pwc-archive/papers-with-abstracts huggingface dataset:
A Japanese Word Dependency Corpus
Authors: Shinsuke Mori, Hideki Ogura, Tetsuro Sasada
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Articles, Dependency Parsing, Machine Translation, Sentence, Translation

In this paper, we present a corpus annotated with dependency relationships in Japanese. It contains about 30 thousand sentences in various domains. Six domains in Balanced Corpus of Contemporary Written Japanese have part-of-speech and pronunciation annotation as well. Dictionary example sentences have pronunciation annotation and cover basic vocabulary in Japanese with English sentence equivalent. Economic newspaper articles also have pronunciation annotation and the topics are similar to those of Penn Treebank. Invention disclosures do not have other annotation, but it has a clear application, machine translation. The unit of our corpus is word like other languages contrary to existing Japanese corpora whose unit is phrase called bunsetsu. Each sentence is manually segmented into words. We first present the specification of our corpus. Then we give a detailed explanation about our standard of word dependency. We also report some preliminary results of an MST-based dependency parser on our corpus.
Crowdsourcing and annotating NER for Twitter \#drift
Authors: Hege Fromreide, Dirk Hovy, Anders S{\o}gaard
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Named Entity Recognition (NER), NER

We present two new NER datasets for Twitter; a manually annotated set of 1,467 tweets (kappa=0.942) and a set of 2,975 expert-corrected, crowdsourced NER annotated tweets from the dataset described in Finin et al. (2010). In our experiments with these datasets, we observe two important points: (a) language drift on Twitter is significant, and while off-the-shelf systems have been reported to perform well on in-sample data, they often perform poorly on new samples of tweets, (b) state-of-the-art performance across various datasets can be obtained from crowdsourced annotations, making it more feasible to {``}catch up{''} with language drift.
Language Resources and Annotation Tools for Cross-Sentence Relation Extraction
Authors: Sebastian Krause, Hong Li, Feiyu Xu, Hans Uszkoreit, Robert Hummel, Luise Spielhagen
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Coreference Resolution, Dependency Parsing, Relation, Relation Extraction, Sentence

In this paper, we present a novel combination of two types of language resources dedicated to the detection of relevant relations (RE) such as events or facts across sentence boundaries. One of the two resources is the sar-graph, which aggregates for each target relation ten thousands of linguistic patterns of semantically associated relations that signal instances of the target relation (Uszkoreit and Xu, 2013). These have been learned from the Web by intra-sentence pattern extraction (Krause et al., 2012) and after semantic filtering and enriching have been automatically combined into a single graph. The other resource is cockrACE, a specially annotated corpus for the training and evaluation of cross-sentence RE. By employing our powerful annotation tool Recon, annotators mark selected entities and relations (including events), coreference relations among these entities and events, and also terms that are semantically related to the relevant relations and events. This paper describes how the two resources are created and how they complement each other.
Evaluating corpora documentation with regards to the Ethics and Big Data Charter
Authors: Alain Couillault, Kar{\"e}n Fort, Gilles Adda, Hugues de Mazancourt
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Ethics

The authors have written the Ethic and Big Data Charter in collaboration with various agencies, private bodies and associations. This Charter aims at describing any large or complex resources, and in particular language resources, from a legal and ethical viewpoint and ensuring the transparency of the process of creating and distributing such resources. We propose in this article an analysis of the documentation coverage of the most frequently mentioned language resources with regards to the Charter, in order to show the benefit it offers.
Bootstrapping Term Extractors for Multiple Languages
Authors: Ahmet Aker, Monica Paramita, Emma Barker, Robert Gaizauskas
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Information Retrieval, Management, POS, Retrieval, Term Extraction, Translation

Terminology extraction resources are needed for a wide range of human language technology applications, including knowledge management, information extraction, semantic search, cross-language information retrieval and automatic and assisted translation. We create a low cost method for creating terminology extraction resources for 21 non-English EU languages. Using parallel corpora and a projection method, we create a General POS Tagger for these languages. We also investigate the use of EuroVoc terms and Wikipedia corpus to automatically create term grammar for each language. Our results show that these automatically generated resources can assist term extraction process with similar performance to manually generated resources. All resources resulted in this experiment are freely available for download.
Evaluation of Automatic Hypernym Extraction from Technical Corpora in English and Dutch
Authors: Els Lefever, Marjan Van de Kauter, V{\'e}ronique Hoste
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Morphological Analysis

