pwc-archive/papers-with-abstracts huggingface dataset:
Evaluating Lemmatization Models for Machine-Assisted Corpus-Dictionary Linkage
Authors: Kevin Black, Eric Ringger, Paul Felt, Kevin Seppi, Kristian Heal, Deryle Lonsdale
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Lemmatization, Morphological Analysis, Translation, Word Sense Disambiguation

The task of corpus-dictionary linkage (CDL) is to annotate each word in a corpus with a link to an appropriate dictionary entry that documents the sense and usage of the word. Corpus-dictionary linked resources include concordances, dictionaries with word usage examples, and corpora annotated with lemmas or word-senses. Such CDL resources are essential in learning a language and in linguistic research, translation, and philology. Lemmatization is a common approximation to automating corpus-dictionary linkage, where lemmas are treated as dictionary entry headwords. We intend to use data-driven lemmatization models to provide machine assistance to human annotators in the form of pre-annotations, and thereby reduce the costs of CDL annotation. In this work we adapt the discriminative string transducer DirecTL+ to perform lemmatization for classical Syriac, a low-resource language. We compare the accuracy of DirecTL+ with the Morfette discriminative lemmatizer. DirecTL+ achieves 96.92{\%} overall accuracy but only by a margin of 0.86{\%} over Morfette at the cost of a longer time to train the model. Error analysis on the models provides guidance on how to apply these models in a machine assistance setting for corpus-dictionary linkage.
Finite-state morphological transducers for three Kypchak languages
Authors: Jonathan Washington, Ilnar Salimzyanov, Francis Tyers
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Machine Translation

This paper describes the development of free/open-source finite-state morphological transducers for three Turkic languages―Kazakh, Tatar, and Kumyk―representing one language from each of the three sub-branches of the Kypchak branch of Turkic. The finite-state toolkit used for the work is the Helsinki Finite-State Toolkit (HFST). This paper describes how the development of a transducer for each subsequent closely-related language took less development time. An evaluation is presented which shows that the transducers all have a reasonable coverage―around 90{\textbackslash}{\%}―on freely available corpora of the languages, and high precision over a manually verified test set.
Automatic creation of WordNets from parallel corpora
Authors: Antoni Oliver, Salvador Climent
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Information Retrieval, Translation, Word Alignment

In this paper we present the evaluation results for the creation of WordNets for five languages (Spanish, French, German, Italian and Portuguese) using an approach based on parallel corpora. We have used three very large parallel corpora for our experiments: DGT-TM, EMEA and ECB. The English part of each corpus is semantically tagged using Freeling and UKB. After this step, the process of WordNet creation is converted into a word alignment problem, where we want to alignWordNet synsets in the English part of the corpus with lemmata on the target language part of the corpus. The word alignment algorithm used in these experiments is a simple most frequent translation algorithm implemented into the WN-Toolkit. The obtained precision values are quite satisfactory, but the overall number of extracted synset-variant pairs is too low, leading into very poor recall values. In the conclusions, the use of more advanced word alignment algorithms, such as Giza++, Fast Align or Berkeley aligner is suggested.
The Polish Summaries Corpus
Authors: Maciej Ogrodniczuk, Mateusz Kope{\'c}
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Articles, Document Summarization

This article presents the Polish Summaries Corpus, a new resource created to support the development and evaluation of the tools for automated single-document summarization of Polish. The Corpus contains a large number of manual summaries of news articles, with many independently created summaries for a single text. Such approach is supposed to overcome the annotator bias, which is often described as a problem during the evaluation of the summarization algorithms against a single gold standard. There are several summarizers developed specifically for Polish language, but their in-depth evaluation and comparison was impossible without a large, manually created corpus. We present in detail the process of text selection, annotation process and the contents of the corpus, which includes both abstract free-word summaries, as well as extraction-based summaries created by selecting text spans from the original document. Finally, we describe how that resource could be used not only for the evaluation of the existing summarization tools, but also for studies on the human summarization process in Polish language.
GlobalPhone: Pronunciation Dictionaries in 20 Languages
Authors: Tanja Schultz, Tim Schlippe
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Language Identification, Language Modelling, Speaker Recognition, speech-recognition, Speech Recognition, Speech Synthesis

