pwc-archive/papers-with-abstracts huggingface dataset:
Synergy of Nederlab and
Authors: Martin Reynaert
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Optical Character Recognition (OCR)

In two concurrent projects in the Netherlands we are further developing TICCL or Text-Induced Corpus Clean-up. In project Nederlab TICCL is set to work on diachronic Dutch text. To this end it has been equipped with the largest diachronic lexicon and a historical name list developed at the Institute for Dutch Lexicology or INL. In project @PhilosTEI TICCL will be set to work on a fair range of European languages. We present a new implementation in C++ of the system which has been tailored to be easily adaptable to different languages. We further revisit prior work on diachronic Portuguese in which it was compared to VARD2 which had been manually adapted to Portuguese. This tested the new mechanisms for ranking correction candidates we have devised. We then move to evaluating the new TICCL port on a very large corpus of Dutch books known as EDBO, digitized by the Dutch National Library. The results show that TICCL scales to the largest corpus sizes and performs excellently raising the quality of the Gold Standard EDBO book by about 20{\%} to 95{\%} word accuracy. Simultaneous unsupervised post-correction of 10,000 digitized books is now a real option.
Quality Estimation for Synthetic Parallel Data Generation
Authors: Raphael Rubino, Antonio Toral, Nikola Ljube{\v{s}}i{\'c}, Gema Ram{\'\i}rez-S{\'a}nchez
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Machine Translation, Sentence, Translation

This paper presents a novel approach for parallel data generation using machine translation and quality estimation. Our study focuses on pivot-based machine translation from English to Croatian through Slovene. We generate an English―Croatian version of the Europarl parallel corpus based on the English―Slovene Europarl corpus and the Apertium rule-based translation system for Slovene―Croatian. These experiments are to be considered as a first step towards the generation of reliable synthetic parallel data for under-resourced languages. We first collect small amounts of aligned parallel data for the Slovene―Croatian language pair in order to build a quality estimation system for sentence-level Translation Edit Rate (TER) estimation. We then infer TER scores on automatically translated Slovene to Croatian sentences and use the best translations to build an English―Croatian statistical MT system. We show significant improvement in terms of automatic metrics obtained on two test sets using our approach compared to a random selection of synthetic parallel data.
An Out-of-Domain Test Suite for Dependency Parsing of German
Authors: Wolfgang Seeker, Jonas Kuhn
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Dependency Parsing, Domain Adaptation, Text Normalization

We present a dependency conversion of five German test sets from five different genres. The dependency representation is made as similar as possible to the dependency representation of TiGer, one of the two big syntactic treebanks of German. The purpose of these test sets is to enable researchers to test dependency parsing models on several different data sets from different text genres. We discuss some easy to compute statistics to demonstrate the variation and differences in the test sets and provide some baseline experiments where we test the effect of additional lexical knowledge on the out-of-domain performance of two state-of-the-art dependency parsers. Finally, we demonstrate with three small experiments that text normalization may be an important step in the standard processing pipeline when applied in an out-of-domain setting.
Representing Multilingual Data as Linked Data: the Case of BabelNet 2.0
Authors: Maud Ehrmann, Francesco Cecconi, Daniele Vannella, John Philip McCrae, Philipp Cimiano, Roberto Navigli
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

