pwc-archive/papers-with-abstracts huggingface dataset:
A Graph-Based Approach for Computing Free Word Associations
Authors: Gemma Bel Enguix, Reinhard Rapp, Michael Zock
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Language Acquisition

A graph-based algorithm is used to analyze the co-occurrences of words in the British National Corpus. It is shown that the statistical regularities detected can be exploited to predict human word associations. The corpus-derived associations are evaluated using a large test set comprising several thousand stimulus/response pairs as collected from humans. The finding is that there is a high agreement between the two types of data. The considerable size of the test set allows us to split the stimulus words into a number of classes relating to particular word properties. For example, we construct six saliency classes, and for the words in each of these classes we compare the simulation results with the human data. It turns out that for each class there is a close relationship between the performance of our system and human performance. This is also the case for classes based on two other properties of words, namely syntactic and semantic word ambiguity. We interpret these findings as evidence for the claim that human association acquisition must be based on the statistical analysis of perceived language and that when producing associations the detected statistical regularities are replicated.
Developing Text Resources for Ten South African Languages
Authors: Roald Eiselen, Martin Puttkammer
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

The development of linguistic resources for use in natural language processing is of utmost importance for the continued growth of research and development in the field, especially for resource-scarce languages. In this paper we describe the process and challenges of simultaneously developing multiple linguistic resources for ten of the official languages of South Africa. The project focussed on establishing a set of foundational resources that can foster further development of both resources and technologies for the NLP industry in South Africa. The development efforts during the project included creating monolingual unannotated corpora, of which a subset of the corpora for each language was annotated on token, orthographic, morphological and morphosyntactic layers. The annotated subsets includes both development and test sets and were used in the creation of five core-technologies, viz. a tokeniser, sentenciser, lemmatiser, part of speech tagger and morphological decomposer for each language. We report on the quality of these tools for each language and discuss the importance of the resources within the South African context.
Momresp: A Bayesian Model for Multi-Annotator Document Labeling
Authors: Paul Felt, Robbie Haertel, Eric Ringger, Kevin Seppi
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Document Classification

Data annotation in modern practice often involves multiple, imperfect human annotators. Multiple annotations can be used to infer estimates of the ground-truth labels and to estimate individual annotator error characteristics (or reliability). We introduce MomResp, a model that incorporates information from both natural data clusters as well as annotations from multiple annotators to infer ground-truth labels and annotator reliability for the document classification task. We implement this model and show dramatic improvements over majority vote in situations where both annotations are scarce and annotation quality is low as well as in situations where annotators disagree consistently. Because MomResp predictions are subject to label switching, we introduce a solution that finds nearly optimal predicted class reassignments in a variety of settings using only information available to the model at inference time. Although MomResp does not perform well in annotation-rich situations, we show evidence suggesting how this shortcoming may be overcome in future work.
Towards Automatic Detection of Narrative Structure
Authors: Jessica Ouyang, Kathy Mckeown
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

We present novel computational experiments using William Labov{'}s theory of narrative analysis. We describe his six elements of narrative structure and construct a new corpus based on his most recent work on narrative. Using this corpus, we explore the correspondence between LabovÂ’s elements of narrative structure and the implicit discourse relations of the Penn Discourse Treebank, and we construct a mapping between the elements of narrative structure and the discourse relation classes of the PDTB. We present first experiments on detecting Complicating Actions, the most common of the elements of narrative structure, achieving an f-score of 71.55. We compare the contributions of features derived from narrative analysis, such as the length of clauses and the tenses of main verbs, with those of features drawn from work on detecting implicit discourse relations. Finally, we suggest directions for future research on narrative structure, such as applications in assessing text quality and in narrative generation.
OpenLogos Semantico-Syntactic Knowledge-Rich Bilingual Dictionaries
Authors: Anabela Barreiro, Fern Batista, o, Ricardo Ribeiro, Helena Moniz, Isabel Trancoso
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Machine Translation, Translation

This paper presents 3 sets of OpenLogos resources, namely the English-German, the English-French, and the English-Italian bilingual dictionaries. In addition to the usual information on part-of-speech, gender, and number for nouns, offered by most dictionaries currently available, OpenLogos bilingual dictionaries have some distinctive features that make them unique: they contain cross-language morphological information (inflectional and derivational), semantico-syntactic knowledge, indication of the head word in multiword units, information about whether a source word corresponds to an homograph, information about verb auxiliaries, alternate words (i.e., predicate or process nouns), causatives, reflexivity, verb aspect, among others. The focal point of the paper will be the semantico-syntactic knowledge that is important for disambiguation and translation precision. The resources are publicly available at the METANET platform for free use by the research community.
Using Large Biomedical Databases as Gold Annotations for Automatic Relation Extraction
Authors: Tilia Ellendorff, Fabio Rinaldi, Simon Clematide
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Document Classification, Entity Extraction using GAN, Relation, Relation Extraction

