machine Translation, Word Sense Disambiguation

Lexical substitution is a task of determining a meaning-preserving replacement for a word in context. We report on a preliminary study of this task for the Croatian language on a small-scale lexical sample dataset, manually annotated using three different annotation schemes. We compare the annotations, analyze the inter-annotator agreement, and observe a number of interesting language specific details in the obtained lexical substitutes. Furthermore, we apply a recently-proposed, dependency-based lexical substitution model to our dataset. The model achieves a P@3 score of 0.35, which indicates the difficulty of the task.
Projecting Multiword Expression Resources on a Polish Treebank
Authors: Agata Savary, Jakub Waszczuk
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4

Multiword expressions (MWEs) are linguistic objects containing two or more words and showing idiosyncratic behavior at different levels. Treebanks with annotated MWEs enable studies of such properties, as well as training and evaluation of MWE-aware parsers. However, few treebanks contain full-fledged MWE annotations. We show how this gap can be bridged in Polish by projecting 3 MWE resources on a constituency treebank.
Lexicon Induction for Spoken Rusyn -- Challenges and Results
Authors: Achim Rabus, Yves Scherrer
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4

This paper reports on challenges and results in developing NLP resources for spoken Rusyn. Being a Slavic minority language, Rusyn does not have any resources to make use of. We propose to build a morphosyntactic dictionary for Rusyn, combining existing resources from the etymologically close Slavic languages Russian, Ukrainian, Slovak, and Polish. We adapt these resources to Rusyn by using vowel-sensitive Levenshtein distance, hand-written language-specific transformation rules, and combinations of the two. Compared to an exact match baseline, we increase the coverage of the resulting morphological dictionary by up to 77.4{\%} relative (42.9{\%} absolute), which results in a tagging recall increased by 11.6{\%} relative (9.1{\%} absolute). Our research confirms and expands the results of previous studies showing the efficiency of using NLP resources from neighboring languages for low-resourced languages.
The Universal Dependencies Treebank for Slovenian
Authors: Kaja Dobrovoljc, Toma{\v{z}} Erjavec, Simon Krek
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4

This paper introduces the Universal Dependencies Treebank for Slovenian. We overview the existing dependency treebanks for Slovenian and then detail the conversion of the ssj200k treebank to the framework of Universal Dependencies version 2. We explain the mapping of part-of-speech categories, morphosyntactic features, and the dependency relations, focusing on the more problematic language-specific issues. We conclude with a quantitative overview of the treebank and directions for further work.
Universal Dependencies for Serbian in Comparison with Croatian and Other Slavic Languages
Authors: Tanja Samard{\v{z}}i{\'c}, Mirjana Starovi{\'c}, {\v{Z}}eljko Agi{\'c}, Nikola Ljube{\v{s}}i{\'c}
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4

The paper documents the procedure of building a new Universal Dependencies (UDv2) treebank for Serbian starting from an existing Croatian UDv1 treebank and taking into account the other Slavic UD annotation guidelines. We describe the automatic and manual annotation procedures, discuss the annotation of Slavic-specific categories (case governing quantifiers, reflexive pronouns, question particles) and propose an approach to handling deverbal nouns in Slavic languages.
Spelling Correction for Morphologically Rich Language: a Case Study of Russian
Authors: Alexey Sorokin
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Language Modeling, Language Modelling, Lemmatization, Reranking, Sentence, Spelling Correction

We present an algorithm for automatic correction of spelling errors on the sentence level, which uses noisy channel model and feature-based reranking of hypotheses. Our system is designed for Russian and clearly outperforms the winner of SpellRuEval-2016 competition. We show that language model size has the greatest influence on spelling correction quality. We also experiment with different types of features and show that morphological and semantic information also improves the accuracy of spellchecking.
Debunking Sentiment Lexicons: A Case of Domain-Specific Sentiment Classification for Croatian
Authors: Paula Gombar, Zoran Medi{\'c}, Domagoj Alagi{\'c}, Jan {\v{S}}najder
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Classification, General Classification, Open-Ended Question Answering, Sentiment Analysis, Sentiment Classification, Stock Price Prediction

Sentiment lexicons are widely used as an intuitive and inexpensive way of tackling sentiment classification, often within a simple lexicon word-counting approach or as part of a supervised model. However, it is an open question whether these approaches can compete with supervised models that use only word-representation features. We address this question in the context of domain-specific sentiment classification for Croatian. We experiment with the graph-based acquisition of sentiment lexicons, analyze their quality, and investigate how effectively they can be used in sentiment classification. Our results indicate that, even with as few as 500 labeled instances, a supervised model substantially outperforms a word-counting model. We also observe that adding lexicon-based features does not significantly improve supervised sentiment classification.
Adapting a State-of-the-Art Tagger for South Slavic Languages to Non-Standard Text
Authors: Nikola Ljube{\v{s}}i{\'c}, Toma{\v{z}} Erjavec, Darja Fi{\v{s}}er
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Domain Adaptation, Lemmatization, Machine Translation, Part-Of-Speech Tagging

In this paper we present the adaptations of a state-of-the-art tagger for South Slavic languages to non-standard texts on the example of the Slovene language. We investigate the impact of introducing in-domain training data as well as additional supervision through external resources or tools like word clusters and word normalization. We remove more than half of the error of the standard tagger when applied to non-standard texts by training it on a combination of standard and non-standard training data, while enriching the data representation with external resources removes additional 11 percent of the error. The final configuration achieves tagging accuracy of 87.41{\%} on the full morphosyntactic description, which is, nevertheless, still quite far from the accuracy of 94.27{\%} achieved on standard text.
Comparison of Short-Text Sentiment Analysis Methods for Croatian
Authors: Leon Rotim, Jan {\v{S}}najder
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: General Classification, Sentiment Analysis, Sentiment Classification, Stock Price Prediction, Text Classification, Word Embeddings

We focus on the task of supervised sentiment classification of short and informal texts in Croatian, using two simple yet effective methods: word embeddings and string kernels. We investigate whether word embeddings offer any advantage over corpus- and preprocessing-free string kernels, and how these compare to bag-of-words baselines. We conduct a comparison on three different datasets, using different preprocessing methods and kernel functions. Results show that, on two out of three datasets, word embeddings outperform string kernels, which in turn outperform word and n-gram bag-of-words baselines.
The First Cross-Lingual Challenge on Recognition, Normalization, and Matching of Named Entities in Slavic Languages
Authors: Jakub Piskorski, Lidia Pivovarova, Jan {\v{S}}najder, Josef Steinberger, Roman Yangarber
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Entity Linking, Lemmatization, Multilingual Named Entity Recognition, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

This paper describes the outcomes of the first challenge on multilingual named entity recognition that aimed at recognizing mentions of named entities in web documents in Slavic languages, their normalization/lemmatization, and cross-language matching. It was organised in the context of the 6th Balto-Slavic Natural Language Processing Workshop, co-located with the EACL 2017 conference. Although eleven teams signed up for the evaluation, due to the complexity of the task(s) and short time available for elaborating a solution, only two teams submitted results on time. The reported evaluation figures reflect the relatively higher level of complexity of named entity-related tasks in the context of processing texts in Slavic languages. Since the duration of the challenge goes beyond the date of the publication of this paper and updated picture of the participating systems and their corresponding performance can be found on the web page of the challenge.