This paper describes the advances in the multilingual text and speech database GlobalPhone, a multilingual database of high-quality read speech with corresponding transcriptions and pronunciation dictionaries in 20 languages. GlobalPhone was designed to be uniform across languages with respect to the amount of data, speech quality, the collection scenario, the transcription and phone set conventions. With more than 400 hours of transcribed audio data from more than 2000 native speakers GlobalPhone supplies an excellent basis for research in the areas of multilingual speech recognition, rapid deployment of speech processing systems to yet unsupported languages, language identification tasks, speaker recognition in multiple languages, multilingual speech synthesis, as well as monolingual speech recognition in a large variety of languages. Very recently the GlobalPhone pronunciation dictionaries have been made available for research and commercial purposes by the European Language Resources Association (ELRA).
Pre-ordering of phrase-based machine translation input in translation workflow
Authors: Alex Ceausu, ru, Sabine Hunsicker
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Machine Translation, Sentence, Translation

Word reordering is a difficult task for decoders when the languages involved have a significant difference in syntax. Phrase-based statistical machine translation (PBSMT), preferred in commercial settings due to its maturity, is particularly prone to errors in long range reordering. Source sentence pre-ordering, as a pre-processing step before PBSMT, proved to be an efficient solution that can be achieved using limited resources. We propose a dependency-based pre-ordering model with parameters optimized using a reordering score to pre-order the source sentence. The source sentence is then translated using an existing phrase-based system. The proposed solution is very simple to implement. It uses a hierarchical phrase-based statistical machine translation system (HPBSMT) for pre-ordering, combined with a PBSMT system for the actual translation. We show that the system can provide alternate translations of less post-editing effort in a translation workflow with German as the source language.
A Vector Space Model for Syntactic Distances Between Dialects
Authors: Emanuele Di Buccio, Giorgio Maria Di Nunzio, Gianmaria Silvello
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

Syntactic comparison across languages is essential in the research field of linguistics, e.g. when investigating the relationship among closely related languages. In IR and NLP, the syntactic information is used to understand the meaning of word occurrences according to the context in which their appear. In this paper, we discuss a mathematical framework to compute the distance between languages based on the data available in current state-of-the-art linguistic databases. This framework is inspired by approaches presented in IR and NLP.
A finite-state morphological analyzer for a Lakota HPSG grammar
Authors: Christian Curtis
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Machine Translation, Translation

This paper reports on the design and implementation of a morphophonological analyzer for Lakota, a member of the Siouan language family. The initial motivation for this work was to support development of a precision implemented grammar for Lakota on the basis of the LinGO Grammar Matrix. A finite-state transducer (FST) was developed to adapt LakotaÂ’s complex verbal morphology into a form directly usable as input to the Grammar Matrix-derived grammar. As the FST formalism can be applied in both directions, this approach also supports generative output of correct surface forms from the implemented grammar. This article describes the approach used to model Lakota verbal morphology using finite-state methods. It also discusses the results of developing a lexicon from existing text and evaluating its application to related but novel text. The analyzer presented here, along with its companion precision grammar, explores an approach that may have application in enabling machine translation for endangered and under-resourced languages.
A Wikipedia-based Corpus for Contextualized Machine Translation
Authors: Jennifer Drexler, Pushpendre Rastogi, Jacqueline Aguilar, Benjamin Van Durme, Matt Post
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Articles, Domain Adaptation, Language Modeling, Language Modelling, Machine Translation, Translation

We describe a corpus for target-contextualized machine translation (MT), where the task is to improve the translation of source documents using language models built over presumably related documents in the target language. The idea presumes a situation where most of the information about a topic is in a foreign language, yet some related target-language information is known to exist. Our corpus comprises a set of curated English Wikipedia articles describing news events, along with (i) their Spanish counterparts and (ii) some of the Spanish source articles cited within them. In experiments, we translated these Spanish documents, treating the English articles as target-side context, and evaluate the effect on translation quality when including target-side language models built over this English context and interpolated with other, separately-derived language model data. We find that even under this simplistic baseline approach, we achieve significant improvements as measured by BLEU score.
Mapping WordNet Domains, WordNet Topics and Wikipedia Categories to Generate Multilingual Domain Specific Resources
Authors: Sp Gella, ana, Carlo Strapparava, Vivi Nastase
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Text Categorization, Word Sense Disambiguation

In this paper we present the mapping between WordNet domains and WordNet topics, and the emergent Wikipedia categories. This mapping leads to a coarse alignment between WordNet and Wikipedia, useful for producing domain-specific and multilingual corpora. Multilinguality is achieved through the cross-language links between Wikipedia categories. Research in word-sense disambiguation has shown that within a specific domain, relevant words have restricted senses. The multilingual, and comparable, domain-specific corpora we produce have the potential to enhance research in word-sense disambiguation and terminology extraction in different languages, which could enhance the performance of various NLP tasks.
Annotating Events in an Emotion Corpus
Authors: Sophia Lee, Shoushan Li, Chu-Ren Huang
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Emotion Detection and Classification