We show how to use large biomedical databases in order to obtain a gold standard for training a machine learning system over a corpus of biomedical text. As an example we use the Comparative Toxicogenomics Database (CTD) and describe by means of a short case study how the obtained data can be applied. We explain how we exploit the structure of the database for compiling training material and a testset. Using a Naive Bayes document classification approach based on words, stem bigrams and MeSH descriptors we achieve a macro-average F-score of 61{\%} on a subset of 8 action terms. This outperforms a baseline system based on a lookup of stemmed keywords by more than 20{\%}. Furthermore, we present directions of future work, taking the described system as a vantage point. Future work will be aiming towards a weakly supervised system capable of discovering complete biomedical interactions and events.
Crowdsourcing for the identification of event nominals: an experiment
Authors: Rachele Sprugnoli, Aless Lenci, ro
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Question Answering

This paper presents the design and results of a crowdsourcing experiment on the recognition of Italian event nominals. The aim of the experiment was to assess the feasibility of crowdsourcing methods for a complex semantic task such as distinguishing the eventive interpretation of polysemous nominals taking into consideration various types of syntagmatic cues. Details on the theoretical background and on the experiment set up are provided together with the final results in terms of accuracy and inter-annotator agreement. These results are compared with the ones obtained by expert annotators on the same task. The low values in accuracy and FleissÂ’ kappa of the crowdsourcing experiment demonstrate that crowdsourcing is not always optimal for complex linguistic tasks. On the other hand, the use of non-expert contributors allows to understand what are the most ambiguous patterns of polysemy and the most useful syntagmatic cues to be used to identify the eventive reading of nominals.
Automatic Refinement of Syntactic Categories in Chinese Word Structures
Authors: Jianqiang Ma
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Chinese Word Segmentation, POS, POS Tagging, Segmentation

Annotated word structures are useful for various Chinese NLP tasks, such as word segmentation, POS tagging and syntactic parsing. Chinese word structures are often represented by binary trees, the nodes of which are labeled with syntactic categories, due to the syntactic nature of Chinese word formation. It is desirable to refine the annotation by labeling nodes of word structure trees with more proper syntactic categories so that the combinatorial properties in the word formation process are better captured. This can lead to improved performances on the tasks that exploit word structure annotations. We propose syntactically inspired algorithms to automatically induce syntactic categories of word structure trees using POS tagged corpus and branching in existing Chinese word structure trees. We evaluate the quality of our annotation by comparing the performances of models based on our annotation and another publicly available annotation, respectively. The results on two variations of Chinese word segmentation task show that using our annotation can lead to significant performance improvements.
Incorporating Alternate Translations into English Translation Treebank
Authors: Ann Bies, Justin Mott, Seth Kulick, Jennifer Garland, Colin Warner
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Machine Translation, Translation

New annotation guidelines and new processing methods were developed to accommodate English treebank annotation of a parallel English/Chinese corpus of web data that includes alternate English translations (one fluent, one literal) of expressions that are idiomatic in the Chinese source. In previous machine translation programs, alternate translations of idiomatic expressions had been present in untreebanked data only, but due to the high frequency of such expressions in informal genres such as discussion forums, machine translation system developers requested that alternatives be added to the treebanked data as well. In consultation with machine translation researchers, we chose a pragmatic approach of syntactically annotating only the fluent translation, while retaining the alternate literal translation as a segregated node in the tree. Since the literal translation alternates are often incompatible with English syntax, this approach allows us to create fluent trees without losing information. This resource is expected to support machine translation efforts, and the flexibility provided by the alternate translations is an enhancement to the treebank for this purpose.
Zmorge: A German Morphological Lexicon Extracted from Wiktionary
Authors: Rico Sennrich, Beat Kunz
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Machine Translation, Morphological Analysis

We describe a method to automatically extract a German lexicon from Wiktionary that is compatible with the finite-state morphological grammar SMOR. The main advantage of the resulting lexicon over existing lexica for SMOR is that it is open and permissively licensed. A recall-oriented evaluation shows that a morphological analyser built with our lexicon has comparable coverage compared to existing lexica, and continues to improve as Wiktionary grows. We also describe modifications to the SMOR grammar that result in a more conventional lemmatisation of words.
Tharwa: A Large Scale Dialectal Arabic - Standard Arabic - English Lexicon
Authors: Mona Diab, Mohamed Al-Badrashiny, Maryam Aminian, Mohammed Attia, Heba Elfardy, Nizar Habash, Abdelati Hawwari, Wael Salloum, Pradeep Dasigi, Esk, Ramy er
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: POS

