pwc-archive/papers-with-abstracts huggingface dataset:
The coding and annotation of multimodal dialogue acts
Authors: Volha Petukhova, Harry Bunt
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Speech Recognition

Recent years have witnessed a growing interest in annotating linguistic data at the semantic level, including the annotation of dialogue corpus data. The annotation scheme developed as international standard for dialogue act annotation ISO 24617-2 is based on the DIT++ scheme (Bunt, 2006; 2009) which combines the multidimensional DIT scheme (Bunt, 1994) with concepts from DAMSL (Allen and Core , 1997) and various other schemes. This scheme is designed in a such way that it can be applied not only to spoken dialogue, as is the case for most of the previously defined dialogue annotation schemes, but also to multimodal dialogue. This paper describes how the ISO 24617-2 annotation scheme can be used, together with the DIT++ method of Â‘multidimensional segmentation', to annotate nonverbal and multimodal dialogue behaviour. We analyse the fundamental distinction between (a) the coding of surface features; (b) form-related semantic classification; and (c) semantic annotation in terms of dialogue acts, supported by experimental studies of (a) and (b). We discuss examples of specification languages for representing the results of each of these activities, show how dialogue act annotations can be attached to XML representations of functional segments of multimodal data.
Learning Categories and their Instances by Contextual Features
Authors: Antje Schlaf, Robert Remus
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Named Entity Recognition (NER)

We present a 3-step framework that learns categories and their instances from natural language text based on given training examples. Step 1 extracts contexts of training examples as rules describing this category from text, considering part of speech, capitalization and category membership as features. Step 2 selects high quality rules using two consequent filters. The first filter is based on the number of rule occurrences, the second filter takes two non-independent characteristics into account: a rule's precision and the amount of instances it acquires. Our framework adapts the filter's threshold values to the respective category and the textual genre by automatically evaluating rule sets resulting from different filter settings and selecting the best performing rule set accordingly. Step 3 then identifies new instances of a category using the filtered rules applied within a previously proposed algorithm. We inspect the rule filters' impact on rule set quality and evaluate our framework by learning first names, last names, professions and cities from a hitherto unexplored textual genre -- search engine result snippets -- and achieve high precision on average.
Textual Characteristics for Language Engineering
Authors: Mathias Bank, Robert Remus, Martin Schierle
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Information Retrieval, Retrieval

Language statistics are widely used to characterize and better understand language. In parallel, the amount of text mining and information retrieval methods grew rapidly within the last decades, with many algorithms evaluated on standardized corpora, often drawn from newspapers. However, up to now there were almost no attempts to link the areas of natural language processing and language statistics in order to properly characterize those evaluation corpora, and to help others to pick the most appropriate algorithms for their particular corpus. We believe no results in the field of natural language processing should be published without quantitatively describing the used corpora. Only then the real value of proposed methods can be determined and the transferability to corpora originating from different genres or domains can be estimated. We lay ground for a language engineering process by gathering and defining a set of textual characteristics we consider valuable with respect to building natural language processing systems. We carry out a case study for the analysis of automotive repair orders and explicitly call upon the scientific community to provide feedback and help to establish a good practice of corpus-aware evaluations.
A Survey of Text Mining Architectures and the UIMA Standard
Authors: Mathias Bank, Martin Schierle
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Information Retrieval, Management, Survey

With the rising amount of digitally available text, the need for efficient processing algorithms is growing fast. Although a lot of libraries are commonly available, their modularity and interchangeability is very limited, therefore forcing a lot of reimplementations and modifications not only in research areas but also in real world application scenarios. In recent years, different NLP frameworks have been proposed to provide an efficient, robust and convenient architecture for information processing tasks. This paper will present an overview over the most common approaches with their advantages and shortcomings, and will discuss them with respect to the first standardized architecture - the Unstructured Information Management Architecture (UIMA).
A Rule-based Morphological Analyzer for Murrinh-Patha
Authors: Melanie Seiss
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5