This paper presents the development of a Chinese event-based emotion corpus. It specifically describes the corpus design, collection and annotation. The proposed annotation scheme provides a consistent way of identifying some emotion-associated events (namely pre-events and post-events). Corpus data show that there are significant interactions between emotions and pre-events as well as that of between emotion and post-events. We believe that emotion as a pivot event underlies an innovative approach towards a linguistic model of emotion as well as automatic emotion detection and classification.
Statistical Analysis of Multilingual Text Corpus and Development of Language Models
Authors: Shyam Sundar Agrawal, {Abhimanue}, shweta bansal, Minakshi Mahajan
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Language Identification, Language Modelling, Speech Language Identification

This paper presents two studies, first a statistical analysis for three languages i.e. Hindi, Punjabi and Nepali and the other, development of language models for three Indian languages i.e. Indian English, Punjabi and Nepali. The main objective of this study is to find distinction among these languages and development of language models for their identification. Detailed statistical analysis have been done to compute the information about entropy, perplexity, vocabulary growth rate etc. Based on statistical features a comparative analysis has been done to find the similarities and differences among these languages. Subsequently an effort has been made to develop a trigram model of Indian English, Punjabi and Nepali. A corpus of 500000 words of each language has been collected and used to develop their models (unigram, bigram and trigram models). The models have been tried in two different databases- Parallel corpora of French and English and Non-parallel corpora of Indian English, Punjabi and Nepali. In the second case, the performance of the model is comparable. Usage of JAVA platform has provided a special effect for dealing with a very large database with high computational speed. Furthermore various enhancive concepts like Smoothing, Discounting, Back off, and Interpolation have been included for the designing of an effective model. The results obtained from this experiment have been described. The information can be useful for development of Automatic Speech Language Identification System.
New Directions for Language Resource Development and Distribution
Authors: Christopher Cieri, Denise DiPersio, Mark Liberman, Andrea Mazzucchi, Stephanie Strassel, Jonathan Wright
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Transfer Learning

Despite the growth in the number of linguistic data centers around the world, their accomplishments and expansions and the advances they have help enable, the language resources that exist are a small fraction of those required to meet the goals of Human Language Technologies (HLT) for the worldÂ’s languages and the promises they offer: broad access to knowledge, direct communication across language boundaries and engagement in a global community. Using the Linguistic Data Consortium as a focus case, this paper sketches the progress of data centers, summarizes recent activities and then turns to several issues that have received inadequate attention and proposes some new approaches to their resolution.
Rediscovering 15 Years of Discoveries in Language Resources and Evaluation: The LREC Anthology Analysis
Authors: Joseph Mariani, Patrick Paroubek, Gil Francopoulo, Olivier Hamon
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Speech Recognition, Survey

This paper aims at analyzing the content of the LREC conferences contained in the ELRA Anthology over the past 15 years (1998-2013). It follows similar exercises that have been conducted, such as the survey on the IEEE ICASSP conference series from 1976 to 1990, which served in the launching of the ESCA Eurospeech conference, a survey of the Association of Computational Linguistics (ACL) over 50 years of existence, which was presented at the ACL conference in 2012, or a survey over the 25 years (1987-2012) of the conferences contained in the ISCA Archive, presented at Interspeech 2013. It contains first an analysis of the evolution of the number of papers and authors over time, including the study of their gender, nationality and affiliation, and of the collaboration among authors. It then studies the funding sources of the research investigations that are reported in the papers. It conducts an analysis of the evolution of the research topics within the community over time. It finally looks at reuse and plagiarism in the papers. The survey shows the present trends in the conference series and in the Language Resources and Evaluation scientific community. Conducting this survey also demonstrated the importance of a clear and unique identification of authors, papers and other sources to facilitate the analysis. This survey is preliminary, as many other aspects also deserve attention. But we hope it will help better understanding and forging our community in the global village.
Annotating Question Decomposition on Complex Medical Questions
Authors: Kirk Roberts, Kate Masterton, Marcelo Fiszman, Halil Kilicoglu, Dina Demner-Fushman
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Question Answering, Sentence, Text Simplification

