pwc-archive/papers-with-abstracts huggingface dataset:
Fast Labeling and Transcription with the Speechalyzer Toolkit
Authors: Felix Burkhardt
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Audio Classification, Benchmarking, Classification, General Classification, speech-recognition, Speech Recognition, Speech Synthesis, Speech-to-Text, text-to-speech, Text to Speech

We describe a software tool named Speechalyzer which is optimized to process large speech data sets with respect to transcription, labeling and annotation. It is implemented as a client server based framework in Java and interfaces software for speech recognition, synthesis, speech classification and quality evaluation. The application is mainly the processing of training data for speech recognition and classification models and performing benchmarking tests on speech to text, text to speech and speech categorization software systems.
Smooth Sailing for STEVIN
Authors: Peter Spyns, Elisabeth D{'}Halleweyn
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5

In this paper we report on the past evaluation of the STEVIN programme in the field of Human Language Technology for Dutch (HLTD). STEVIN was a 11.4 M euro programme that was jointly organised and financed by the Flemish and Dutch governments. The aim was to provide academia and industry with basic building blocks for a linguistic infrastructure for the Dutch language. An independent evaluation has been carried out. The evaluators concluded that the most important targets of the STEVIN programme have been achieved to a very high extent. In this paper, we summarise the context, the evaluation method, the resulting resources and the highlights of the STEVIN final evaluation.
Automatic Speech Recognition on a Firefighter TETRA Broadcast Channel
Authors: Daniel Stein, Bela Usabaev
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Keyword Extraction, Language Modelling, Speaker Recognition, speech-recognition, Speech Recognition

For a reliable keyword extraction on firefighter radio communication, a strong automatic speech recognition system is needed. However, real-life data poses several challenges like a distorted voice signal, background noise and several different speakers. Moreover, the domain is out-of-scope for common language models, and the available data is scarce. In this paper, we introduce the PRONTO corpus, which consists of German firefighter exercise transcriptions. We show that by standard adaption techniques the recognition rate already rises from virtually zero to up to 51.7{\%} and can be further improved by domain-specific rules to 47.9{\%}. Extending the acoustic material by semi-automatic transcription and crawled in-domain written material, we arrive at a WER of 45.2{\%}.
Building a Basque-Chinese Dictionary by Using English as Pivot
Authors: Xabier Saralegi, Iker Manterola, I{\~n}aki San Vicente
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Translation

Bilingual dictionaries are key resources in several fields such as translation, language learning or various NLP tasks. However, only major languages have such resources. Automatically built dictionaries by using pivot languages could be a useful resource in these circumstances. Pivot-based bilingual dictionary building is based on merging two bilingual dictionaries which share a common language (e.g. LA-LB, LB-LC) in order to create a dictionary for a new language pair (e.g LA-LC). This process may include wrong translations due to the polisemy of words. We built Basque-Chinese (Mandarin) dictionaries automatically from Basque-English and Chinese-English dictionaries. In order to prune wrong translations we used different methods adequate for less resourced languages. Inverse Consultation and Distributional Similarity methods are used because they just depend on easily available resources. Finally, we evaluated manually the quality of the built dictionaries and the adequacy of the methods. Both Inverse Consultation and Distributional Similarity provide good precision of translations but recall is seriously damaged. Distributional similarity prunes rare translations more accurately than other methods.
Mining Sentiment Words from Microblogs for Predicting Writer-Reader Emotion Transition
Authors: Yi-jie Tang, Hsin-Hsi Chen
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Emotion Classification, Emotion Recognition, Sentiment Analysis

The conversations between posters and repliers in microblogs form a valuable writer-reader emotion corpus. This paper adopts a log relative frequency ratio to investigate the linguistic features which affect emotion transitions, and applies the results to predict writers' and readers' emotions. A 4-class emotion transition predictor, a 2-class writer emotion predictor, and a 2-class reader emotion predictor are proposed and compared.
A High-Quality Web Corpus of Czech
Authors: Johanka Spoustov{\'a}, Miroslav Spousta
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Articles, Machine Translation, POS, POS Tagging, Vocal Bursts Intensity Prediction

In our paper, we present main results of the Czech grant project Internet as a Language Corpus, whose aim was to build a corpus of Czech web texts and to develop and publicly release related software tools. Our corpus may not be the largest web corpus of Czech, but it maintains very good language quality due to high portion of human work involved in the corpus development process. We describe the corpus contents (2.65 billions of words divided into three parts -- 450 millions of words from news and magazines articles, 1 billion of words from blogs, diaries and other non-reviewed literary units, 1.1 billion of words from discussions messages), particular steps of the corpus creation (crawling, HTML and boilerplate removal, near duplicates removal, language filtering) and its automatic language annotation (POS tagging, syntactic parsing). We also describe our software tools being released under an open source license, especially a fast linear-time module for removing near-duplicates on a paragraph level.
German Verb Patterns and Their Implementation in an Electronic Dictionary
Authors: Marc Luder
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Dependency Parsing, Sentence