Recent years have witnessed a surge in the amount of semantic information published on the Web. Indeed, the Web of Data, a subset of the Semantic Web, has been increasing steadily in both volume and variety, transforming the Web into a {`}global database{'} in which resources are linked across sites. Linguistic fields -- in a broad sense -- have not been left behind, and we observe a similar trend with the growth of linguistic data collections on the so-called {`}Linguistic Linked Open Data (LLOD) cloud{'}. While both Semantic Web and Natural Language Processing communities can obviously take advantage of this growing and distributed linguistic knowledge base, they are today faced with a new challenge, i.e., that of facilitating multilingual access to the Web of data. In this paper we present the publication of BabelNet 2.0, a wide-coverage multilingual encyclopedic dictionary and ontology, as Linked Data. The conversion made use of lemon, a lexicon model for ontologies particularly well-suited for this enterprise. The result is an interlinked multilingual (lexical) resource which can not only be accessed on the LOD, but also be used to enrich existing datasets with linguistic information, or to support the process of mapping datasets across languages.
NOMAD: Linguistic Resources and Tools Aimed at Policy Formulation and Validation
Authors: George Kiomourtzis, George Giannakopoulos, Georgios Petasis, Pythagoras Karampiperis, Vangelis Karkaletsis
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Retrieval, Sentiment Analysis, Text Retrieval, Topic Classification

The NOMAD project (Policy Formulation and Validation through non Moderated Crowd-sourcing) is a project that supports policy making, by providing rich, actionable information related to how citizens perceive different policies. NOMAD automatically analyzes citizen contributions to the informal web (e.g. forums, social networks, blogs, newsgroups and wikis) using a variety of tools. These tools comprise text retrieval, topic classification, argument detection and sentiment analysis, as well as argument summarization. NOMAD provides decision-makers with a full arsenal of solutions starting from describing a domain and a policy to applying content search and acquisition, categorization and visualization. These solutions work in a collaborative manner in the policy-making arena. NOMAD, thus, embeds editing, analysis and visualization technologies into a concrete framework, applicable in a variety of policy-making and decision support settings In this paper we provide an overview of the linguistic tools and resources of NOMAD.
Encompassing a spectrum of LT users in the CLARIN-DK Infrastructure
Authors: Lina Henriksen, Dorte Haltrup Hansen, Bente Maegaard, Bolette S. Pedersen, ford, Claus Povlsen
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

CLARIN-DK is a platform with language resources constituting the Danish part of the European infrastructure CLARIN ERIC. Unlike some other language based infrastructures CLARIN-DK is not solely a repository for upload and storage of data, but also a platform of web services permitting the user to process data in various ways. This involves considerable complications in relation to workflow requirements. The CLARIN-DK interface must guide the user to perform the necessary steps of a workflow; even when the user is inexperienced and perhaps has an unclear conception of the requested results. This paper describes a user driven approach to creating a user interface specification for CLARIN-DK. We indicate how different user profiles determined different crucial interface design options. We also describe some use cases established in order to give illustrative examples of how the platform may facilitate research.
Automatically enriching spoken corpora with syntactic information for linguistic studies
Authors: Alexis Nasr, Frederic Bechet, Benoit Favre, Thierry Bazillon, Jose Deulofeu, Andre Valli
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

Syntactic parsing of speech transcriptions faces the problem of the presence of disfluencies that break the syntactic structure of the utterances. We propose in this paper two solutions to this problem. The first one relies on a disfluencies predictor that detects disfluencies and removes them prior to parsing. The second one integrates the disfluencies in the syntactic structure of the utterances and train a disfluencies aware parser.
Propa-L: a semantic filtering service from a lexical network created using Games With A Purpose
Authors: Mathieu Lafourcade, Kar{\"e}n Fort
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

This article presents Propa-L, a freely accessible Web service that allows to semantically filter a lexical network. The language resources behind the service are dynamic and created through Games With A Purpose. We show an example of application of this service: the generation of a list of keywords for parental filtering on the Web, but many others can be envisaged. Moreover, the propagation algorithm we present here can be applied to any lexical network, in any language.
Less is More? Towards a Reduced Inventory of Categories for Training a Parser for the Italian Stanford Dependencies
Authors: Maria Simi, Cristina Bosco, Simonetta Montemagni
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: TAG