In this research, we evaluate different approaches for the automatic extraction of hypernym relations from English and Dutch technical text. The detected hypernym relations should enable us to semantically structure automatically obtained term lists from domain- and user-specific data. We investigated three different hypernymy extraction approaches for Dutch and English: a lexico-syntactic pattern-based approach, a distributional model and a morpho-syntactic method. To test the performance of the different approaches on domain-specific data, we collected and manually annotated English and Dutch data from two technical domains, viz. the dredging and financial domain. The experimental results show that especially the morpho-syntactic approach obtains good results for automatic hypernym extraction from technical and domain-specific texts.
Measuring Readability of Polish Texts: Baseline Experiments
Authors: Bartosz Broda, Bart{\l}omiej Nito{\'n}, W{\l}odzimierz Gruszczy{\'n}ski, Maciej Ogrodniczuk
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Language Modelling

Measuring readability of a text is the first sensible step to its simplification. In this paper we present an overview of the most common approaches to automatic measuring of readability. Of the described ones, we implemented and evaluated: Gunning FOG index, Flesch-based Pisarek method. We also present two other approaches. The first one is based on measuring distributional lexical similarity of a target text and comparing it to reference texts. In the second one, we propose a novel method for automation of Taylor test ― which, in its base form, requires performing a large amount of surveys. The automation of Taylor test is performed using a technique called statistical language modelling. We have developed a free on-line web-based system and constructed plugins for the most common text editors, namely Microsoft Word and OpenOffice.org. Inner workings of the system are described in detail. Finally, extensive evaluations are performed for Polish ― a Slavic, highly inflected language. We show that Pisareks method is highly correlated to Gunning FOG Index, even if different in form, and that both the similarity-based approach and automated Taylor test achieve high accuracy. Merits of using either of them are discussed.
Introducing a web application for labeling, visualizing speech and correcting derived speech signals
Authors: Raphael Winkelmann, Georg Raess
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Management

The advent of HTML5 has sparked a great increase in interest in the web as a development platform for a variety of different research applications. Due to its ability to easily deploy software to remote clients and the recent development of standardized browser APIs, we argue that the browser has become a good platform to develop a speech labeling tool for. This paper introduces a preliminary version of an open-source client-side web application for labeling speech data, visualizing speech and segmentation information and manually correcting derived speech signals such as formant trajectories. The user interface has been designed to be as user-friendly as possible in order to make the sometimes tedious task of transcribing as easy and efficient as possible. The future integration into the next iteration of the EMU speech database management system and its general architecture will also be outlined, as the work presented here is only one of several components contributing to the future system.
An Iterative Approach for Mining Parallel Sentences in a Comparable Corpus
Authors: Lise Rebout, Phillippe Langlais
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Information Retrieval, Machine Translation, Sentence, Translation

We describe an approach for mining parallel sentences in a collection of documents in two languages. While several approaches have been proposed for doing so, our proposal differs in several respects. First, we use a document level classifier in order to focus on potentially fruitful document pairs, an understudied approach. We show that mining less, but more parallel documents can lead to better gains in machine translation. Second, we compare different strategies for post-processing the output of a classifier trained to recognize parallel sentences. Last, we report a simple bootstrapping experiment which shows that promising sentence pairs extracted in a first stage can help to mine new sentence pairs in a second stage. We applied our approach on the English-French Wikipedia. Gains of a statistical machine translation (SMT) engine are analyzed along different test sets.
Development of a TV Broadcasts Speech Recognition System for Qatari Arabic
Authors: Mohamed Elmahdy, Mark Hasegawa-Johnson, Eiman Mustafawi
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Arabic Speech Recognition, Language Modeling, Language Modelling, speech-recognition, Speech Recognition, Transfer Learning

A major problem with dialectal Arabic speech recognition is due to the sparsity of speech resources. In this paper, a transfer learning framework is proposed to jointly use a large amount of Modern Standard Arabic (MSA) data and little amount of dialectal Arabic data to improve acoustic and language modeling. The Qatari Arabic (QA) dialect has been chosen as a typical example for an under-resourced Arabic dialect. A wide-band speech corpus has been collected and transcribed from several Qatari TV series and talk-show programs. A large vocabulary speech recognition baseline system was built using the QA corpus. The proposed MSA-based transfer learning technique was performed by applying orthographic normalization, phone mapping, data pooling, acoustic model adaptation, and system combination. The proposed approach can achieve more than 28{\%} relative reduction in WER.
Can Crowdsourcing be used for Effective Annotation of Arabic?
Authors: Wajdi Zaghouani, Kais Dukes
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Entity Resolution, Multiple-choice, Natural Language Inference, Part-Of-Speech Tagging, POS, POS Tagging, Word Sense Disambiguation