We describe an electronic lexical resource for German and the structure of its lexicon entries, notably the structure of verbal single-word and multi-word entries. The verb as the center of the sentence structure, as held by dependency models, is also a basic principle of the JAKOB narrative analysis application, for which the dictionary is the background. Different linguistic layers are combined for construing lexicon entries with a rich set of syntactic and semantic properties, suited to represent the syntactic and semantic behavior of verbal expressions (verb patterns), extracted from transcripts of real discourse, thereby lexicalizing the specific meaning of a specific verb pattern in a specific context. Verb patterns are built by the lexicographer by using a parser analyzing the input of a test clause and generating a machine-readable property string with syntactic characteristics and propositions for semantic characteristics grounded in an ontology. As an example, the German idiomatic expression ''''''``an den Karren fahren'''''''' (to come down hard on somebody) demonstrates the overall structure of a dictionary entry. The goal is to build unique dictionary entries (verb patterns) with reference to the whole of their properties.
Large Scale Semantic Annotation, Indexing and Search at The National Archives
Authors: Diana Maynard, Mark A. Greenwood
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5

This paper describes a tool developed to improve access to the enormous volume of data housed at the UK's National Archives, both for the general public and for specialist researchers. The system we have developed, TNA-Search, enables a multi-paradigm search over the entire electronic archive (42TB of data in various formats). The search functionality allows queries that arbitrarily mix any combination of full-text, structural, linguistic and semantic queries. The archive is annotated and indexed with respect to a massive semantic knowledge base containing data from the LOD cloud, data.gov.uk, related TNA projects, and a large geographical database. The semantic annotation component achieves approximately 83{\%} F-measure, which is very reasonable considering the wide range of entities and document types and the open domain. The technologies are being adopted by real users at The National Archives and will form the core of their suite of search tools, with additional in-house interfaces.
QurAna: Corpus of the Quran annotated with Pronominal Anaphora
Authors: Abdul-Baquee Sharaf, Eric Atwell
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Coreference Resolution, Information Retrieval, Machine Translation, Question Answering, Retrieval

This paper presents QurAna: a large corpus created from the original Quranic text, where personal pronouns are tagged with their antecedence. These antecedents are maintained as an ontological list of concepts, which have proved helpful for information retrieval tasks. QurAna is characterized by: (a) comparatively large number of pronouns tagged with antecedent information (over 24,500 pronouns), and (b) maintenance of an ontological concept list out of these antecedents. We have shown useful applications of this corpus. This corpus is first of its kind considering classical Arabic text, which could be used for interesting applications for Modern Standard Arabic as well. This corpus would benefit researchers in obtaining empirical and rules in building new anaphora resolution approaches. Also, such corpus would be used to train, optimize and evaluate existing approaches.
Bootstrapping Sentiment Labels For Unannotated Documents With Polarity PageRank
Authors: Christian Scheible, Hinrich Sch{\"u}tze
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Document Classification, General Classification, Sentiment Analysis

We present a novel graph-theoretic method for the initial annotation of high-confidence training data for bootstrapping sentiment classifiers. We estimate polarity using topic-specific PageRank. Sentiment information is propagated from an initial seed lexicon through a joint graph representation of words and documents. We report improved classification accuracies across multiple domains for the base models and the maximum entropy model bootstrapped from the PageRank annotation.
MLSA --- A Multi-layered Reference Corpus for German Sentiment Analysis
Authors: Simon Clematide, Stefan Gindl, Manfred Klenner, Stefanos Petrakis, Robert Remus, Josef Ruppenhofer, Ulli Waltinger, Michael Wiegand
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Opinion Mining, Question Answering, Sentence, Sentiment Analysis