We introduce an electronic three-way lexicon, Tharwa, comprising Dialectal Arabic, Modern Standard Arabic and English correspondents. The paper focuses on Egyptian Arabic as the first pilot dialect for the resource, with plans to expand to other dialects of Arabic in later phases of the project. We describe TharwaÂ’s creation process and report on its current status. The lexical entries are augmented with various elements of linguistic information such as POS, gender, rationality, number, and root and pattern information. The lexicon is based on a compilation of information from both monolingual and bilingual existing resources such as paper dictionaries and electronic, corpus-based dictionaries. Multiple levels of quality checks are performed on the output of each step in the creation process. The importance of this lexicon lies in the fact that it is the first resource of its kind bridging multiple variants of Arabic with English. Furthermore, it is a wide coverage lexical resource containing over 73,000 Egyptian entries. Tharwa is publicly available. We believe it will have a significant impact on both Theoretical Linguistics as well as Computational Linguistics research.
A SKOS-based Schema for TEI encoded Dictionaries at ICLTT
Authors: Thierry Declerck, Karlheinz M{\"o}rth, W, Eveline l-Vogt
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

At our institutes we are working with quite some dictionaries and lexical resources in the field of less-resourced language data, like dialects and historical languages. We are aiming at publishing those lexical data in the Linked Open Data framework in order to link them with available data sets for highly-resourced languages and elevating them thus to the same digital dignity the mainstream languages have gained. In this paper we concentrate on two TEI encoded variants of the Arabic language and propose a mapping of this TEI encoded data onto SKOS, showing how the lexical entries of the two dialectal dictionaries can be linked to other language resources available in the Linked Open Data cloud.
Semantic Technologies for Querying Linguistic Annotations: An Experiment Focusing on Graph-Structured Data
Authors: Milen Kouylekov, Stephan Oepen
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Form, Sentence

With growing interest in the creation and search of linguistic annotations that form general graphs (in contrast to formally simpler, rooted trees), there also is an increased need for infrastructures that support the exploration of such representations, for example logical-form meaning representations or semantic dependency graphs. In this work, we heavily lean on semantic technologies and in particular the data model of the Resource Description Framework (RDF) to represent, store, and efficiently query very large collections of text annotated with graph-structured representations of sentence meaning.
Eliciting and Annotating Uncertainty in Spoken Language
Authors: Heather Pon-Barry, Stuart Shieber, Nicholas Longenbaugh
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

A major challenge in the field of automatic recognition of emotion and affect in speech is the subjective nature of affect labels. The most common approach to acquiring affect labels is to ask a panel of listeners to rate a corpus of spoken utterances along one or more dimensions of interest. For applications ranging from educational technology to voice search to dictation, a speaker{'}s level of certainty is a primary dimension of interest. In such applications, we would like to know the speaker{'}s actual level of certainty, but past research has only revealed listeners{'} perception of the speaker{'}s level of certainty. In this paper, we present a method for eliciting spoken utterances using stimuli that we design such that they have a quantitative, crowdsourced legibility score. While we cannot control a speaker{'}s actual internal level of certainty, the use of these stimuli provides a better estimate of internal certainty compared to existing speech corpora. The Harvard Uncertainty Speech Corpus, containing speech data, certainty annotations, and prosodic features, is made available to the research community.
A hierarchical taxonomy for classifying hardness of inference tasks
Authors: Martin Gleize, Brigitte Grau
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: coreference-resolution, Coreference Resolution, Information Retrieval, Natural Language Inference, Question Answering, Reading Comprehension, Relation Extraction, Text Summarization

Exhibiting inferential capabilities is one of the major goals of many modern Natural Language Processing systems. However, if attempts have been made to define what textual inferences are, few seek to classify inference phenomena by difficulty. In this paper we propose a hierarchical taxonomy for inferences, relatively to their hardness, and with corpus annotation and system design and evaluation in mind. Indeed, a fine-grained assessment of the difficulty of a task allows us to design more appropriate systems and to evaluate them only on what they are designed to handle. Each of seven classes is described and provided with examples from different tasks like question answering, textual entailment and coreference resolution. We then test the classes of our hierarchy on the specific task of question answering. Our annotation process of the testing data at the QA4MRE 2013 evaluation campaign reveals that it is possible to quantify the contrasts in types of difficulty on datasets of the same task.
Automatic Methods for the Extension of a Bilingual Dictionary using Comparable Corpora
Authors: Michael Rosner, Kurt Sultana
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