Resource development mainly focuses on well-described languages with a large amount of speakers. However, smaller languages may also profit from language resources which can then be used in applications such as electronic dictionaries or computer-assisted language learning materials. The development of resources for such languages may face various challenges. Often, not enough data is available for a successful statistical approach and the methods developed for other languages may not be suitable for this specific language. This paper presents a morphological analyzer for Murrinh-Patha, a polysynthetic language spoken in the Northern Territory of Australia. While nouns in Murrinh-Patha only show minimal inflection, verbs in this language are very complex. The complexity makes it very difficult if not impossible to handle data in Murrinh-Patha with statistical, surface-oriented methods. I therefore present a rule-based morphological analyzer built in XFST and LEXC (Beesley and Karttunen, 2003) which can handle the inflection on nouns and adjectives as well as the complexities of the Murrinh-Patha verb.
DutchSemCor: Targeting the ideal sense-tagged corpus
Authors: Piek Vossen, Attila G{\"o}r{\"o}g, Rub{\'e}n Izquierdo, Antal Van den Bosch
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Active Learning, Word Sense Disambiguation

Word Sense Disambiguation (WSD) systems require large sense-tagged corpora along with lexical databases to reach satisfactory results. The number of English language resources for developed WSD increased in the past years while most other languages are still under-resourced. The situation is no different for Dutch. In order to overcome this data bottleneck, the DutchSemCor project will deliver a Dutch corpus that is sense-tagged with senses from the Cornetto lexical database. In this paper, we discuss the different conflicting requirements for a sense-tagged corpus and our strategies to fulfill them. We report on a first series of experiments to sup- port our semi-automatic approach to build the corpus.
Extracting Directional and Comparable Corpora from a Multilingual Corpus for Translation Studies
Authors: Bruno Cartoni, Thomas Meyer
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Machine Translation, Translation

Translation studies rely more and more on corpus data to examine specificities of translated texts, that can be translated from different original languages and compared to original texts. In parallel, more and more multilingual corpora are becoming available for various natural language processing tasks. This paper questions the use of these multilingual corpora in translation studies and shows the methodological steps needed in order to obtain more reliably comparable sub-corpora that consist of original and directly translated text only. Various experiments are presented that show the advantage of directional sub-corpora.
QurSim: A corpus for evaluation of relatedness in short texts
Authors: Abdul-Baquee Sharaf, Eric Atwell
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Information Retrieval, Machine Translation, Semantic Textual Similarity, Text Clustering, Text Summarization, Translation

This paper presents a large corpus created from the original Quranic text, where semantically similar or related verses are linked together. This corpus will be a valuable evaluation resource for computational linguists investigating similarity and relatedness in short texts. Furthermore, this dataset can be used for evaluation of paraphrase analysis and machine translation tasks. Our dataset is characterised by: (1) superior quality of relatedness assignment; as we have incorporated relations marked by well-known domain experts, this dataset could thus be considered a gold standard corpus for various evaluation tasks, (2) the size of our dataset; over 7,600 pairs of related verses are collected from scholarly sources with several levels of degree of relatedness. This dataset could be extended to over 13,500 pairs of related verses observing the commutative property of strongly related pairs. This dataset was incorporated into online query pages where users can visualize for a given verse a network of all directly and indirectly related verses. Empirical experiments showed that only 33{\%} of related pairs shared root words, emphasising the need to go beyond common lexical matching methods, and incorporate -in addition- semantic, domain knowledge, and other corpus-based approaches.
Eye Tracking as a Tool for Machine Translation Error Analysis
Authors: Sara Stymne, Henrik Danielsson, Sofia Bremin, Hongzhan Hu, Johanna Karlsson, Anna Prytz Lillkull, Martin Wester
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Machine Translation, Reading Comprehension, Translation

We present a preliminary study where we use eye tracking as a complement to machine translation (MT) error analysis, the task of identifying and classifying MT errors. We performed a user study where subjects read short texts translated by three MT systems and one human translation, while we gathered eye tracking data. The subjects were also asked comprehension questions about the text, and were asked to estimate the text quality. We found that there are a longer gaze time and a higher number of fixations on MT errors, than on correct parts. There are also differences in the gaze time of different error types, with word order errors having the longest gaze time. We also found correlations between eye tracking data and human estimates of text quality. Overall our study shows that eye tracking can give complementary information to error analysis, such as aiding in ranking error types for seriousness.
Automatic annotation of head velocity and acceleration in Anvil
Authors: Bart Jongejan
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Face Detection