Crowdsourcing has been used recently as an alternative to traditional costly annotation by many natural language processing groups. In this paper, we explore the use of Amazon Mechanical Turk (AMT) in order to assess the feasibility of using AMT workers (also known as Turkers) to perform linguistic annotation of Arabic. We used a gold standard data set taken from the Quran corpus project annotated with part-of-speech and morphological information. An Arabic language qualification test was used to filter out potential non-qualified participants. Two experiments were performed, a part-of-speech tagging task in where the annotators were asked to choose a correct word-category from a multiple choice list and case ending identification task. The results obtained so far showed that annotating Arabic grammatical case is harder than POS tagging, and crowdsourcing for Arabic linguistic annotation requiring expert annotators could be not as effective as other crowdsourcing experiments requiring less expertise and qualifications.
Design and development of an RDB version of the Corpus of Spontaneous Japanese
Authors: Hanae Koiso, Yasuharu Den, Ken{'}ya Nishikawa, Kikuo Maekawa
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

In this paper, we describe the design and development of a new version of the Corpus of Spontaneous Japanese (CSJ), which is a large-scale spoken corpus released in 2004. CSJ contains various annotations that are represented in XML format (CSJ-XML). CSJ-XML, however, is very complicated and suffers from some problems. To overcome this problem, we have developed and released, in 2013, a relational database version of CSJ (CSJ-RDB). CSJ-RDB is based on an extension of the segment and link-based annotation scheme, which we adapted to handle multi-channel and multi-modal streams. Because this scheme adopts a stand-off framework, CSJ-RDB can represent three hierarchical structures at the same time: inter-pausal-unit-top, clause-top, and intonational-phrase-top. CSJ-RDB consists of five different types of tables: segment, unaligned-segment, link, relation, and meta-information tables. The database was automatically constructed from annotation files extracted from CSJ-XML by using general-purpose corpus construction tools. CSJ-RDB enables us to easily and efficiently conduct complex searches required for corpus-based studies of spoken language.
Automatic Long Audio Alignment and Confidence Scoring for Conversational Arabic Speech
Authors: Mohamed Elmahdy, Mark Hasegawa-Johnson, Eiman Mustafawi
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Language Modelling, speech-recognition, Speech Recognition

In this paper, a framework for long audio alignment for conversational Arabic speech is proposed. Accurate alignments help in many speech processing tasks such as audio indexing, speech recognizer acoustic model (AM) training, audio summarizing and retrieving, etc. We have collected more than 1,400 hours of conversational Arabic besides the corresponding human generated non-aligned transcriptions. Automatic audio segmentation is performed using a split and merge approach. A biased language model (LM) is trained using the corresponding text after a pre-processing stage. Because of the dominance of non-standard Arabic in conversational speech, a graphemic pronunciation model (PM) is utilized. The proposed alignment approach is performed in two passes. Firstly, a generic standard Arabic AM is used along with the biased LM and the graphemic PM in a fast speech recognition pass. In a second pass, a more restricted LM is generated for each audio segment, and unsupervised acoustic model adaptation is applied. The recognizer output is aligned with the processed transcriptions using Levenshtein algorithm. The proposed approach resulted in an initial alignment accuracy of 97.8-99.0{\%} depending on the amount of disfluencies. A confidence scoring metric is proposed to accept/reject aligner output. Using confidence scores, it was possible to reject the majority of mis-aligned segments resulting in alignment accuracy of 99.0-99.8{\%} depending on the speech domain and the amount of disfluencies.
Vocabulary-Based Language Similarity using Web Corpora
Authors: Dirk Goldhahn, Uwe Quasthoff
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Language Identification, Transliteration

This paper will focus on the evaluation of automatic methods for quantifying language similarity. This is achieved by ascribing language similarity to the similarity of text corpora. This corpus similarity will first be determined by the resemblance of the vocabulary of languages. Thereto words or parts of them such as letter n-grams are examined. Extensions like transliteration of the text data will ensure the independence of the methods from text characteristics such as the writing system used. Further analyzes will show to what extent knowledge about the distribution of words in parallel text can be used in the context of language similarity.
NewsReader: recording history from daily news streams
Authors: Piek Vossen, German Rigau, Luciano Serafini, Pim Stouten, Francis Irving, Willem van Hage
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

The European project NewsReader develops technology to process daily news streams in 4 languages, extracting what happened, when, where and who was involved. NewsReader does not just read a single newspaper but massive amounts of news coming from thousands of sources. It compares the results across sources to complement information and determine where they disagree. Furthermore, it merges news of today with previous news, creating a long-term history rather than separate events. The result is stored in a KnowledgeStore, that cumulates information over time, producing an extremely large knowledge graph that is visualized using new techniques to provide more comprehensive access. We present the first version of the system and the results of processing first batches of data.
A set of open source tools for Turkish natural language processing
Authors: {\c{C}}a{\u{g}}r{\i} {\c{C}}{\"o}ltekin
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Grapheme-to-Phoneme Conversion, Lemmatization, Morphological Disambiguation