Stanford Dependencies (SD) represent nowadays a de facto standard as far as dependency annotation is concerned. The goal of this paper is to explore pros and cons of different strategies for generating SD annotated Italian texts to enrich the existing Italian Stanford Dependency Treebank (ISDT). This is done by comparing the performance of a statistical parser (DeSR) trained on a simpler resource (the augmented version of the Merged Italian Dependency Treebank or MIDT+) and whose output was automatically converted to SD, with the results of the parser directly trained on ISDT. Experiments carried out to test reliability and effectiveness of the two strategies show that the performance of a parser trained on the reduced dependencies repertoire, whose output can be easily converted to SD, is slightly higher than the performance of a parser directly trained on ISDT. A non-negligible advantage of the first strategy for generating SD annotated texts is that semi-automatic extensions of the training resource are more easily and consistently carried out with respect to a reduced dependency tag set. Preliminary experiments carried out for generating the collapsed and propagated SD representation are also reported.
Meta-Classifiers Easily Improve Commercial Sentiment Detection Tools
Authors: Mark Cieliebak, Oliver D{\"u}rr, Fatih Uzdilli
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: All, BIG-bench Machine Learning, Opinion Mining, Sentiment Analysis

In this paper, we analyze the quality of several commercial tools for sentiment detection. All tools are tested on nearly 30,000 short texts from various sources, such as tweets, news, reviews etc. The best commercial tools have average accuracy of 60{\%}. We then apply machine learning techniques (Random Forests) to combine all tools, and show that this results in a meta-classifier that improves the overall performance significantly.
UnixMan Corpus: A Resource for Language Learning in the Unix Domain
Authors: Kyle Richardson, Jonas Kuhn
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Grounded language learning, Machine Translation, Semantic Parsing

We present a new resource, the UnixMan Corpus, for studying language learning it the domain of Unix utility manuals. The corpus is built by mining Unix (and other Unix related) man pages for parallel example entries, consisting of English textual descriptions with corresponding command examples. The commands provide a grounded and ambiguous semantics for the textual descriptions, making the corpus of interest to work on Semantic Parsing and Grounded Language Learning. In contrast to standard resources for Semantic Parsing, which tend to be restricted to a small number of concepts and relations, the UnixMan Corpus spans a wide variety of utility genres and topics, and consists of hundreds of command and domain entity types. The semi-structured nature of the manuals also makes it easy to exploit other types of relevant information for Grounded Language Learning. We describe the details of the corpus and provide preliminary classification results.
GraPAT: a Tool for Graph Annotations
Authors: Jonathan Sonntag, Manfred Stede
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Dependency Parsing, Sentiment Analysis

We introduce GraPAT, a web-based annotation tool for building graph structures over text. Graphs have been demonstrated to be relevant in a variety of quite diverse annotation efforts and in different NLP applications, and they serve to model annotatorsÂ’ intuitions quite closely. In particular, in this paper we discuss the implementation of graph annotations for sentiment analysis, argumentation structure, and rhetorical text structures. All of these scenarios can create certain problems for existing annotation tools, and we show how GraPAT can help to overcome such difficulties.
Standardisation and Interoperation of Morphosyntactic and Syntactic Annotation Tools for Spanish and their Annotations
Authors: Antonio Pareja-Lora, Guillermo C{\'a}rcamo-Escorza, Alicia Ballesteros-Calvo
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

Linguistic annotation tools and linguistic annotations are scarcely syntactically and/or semantically interoperable. Their low interoperability usually results from the number of factors taken into account in their development and design. These include (i) the type of phenomena annotated (either morphosyntactic, syntactic, semantic, etc.); (ii) how these phenomena are annotated (e.g., the particular guidelines and/or schema used to encode the annotations); and (iii) the languages (Java, C++, etc.) and technologies (as standalone programs, as APIs, as web services, etc.) used to develop them. This low level of interoperability makes it difficult to reuse both the linguistic annotation tools and their annotations in new scenarios, e.g., in natural language processing (NLP) pipelines. In spite of this, developing new linguistic tools from scratch is quite a high time-consuming task that also entails a very high cost. Therefore, cost-effective ways to systematically reuse linguistic tools and annotations must be found urgently. A traditional way to overcome reuse and/or interoperability problems is standardisation. In this paper, we present a web service version of FreeLing that provides standard-compliant morpho-syntactic and syntactic annotations for Spanish, according to several ISO linguistic annotation standards and standard drafts.
A Framework for Compiling High Quality Knowledge Resources From Raw Corpora
Authors: Gongye Jin, Daisuke Kawahara, Sadao Kurohashi
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Dependency Parsing, Machine Translation, Relation Extraction, Vocal Bursts Intensity Prediction