Bilingual dictionaries define word equivalents from one language to another, thus acting as an important bridge between languages. No bilingual dictionary is complete since languages are in a constant state of change. Additionally, dictionaries are unlikely to achieve complete coverage of all language terms. This paper investigates methods for extending dictionaries using non-aligned corpora, by finding translations through context similarity. Most methods compute word contexts from general corpora. This can lead to errors due to data sparsity. We investigate the hypothesis that this problem can be addressed by carefully choosing smaller corpora in which domain-specific terms are more predominant. We also introduce the notion of efficiency which we consider as the effort required to obtain a set of dictionary entries from a given corpus
A Method for Building Burst-Annotated Co-Occurrence Networks for Analysing Trends in Textual Data
Authors: Yutaka Mitsuishi, V{\'\i}t Nov{\'a}{\v{c}}ek, V, Pierre-Yves enbussche
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Articles

This paper presents a method for constructing a specific type of language resources that are conveniently applicable to analysis of trending topics in time-annotated textual data. More specifically, the method consists of building a co-occurrence network from the on-line content (such as New York Times articles) that conform to key words selected by users (e.g., {`}Arab Spring{'}). Within the network, burstiness of the particular nodes (key words) and edges (co-occurrence relations) is computed. A service deployed on the network then facilitates exploration of the underlying text in order to identify trending topics. Using the graph structure of the network, one can assess also a broader context of the trending events. To limit the information overload of users, we filter the edges and nodes displayed by their burstiness scores to show only the presumably more important ones. The paper gives details on the proposed method, including a step-by-step walk through with plenty of real data examples. We report on a specific application of our method to the topic of `Arab Spring{'} and make the language resource applied therein publicly available for experimentation. Last but not least, we outline a methodology of an ongoing evaluation of our method.
Casa de la Lh\'engua: a set of language resources and natural language processing tools for Mirandese
Authors: Jos{\'e} Pedro Ferreira, Cristiano Chesi, Daan Baldewijns, Fern Pinto, o Miguel, Margarita Correia, Daniela Braga, Hyongsil Cho, Amadeu Ferreira, Miguel Dias
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: POS, POS Tagging, Speech Synthesis

This paper describes the efforts for the construction of Language Resources and NLP tools for Mirandese, a minority language spoken in North-eastern Portugal, now available on a community-led portal, Casa de la Lh{\'e}ngua. The resources were developed in the context of a collaborative citizenship project led by Microsoft, in the context of the creation of the first TTS system for Mirandese. Development efforts encompassed the compilation of a corpus with over 1M tokens, the construction of a GTP system, syllable-division, inflection and a Part-of-Speech (POS) tagger modules, leading to the creation of an inflected lexicon of about 200.000 entries with phonetic transcription, detailed POS tagging, syllable division, and stress mark-up. Alongside these tasks, which were made easier through the adaptation and reuse of existing tools for closely related languages, a casting for voice talents among the speaking community was conducted and the first speech database for speech synthesis was recorded for Mirandese. These resources were combined to fulfil the requirements of a well-tested statistical parameter synthesis model, leading to an intelligible voice font. These language resources are available freely at Casa de la Lh{\'e}ngua, aiming at promoting the development of real-life applications and fostering linguistic research on Mirandese.
Untrained Forced Alignment of Transcriptions and Audio for Language Documentation Corpora using WebMAUS
Authors: Jan Strunk, Florian Schiel, Frank Seifart
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Grapheme-to-Phoneme Conversion

Language documentation projects supported by recent funding intiatives have created a large number of multimedia corpora of typologically diverse languages. Most of these corpora provide a manual alignment of transcription and audio data at the level of larger units, such as sentences or intonation units. Their usefulness both for corpus-linguistic and psycholinguistic research and for the development of tools and teaching materials could, however, be increased by achieving a more fine-grained alignment of transcription and audio at the word or even phoneme level. Since most language documentation corpora contain data on small languages, there usually do not exist any speech recognizers or acoustic models specifically trained on these languages. We therefore investigate the feasibility of untrained forced alignment for such corpora. We report on an evaluation of the tool (Web)MAUS (Kisler, 2012) on several language documentation corpora and discuss practical issues in the application of forced alignment. Our evaluation shows that (Web)MAUS with its existing acoustic models combined with simple grapheme-to-phoneme conversion can be successfully used for word-level forced alignment of a diverse set of languages without additional training, especially if a manual prealignment of larger annotation units is already avaible.