This paper introduces a set of freely available, open-source tools for Turkish that are built around TRmorph, a morphological analyzer introduced earlier in Coltekin (2010). The article first provides an update on the analyzer, which includes a complete rewrite using a different finite-state description language and tool set as well as major tagset changes to comply better with the state-of-the-art computational processing of Turkish and the user requests received so far. Besides these major changes to the analyzer, this paper introduces tools for morphological segmentation, stemming and lemmatization, guessing unknown words, grapheme to phoneme conversion, hyphenation and a morphological disambiguation.
The Gulf of Guinea Creole Corpora
Authors: Tjerk Hagemeijer, Michel G{\'e}n{\'e}reux, Iris Hendrickx, Am{\'a}lia Mendes, Abigail Tiny, Arm Zamora, o
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: TAG

We present the process of building linguistic corpora of the Portuguese-related Gulf of Guinea creoles, a cluster of four historically related languages: Santome, Angolar, Principense and Fa dÂ’Amb{\^o}. We faced the typical difficulties of languages lacking an official status, such as lack of standard spelling, language variation, lack of basic language instruments, and small data sets, which comprise data from the late 19th century to the present. In order to tackle these problems, the compiled written and transcribed spoken data collected during field work trips were adapted to a normalized spelling that was applied to the four languages. For the corpus compilation we followed corpus linguistics standards. We recorded meta data for each file and added morphosyntactic information based on a part-of-speech tag set that was designed to deal with the specificities of these languages. The corpora of three of the four creoles are already available and searchable via an online web interface.
S-pot - a benchmark in spotting signs within continuous signing
Authors: Ville Viitaniemi, Tommi Jantunen, Leena Savolainen, Matti Karppa, Jorma Laaksonen
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

In this paper we present S-pot, a benchmark setting for evaluating the performance of automatic spotting of signs in continuous sign language videos. The benchmark includes 5539 video files of Finnish Sign Language, ground truth sign spotting results, a tool for assessing the spottings against the ground truth, and a repository for storing information on the results. In addition we will make our sign detection system and results made with it publicly available as a baseline for comparison and further developments.
Converting an HPSG-based Treebank into its Parallel Dependency-based Treebank
Authors: Masood Ghayoomi, Jonas Kuhn
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

A treebank is an important language resource for supervised statistical parsers. The parser induces the grammatical properties of a language from this language resource and uses the model to parse unseen data automatically. Since developing such a resource is very time-consuming and tedious, one can take advantage of already extant resources by adapting them to a particular application. This reduces the amount of human effort required to develop a new language resource. In this paper, we introduce an algorithm to convert an HPSG-based treebank into its parallel dependency-based treebank. With this converter, we can automatically create a new language resource from an existing treebank developed based on a grammar formalism. Our proposed algorithm is able to create both projective and non-projective dependency trees.
TweetNorm\_es: an annotated corpus for Spanish microtext normalization
Authors: I{\~n}aki Alegria, Nora Aranberri, Pere Comas, V{\'\i}ctor Fresno, Pablo Gamallo, Lluis Padr{\'o}, I{\~n}aki San Vicente, Jordi Turmo, Arkaitz Zubiaga
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Domain Adaptation, Lexical Normalization, Machine Translation, Sentiment Analysis

In this paper we introduce TweetNorm{\_}es, an annotated corpus of tweets in Spanish language, which we make publicly available under the terms of the CC-BY license. This corpus is intended for development and testing of microtext normalization systems. It was created for Tweet-Norm, a tweet normalization workshop and shared task, and is the result of a joint annotation effort from different research groups. In this paper we describe the methodology defined to build the corpus as well as the guidelines followed in the annotation process. We also present a brief overview of the Tweet-Norm shared task, as the first evaluation environment where the corpus was used.
The Procedure of Lexico-Semantic Annotation of Sk\ladnica Treebank
Authors: El{\.z}bieta Hajnicz
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Semantic Parsing, Sentence, Word Sense Disambiguation

In this paper, the procedure of lexico-semantic annotation of Sk{\l}adnica Treebank using Polish WordNet is presented. Other semantically annotated corpora, in particular treebanks, are outlined first. Resources involved in annotation as well as a tool called Semantikon used for it are described. The main part of the paper is the analysis of the applied procedure. It consists of the basic and correction phases. During basic phase all nouns, verbs and adjectives are annotated with wordnet senses. The annotation is performed independently by two linguists. During the correction phase, conflicts are resolved by the linguist supervising the process. Multi-word units obtain special tags, synonyms and hypernyms are used for senses absent in Polish WordNet. Additionally, each sentence receives its general assessment. Finally, some statistics of the results of annotation are given, including inter-annotator agreement. The final resource is represented in XML files preserving the structure of Sk{\l}adnica.