This paper presents a method for annotating question decomposition on complex medical questions. The annotations cover multiple syntactic ways that questions can be decomposed, including separating independent clauses as well as recognizing coordinations and exemplifications. We annotate a corpus of 1,467 multi-sentence consumer health questions about genetic and rare diseases. Furthermore, we label two additional medical-specific annotations: (1) background sentences are annotated with a number of medical categories such as symptoms, treatments, and family history, and (2) the central focus of the complex question (a disease) is marked. We present simple baseline results for automatic classification of these annotations, demonstrating the challenging but important nature of this task.
Boosting Open Information Extraction with Noun-Based Relations
Authors: Clarissa Xavier, Vera Lima
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Open Information Extraction, Relation Extraction

Open Information Extraction (Open IE) is a strategy for learning relations from texts, regardless the domain and without predefining these relations. Work in this area has focused mainly on verbal relations. In order to extend Open IE to extract relationships that are not expressed by verbs, we present a novel Open IE approach that extracts relations expressed in noun compounds (NCs), such as (oil, extracted from, olive) from olive oil, or in adjective-noun pairs (ANs), such as (moon, that is, gorgeous) from gorgeous moon. The approach consists of three steps: detection of NCs and ANs, interpretation of these compounds in view of corpus enrichment and extraction of relations from the enriched corpus. To confirm the feasibility of this method we created a prototype and evaluated the impact of the application of our proposal in two state-of-the-art Open IE extractors. Based on these tests we conclude that the proposed approach is an important step to fulfil the gap concerning the extraction of relations within the noun compounds and adjective-noun pairs in Open IE.
Bootstrapping Open-Source English-Bulgarian Computational Dictionary
Authors: Krasimir Angelov
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Machine Translation, Translation

We present an open-source English-Bulgarian dictionary which is a unification and consolidation of existing and freely available resources for the two languages. The new resource can be used as either a pair of two monolingual morphological lexicons, or as a bidirectional translation dictionary between the languages. The structure of the resource is compatible with the existing synchronous English-Bulgarian grammar in Grammatical Framework (GF). This makes it possible to immediately plug it in as a component in a grammar-based translation system that is currently under development in the same framework. This also meant that we had to enrich the dictionary with additional syntactic and semantic information that was missing in the original resources.
Mot\`aMot project: conversion of a French-Khmer published dictionary for building a multilingual lexical system
Authors: Mathieu Mangeot
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

Economic issues related to the information processing techniques are very important. The development of such technologies is a major asset for developing countries like Cambodia and Laos, and emerging ones like Vietnam, Malaysia and Thailand. The MotAMot project aims to computerize an under-resourced language: Khmer, spoken mainly in Cambodia. The main goal of the project is the development of a multilingual lexical system targeted for Khmer. The macrostructure is a pivot one with each word sense of each language linked to a pivot axi. The microstructure comes from a simplification of the explanatory and combinatory dictionary. The lexical system has been initialized with data coming mainly from the conversion of the French-Khmer bilingual dictionary of Denis Richer from Word to XML format. The French part was completed with pronunciation and parts-of-speech coming from the FeM French-english-Malay dictionary. The Khmer headwords noted in IPA in the Richer dictionary were converted to Khmer writing with OpenFST, a finite state transducer tool. The resulting resource is available online for lookup, editing, download and remote programming via a REST API on a Jibiki platform.
JUST.ASK, a QA system that learns to answer new questions from previous interactions
Authors: S{\'e}rgio Curto, Ana C. Mendes, Pedro Curto, Lu{\'\i}sa Coheur, {\^A}ngela Costa
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Passage Retrieval, Question Answering, Retrieval

We present JUST.ASK, a publicly available Question Answering system, which is freely available. Its architecture is composed of the usual Question Processing, Passage Retrieval and Answer Extraction components. Several details on the information generated and manipulated by each of these components are also provided to the user when interacting with the demonstration. Since JUST.ASK also learns to answer new questions based on usersÂ’ feedback, (s)he is invited to identify the correct answers. These will then be used to retrieve answers to future questions.
Design and Development of an Online Computational Framework to Facilitate Language Comprehension Research on Indian Languages
Authors: Manjira Sinha, Tirthankar Dasgupta, Anupam Basu
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Reading Comprehension, Sentence

In this paper we have developed an open-source online computational framework that can be used by different research groups to conduct reading researches on Indian language texts. The framework can be used to develop a large annotated Indian language text comprehension data from different user based experiments. The novelty in this framework lies in the fact that it brings different empirical data-collection techniques for text comprehension under one roof. The framework has been customized specifically to address language particularities for Indian languages. It will also offer many types of automatic analysis on the data at different levels such as full text, sentence and word level. To address the subjectivity of text difficulty perception, the framework allows to capture user background against multiple factors. The assimilated data can be automatically cross referenced against varying strata of readers.