In this paper, we describe MLSA, a publicly available multi-layered reference corpus for German-language sentiment analysis. The construction of the corpus is based on the manual annotation of 270 German-language sentences considering three different layers of granularity. The sentence-layer annotation, as the most coarse-grained annotation, focuses on aspects of objectivity, subjectivity and the overall polarity of the respective sentences. Layer 2 is concerned with polarity on the word- and phrase-level, annotating both subjective and factual language. The annotations on Layer 3 focus on the expression-level, denoting frames of private states such as objective and direct speech events. These three layers and their respective annotations are intended to be fully independent of each other. At the same time, exploring for and discovering interactions that may exist between different layers should also be possible. The reliability of the respective annotations was assessed using the average pairwise agreement and Fleiss' multi-rater measures. We believe that MLSA is a beneficial resource for sentiment analysis research, algorithms and applications that focus on the German language.
Versatile Speech Databases for High Quality Synthesis for Basque
Authors: I{\~n}aki Sainz, Daniel Erro, Eva Navas, Inma Hern{\'a}ez, Jon Sanchez, Ibon Saratxaga, Igor Odriozola
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Emotional Speech Synthesis, Speech Synthesis, Vocal Bursts Intensity Prediction, Voice Conversion

This paper presents three new speech databases for standard Basque. They are designed primarily for corpus-based synthesis but each database has its specific purpose: 1) AhoSyn: high quality speech synthesis (recorded also in Spanish), 2) AhoSpeakers: voice conversion and 3) AhoEmo3: emotional speech synthesis. The whole corpus design and the recording process are described with detail. Once the databases were collected all the data was automatically labelled and annotated. Then, an HMM-based TTS voice was built and subjectively evaluated. The results of the evaluation are pretty satisfactory: 3.70 MOS for Basque and 3.44 for Spanish. Therefore, the evaluation assesses the quality of this new speech resource and the validity of the automated processing presented.
TIMEN: An Open Temporal Expression Normalisation Resource
Authors: Hector Llorens, Leon Derczynski, Robert Gaizauskas, Estela Saquete
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Information Retrieval, Knowledge Base Population, Question Answering, Timex normalization

Temporal expressions are words or phrases that describe a point, duration or recurrence in time. Automatically annotating these expressions is a research goal of increasing interest. Recognising them can be achieved with minimally supervised machine learning, but interpreting them accurately (normalisation) is a complex task requiring human knowledge. In this paper, we present TIMEN, a community-driven tool for temporal expression normalisation. TIMEN is derived from current best approaches and is an independent tool, enabling easy integration in existing systems. We argue that temporal expression normalisation can only be effectively performed with a large knowledge base and set of rules. Our solution is a framework and system with which to capture this knowledge for different languages. Using both existing and newly-annotated data, we present results showing competitive performance and invite the IE community to contribute to a knowledge base in order to solve the temporal expression normalisation problem.
Measuring Interlanguage: Native Language Identification with L1-influence Metrics
Authors: Julian Brooke, Graeme Hirst
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Language Acquisition, Language Identification, Machine Translation, Native Language Identification, Text Classification, Translation, Word Translation

The task of native language (L1) identification suffers from a relative paucity of useful training corpora, and standard within-corpus evaluation is often problematic due to topic bias. In this paper, we introduce a method for L1 identification in second language (L2) texts that relies only on much more plentiful L1 data, rather than the L2 texts that are traditionally used for training. In particular, we do word-by-word translation of large L1 blog corpora to create a mapping to L2 forms that are a possible result of language transfer, and then use that information for unsupervised classification. We show this method is effective in several different learner corpora, with bigram features being particularly useful.
DISLOG: A logic-based language for processing discourse structures
Authors: Patrick Saint-Dizier
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Machine Translation, Question Answering, Text Generation

In this paper, we present the foundations and the properties of the DISLOG language, a logic-based language designed to describe and implement discourse structure analysis. Dislog has the flexibility and the expressiveness of a rule-based system, it offers the possibility to include knowledge and reasoning capabilities and the expression a variety of well-formedness constraints proper to discourse. Dislog is embedded into the platform that offers an engine with various processing capabilities and a programming environment.
A Gold Standard for Relation Extraction in the Food Domain
Authors: Michael Wiegand, Benjamin Roth, Eva Lasarcyk, Stephanie Köser, Dietrich Klakow
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Knowledge Base Population, Relation, Relation Extraction

We present a gold standard for semantic relation extraction in the food domain for German. The relation types that we address are motivated by scenarios for which IT applications present a commercial potential, such as virtual customer advice in which a virtual agent assists a customer in a supermarket in finding those products that satisfy their needs best. Moreover, we focus on those relation types that can be extracted from natural language text corpora, ideally content from the internet, such as web forums, that are easy to retrieve. A typical relation type that meets these requirements are pairs of food items that are usually consumed together. Such a relation type could be used by a virtual agent to suggest additional products available in a shop that would potentially complement the items a customer has already in their shopping cart. Our gold standard comprises structural data, i.e. relation tables, which encode relation instances. These tables are vital in order to evaluate natural language processing systems that extract those relations.
A Repository of Rules and Lexical Resources for Discourse Structure Analysis: the Case of Explanation Structures
Authors: Sarah Bourse, Patrick Saint-Dizier
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Text Generation