The identification of various types of relations is a necessary step to allow computers to understand natural language text. In particular, the clarification of relations between predicates and their arguments is essential because predicate-argument structures convey most of the information in natural languages. To precisely capture these relations, wide-coverage knowledge resources are indispensable. Such knowledge resources can be derived from automatic parses of raw corpora, but unfortunately parsing still has not achieved a high enough performance for precise knowledge acquisition. We present a framework for compiling high quality knowledge resources from raw corpora. Our proposed framework selects high quality dependency relations from automatic parses and makes use of them for not only the calculation of fundamental distributional similarity but also the acquisition of knowledge such as case frames.
Fuzzy V-Measure - An Evaluation Method for Cluster Analyses of Ambiguous Data
Authors: Jason Utt, Sylvia Springorum, Maximilian K{\"o}per, Sabine Schulte im Walde
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

This paper discusses an extension of the V-measure (Rosenberg and Hirschberg, 2007), an entropy-based cluster evaluation metric. While the original work focused on evaluating hard clusterings, we introduce the Fuzzy V-measure which can be used on data that is inherently ambiguous. We perform multiple analyses varying the sizes and ambiguity rates and show that while entropy-based measures in general tend to suffer when ambiguity increases, a measure with desirable properties can be derived from these in a straightforward manner.
Clustering tweets usingWikipedia concepts
Authors: Guoyu Tang, Yunqing Xia, Weizhi Wang, Raymond Lau, Fang Zheng
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Clustering, Text Clustering

Two challenging issues are notable in tweet clustering. Firstly, the sparse data problem is serious since no tweet can be longer than 140 characters. Secondly, synonymy and polysemy are rather common because users intend to present a unique meaning with a great number of manners in tweets. Enlightened by the recent research which indicates Wikipedia is promising in representing text, we exploit Wikipedia concepts in representing tweets with concept vectors. We address the polysemy issue with a Bayesian model, and the synonymy issue by exploiting the Wikipedia redirections. To further alleviate the sparse data problem, we further make use of three types of out-links in Wikipedia. Evaluation on a twitter dataset shows that the concept model outperforms the traditional VSM model in tweet clustering.
The Tutorbot Corpus --- A Corpus for Studying Tutoring Behaviour in Multiparty Face-to-Face Spoken Dialogue
Authors: Maria Koutsombogera, Samer Al Moubayed, Bajibabu Bollepalli, Ahmed Hussen Abdelaziz, Martin Johansson, Jos{\'e} David Aguas Lopes, Jekaterina Novikova, Catharine Oertel, Kalin Stefanov, G{\"u}l Varol
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Spoken Dialogue Systems

This paper describes a novel experimental setup exploiting state-of-the-art capture equipment to collect a multimodally rich game-solving collaborative multiparty dialogue corpus. The corpus is targeted and designed towards the development of a dialogue system platform to explore verbal and nonverbal tutoring strategies in multiparty spoken interactions. The dialogue task is centered on two participants involved in a dialogue aiming to solve a card-ordering game. The participants were paired into teams based on their degree of extraversion as resulted from a personality test. With the participants sits a tutor that helps them perform the task, organizes and balances their interaction and whose behavior was assessed by the participants after each interaction. Different multimodal signals captured and auto-synchronized by different audio-visual capture technologies, together with manual annotations of the tutors behavior constitute the Tutorbot corpus. This corpus is exploited to build a situated model of the interaction based on the participants temporally-changing state of attention, their conversational engagement and verbal dominance, and their correlation with the verbal and visual feedback and conversation regulatory actions generated by the tutor.
TweetCaT: a tool for building Twitter corpora of smaller languages
Authors: Nikola Ljube{\v{s}}i{\'c}, Darja Fi{\v{s}}er, Toma{\v{z}} Erjavec
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Language Identification