We describe an automatic face tracker plugin for the ANVIL annotation tool. The face tracker produces data for velocity and for acceleration in two dimensions. We compare annotations generated by the face tracking algorithm with independently made manual annotations for head movements. The annotations are a useful supplement to manual annotations and may help human annotators to quickly and reliably determine onset of head movements and to suggest which kind of head movement is taking place.
Representing the Translation Relation in a Bilingual Wordnet
Authors: Jyrki Niemi, Krister Lind{\'e}n
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Relation, Translation

This paper describes representing translations in the Finnish wordnet, FinnWordNet (FiWN), and constructing the FiWN database. FiWN was created by translating all the word senses of the Princeton WordNet (PWN) into Finnish and by joining the translations with the semantic and lexical relations of PWN extracted into a relational (database) format. The approach naturally resulted in a translation relation between PWN and FiWN. Unlike many other multilingual wordnets, the translation relation in FiWN is not primarily on the synset level, but on the level of an individual word sense, which allows more precise translation correspondences. This can easily be projected into a synset-level translation relation, used for linking with other wordnets, for example, via Core WordNet. Synset-level translations are also used as a default in the absence of word-sense translations. The FiWN data in the relational database can be converted to other formats. In the PWN database format, translations are attached to source-language words, allowing the implementation of a Web search interface also working as a bilingual dictionary. Another representation encodes the translation relation as a finite-state transducer.
Can Statistical Post-Editing with a Small Parallel Corpus Save a Weak MT Engine?
Authors: Marianna J. Martindale
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Machine Translation

Statistical post-editing has been shown in several studies to increase BLEU score for rule-based MT systems. However, previous studies have relied solely on BLEU and have not conducted further study to determine whether those gains indicated an increase in quality or in score alone. In this work we conduct a human evaluation of statistical post-edited output from a weak rule-based MT system, comparing the results with the output of the original rule-based system and a phrase-based statistical MT system trained on the same data. We show that for this weak rule-based system, despite significant BLEU score increases, human evaluators prefer the output of the original system. While this is not a generally conclusive condemnation of statistical post-editing, this result does cast doubt on the efficacy of statistical post-editing for weak MT systems and on the reliability of BLEU score for comparison between weak rule-based and hybrid systems built from them.
The Impact of Automatic Morphological Analysis \& Disambiguation on Dependency Parsing of Turkish
Authors: G{\"u}l{\c{s}}en Eryi{\u{g}}it
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Dependency Parsing, Information Retrieval, Morphological Analysis

The studies on dependency parsing of Turkish so far gave their results on the Turkish Dependency Treebank. This treebank consists of sentences where gold standard part-of-speech tags are manually assigned to each word and the words forming multi word expressions are also manually determined and combined into single units. For the first time, we investigate the results of parsing Turkish sentences from scratch and observe the accuracy drop at the end of processing raw data. We test one state-of-the art morphological analyzer together with two different morphological disambiguators. We both show separately the accuracy drop due to the automatic morphological processing and to the lack of multi word unit extraction. With this purpose, we use and present a new version of the Turkish Treebank where we detached the multi word expressions (MWEs) into multiple tokens and manually annotated the missing part-of-speech tags of these new tokens.
Detecting Reduplication in Videos of American Sign Language
Authors: Zoya Gavrilov, Stan Sclaroff, Carol Neidle, Sven Dickinson
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5

A framework is proposed for the detection of reduplication in digital videos of American Sign Language (ASL). In ASL, reduplication is used for a variety of linguistic purposes, including overt marking of plurality on nouns, aspectual inflection on verbs, and nominalization of verbal forms. Reduplication involves the repetition, often partial, of the articulation of a sign. In this paper, the apriori algorithm for mining frequent patterns in data streams is adapted for finding reduplication in videos of ASL. The proposed algorithm can account for varying weights on items in the apriori algorithm's input sequence. In addition, the apriori algorithm is extended to allow for inexact matching of similar hand motion subsequences and to provide robustness to noise. The formulation is evaluated on 105 lexical signs produced by two native signers. To demonstrate the formulation, overall hand motion direction and magnitude are considered; however, the formulation should be amenable to combining these features with others, such as hand shape, orientation, and place of articulation.
Building a multilingual parallel corpus for human users
Authors: Alex Rosen, R, Martin Vav{\v{r}}{\'\i}n
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Lemmatization