In this paper, we present an analysis method, a set of rules, lexical resources dedicated to discourse relation identification, in particular for explanation analysis. The following relations are described with prototypical rules: instructions, advice, warnings, illustration, restatement, purpose, condition, circumstance, concession, contrast and some forms of causes. Rules are developed for French and English. The approach used to describe the analysis of such relations is basically generative and also provides a conceptual view of explanation. The implementation is realized in Dislog, using the logic-based platform, and the Dislog language, that also allows for the integration of knowledge and reasoning into rules describing the structure of explanation.
Risk Analysis and Prevention: LELIE, a Tool dedicated to Procedure and Requirement Authoring
Authors: Flore Barcellini, Camille Albert, Corinne Grosse, Patrick Saint-Dizier
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Action Detection

In this paper, we present the first phase of the LELIE project. A tool that detects business errors in technical documents such as procedures or requirements is introduced. The objective is to improve readability and to check for some elements of contents so that risks that could be entailed by misunderstandings or typos can be prevented. Based on a cognitive ergonomics analysis, we survey a number of frequently encountered types of errors and show how they can be detected using the discourse analysis platform. We show how errors can be annotated, give figures on error frequencies and analyze how technical writers perceive our system.
WebAnnotator, an Annotation Tool for Web Pages
Authors: Xavier Tannier
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Image Retrieval, Information Retrieval

This article presents WebAnnotator, a new tool for annotating Web pages. WebAnnotator is implemented as a Firefox extension, allowing annotation of both offline and inline pages. The HTML rendering fully preserved and all annotations consist in new HTML spans with specific styles. WebAnnotator provides an easy and general-purpose framework and is made available under CeCILL free license (close to GNU GPL), so that use and further contributions are made simple. All parts of an HTML document can be annotated: text, images, videos, tables, menus, etc. The annotations are created by simply selecting a part of the document and clicking on the relevant type and subtypes. The annotated elements are then highlighted in a specific color. Annotation schemas can be defined by the user by creating a simple DTD representing the types and subtypes that must be highlighted. Finally, annotations can be saved (HTML with highlighted parts of documents) or exported (in a machine-readable format).
Towards a comprehensive open repository of Polish language resources
Authors: Maciej Ogrodniczuk, Piotr P{\k{e}}zik, Adam Przepi{\'o}rkowski
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5

The aim of this paper is to present current efforts towards the creation of a comprehensive open repository of Polish language resources and tools (LRTs). The work described here is carried out within the CESAR project, member of the META-NET consortium. It has already resulted in the creation of the Computational Linguistics in Poland site containing an exhaustive collection of Polish LRTs. Current work is focused on the creation of new LRTs and, esp., the enhancement of existing LRTs, such as parallel corpora, annotated corpora of written and spoken Polish and morphological dictionaries to be made available via the META-SHARE repository.
Towards an LFG parser for Polish: An exercise in parasitic grammar development
Authors: Agnieszka Patejuk, Adam Przepi{\'o}rkowski
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5

While it is possible to build a formal grammar manually from scratch or, going to another extreme, to derive it automatically from a treebank, the development of the LFG grammar of Polish presented in this paper is different from both of these methods as it relies on extensive reuse of existing language resources for Polish. LFG grammars minimally provide two levels of representation: constituent structure (c-structure) produced by context-free phrase structure rules and functional structure (f-structure) created by functional descriptions. The c-structure was based on a DCG grammar of Polish, while the f-structure level was mainly inspired by the available HPSG analyses of Polish. The morphosyntactic information needed to create a lexicon may be taken from one of the following resources: a morphological analyser, a treebank or a corpus. Valence information from the dictionary which accompanies the DCG grammar was converted so that subcategorisation is stated in terms of grammatical functions rather than categories; additionally, missing valence frames may be extracted from the treebank. The obtained grammar is evaluated using constructed testsuites (half of which were provided by previous grammars) and the treebank.
Constructive Interaction for Talking about Interesting Topics
Authors: Kristiina Jokinen, Graham Wilcock
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Management, Speech Recognition, Speech Synthesis, World Knowledge

The paper discusses mechanisms for topic management in conversations, concentrating on interactions where the interlocutors react to each other's presentation of new information and construct a shared context in which to exchange information about interesting topics. This is illustrated with a robot simulator that can talk about unrestricted (open-domain) topics that the human interlocutor shows interest in. Wikipedia is used as the source of information from which the robotic agent draws its world knowledge.