This paper presents TweetCaT, an open-source Python tool for building Twitter corpora that was designed for smaller languages. Using the Twitter search API and a set of seed terms, the tool identifies users tweeting in the language of interest together with their friends and followers. By running the tool for 235 days we tested it on the task of collecting two monitor corpora, one for Croatian and Serbian and the other for Slovene, thus also creating new and valuable resources for these languages. A post-processing step on the collected corpus is also described, which filters out users that tweet predominantly in a foreign language thus further cleans the collected corpora. Finally, an experiment on discriminating between Croatian and Serbian Twitter users is reported.
HindEnCorp - Hindi-English and Hindi-only Corpus for Machine Translation
Authors: Ond{\v{r}}ej Bojar, Vojt{\v{e}}ch Diatka, Pavel Rychl{\'y}, Pavel Stra{\v{n}}{\'a}k, V{\'\i}t Suchomel, Ale{\v{s}} Tamchyna, Daniel Zeman
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Machine Translation, Translation

We present HindEnCorp, a parallel corpus of Hindi and English, and HindMonoCorp, a monolingual corpus of Hindi in their release version 0.5. Both corpora were collected from web sources and preprocessed primarily for the training of statistical machine translation systems. HindEnCorp consists of 274k parallel sentences (3.9 million Hindi and 3.8 million English tokens). HindMonoCorp amounts to 787 million tokens in 44 million sentences. Both the corpora are freely available for non-commercial research and their preliminary release has been used by numerous participants of the WMT 2014 shared translation task.
Combining dependency information and generalization in a pattern-based approach to the classification of lexical-semantic relation instances
Authors: Silvia Nec{\c{s}}ulescu, Sara Mendes, N{\'u}ria Bel
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Classification, General Classification, Information Retrieval, Question Answering, Sentence

This work addresses the classification of word pairs as instances of lexical-semantic relations. The classification is approached by leveraging patterns of co-occurrence contexts from corpus data. The significance of using dependency information, of augmenting the set of dependency paths provided to the system, and of generalizing patterns using part-of-speech information for the classification of lexical-semantic relation instances is analyzed. Results show that dependency information is decisive to achieve better results both in precision and recall, while generalizing features based on dependency information by replacing lexical forms with their part-of-speech increases the coverage of classification systems. Our experiments also make apparent that approaches based on the context where word pairs co-occur are upper-bound-limited by the times these appear in the same sentence. Therefore strategies to use information across sentence boundaries are necessary.
Using Audio Books for Training a Text-to-Speech System
Authors: Chalam, Aimilios aris, Pirros Tsiakoulis, Sotiris Karabetsos, Spyros Raptis
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Diversity, Speech Synthesis, text-to-speech, Text to Speech

Creating new voices for a TTS system often requires a costly procedure of designing and recording an audio corpus, a time consuming and effort intensive task. Using publicly available audiobooks as the raw material of a spoken corpus for such systems creates new perspectives regarding the possibility of creating new synthetic voices quickly and with limited effort. This paper addresses the issue of creating new synthetic voices based on audiobook data in an automated method. As an audiobook includes several types of speech, such as narration, character playing etc., special care is given in identifying the data subset that leads to a more neutral and general purpose synthetic voice. The main goal is to identify and address the effect the audiobook speech diversity on the resulting TTS system. Along with the methodology for coping with this diversity in the speech data, we also describe a set of experiments performed in order to investigate the efficiency of different approaches for automatic data pruning. Further plans for exploiting the diversity of the speech incorporated in an audiobook are also described in the final section and conclusions are drawn.