We present the architecture and the current state of InterCorp, a multilingual parallel corpus centered around Czech, intended primarily for human users and consisting of written texts with a focus on fiction. Following an outline of its recent development and a comparison with some other multilingual parallel corpora we give an overview of the data collection procedure that covers text selection criteria, data format, conversion, alignment, lemmatization and tagging. Finally, we show a sample query using the web-based search interface and discuss challenges and prospects of the project.
EmpaTweet: Annotating and Detecting Emotions on Twitter
Authors: Kirk Roberts, Michael A. Roach, Joseph Johnson, Josh Guthrie, S Harabagiu, a M.
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Opinion Mining, Sentiment Analysis

The rise of micro-blogging in recent years has resulted in significant access to emotion-laden text. Unlike emotion expressed in other textual sources (e.g., blogs, quotes in newswire, email, product reviews, or even clinical text), micro-blogs differ by (1) placing a strict limit on length, resulting radically in new forms of emotional expression, and (2) encouraging users to express their daily thoughts in real-time, often resulting in far more emotion statements than might normally occur. In this paper, we introduce a corpus collected from Twitter with annotated micro-blog posts (or tweets) annotated at the tweet-level with seven emotions: ANGER, DISGUST, FEAR, JOY, LOVE, SADNESS, and SURPRISE. We analyze how emotions are distributed in the data we annotated and compare it to the distributions in other emotion-annotated corpora. We also used the annotated corpus to train a classifier that automatically discovers the emotions in tweets. In addition, we present an analysis of the linguistic style used for expressing emotions our corpus. We hope that these observations will lead to the design of novel emotion detection techniques that account for linguistic style and psycholinguistic theories.
EVALIEX --- A Proposal for an Extended Evaluation Methodology for Information Extraction Systems
Authors: Christina Feilmayr, Birgit Pr{\"o}ll, Elisabeth Linsmayr
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Information Retrieval

Assessing the correctness of extracted data requires performance evaluation, which is accomplished by calculating quality metrics. The evaluation process must cope with the challenges posed by information extraction and natural language processing. In the previous work most of the existing methodologies have been shown that they support only traditional scoring metrics. Our research work addresses requirements, which arose during the development of three productive rule-based information extraction systems. The main contribution is twofold: First, we developed a proposal for an evaluation methodology that provides the flexibility and effectiveness needed for comprehensive performance measurement. The proposal extends state-of-the-art scoring metrics by measuring string and semantic similarities and by parameterization of metric scoring, and thus simulating with human judgment. Second, we implemented an IE evaluation tool named EVALIEX, which integrates these measurement concepts and provides an efficient user interface that supports evaluation control and the visualization of IE results. To guarantee domain independence, the tool additionally provides a Generic Mapper for XML Instances (GeMap) that maps domain-dependent XML files containing IE results to generic ones. Compared to other tools, it provides more flexible testing and better visualization of extraction results for the comparison of different (versions of) information extraction systems.
BiBiKit - A Bilingual Bimodal Reading and Writing Tool for Sign Language Users
Authors: Nedelina Ivanova, Olle Eriksen
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Navigate, Part-Of-Speech Tagging, Sentence

Sign language is used by many people who were born deaf or who became deaf early in life use as their first and/or preferred language. There is no writing system for sign languages; texts are signed on video. As a consequence, texts in sign language are hard to navigate, search and annotate. The BiBiKit project is an easy to use authoring kit which is being developed and enables students, teachers, and virtually everyone to write and read bilingual bimodal texts and thereby creating electronic productions, which link text to sign language video. The main purpose of the project is to develop software that enables the user to link text to video, at the word, phrase and/or sentence level. The software will be developed for sign language and vice versa, but can be used to easily link text to any video: e.g. to add annotations, captions, or navigation points. The three guiding principles are: Software that is 1) stable, 2) easy to use, and 3) foolproof. A web based platform will be developed so the software is available whenever and wherever.
The BladeMistress Corpus: From Talk to Action in Virtual Worlds
Authors: Anton Leuski, Carsten Eickhoff, James Ganis, Victor Lavrenko
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Text Classification

Virtual Worlds (VW) are online environments where people come together to interact and perform various tasks. The chat transcripts of interactions in VWs pose unique opportunities and challenges for language analysis: Firstly, the language of the transcripts is very brief, informal, and task-oriented. Secondly, in addition to chat, a VW system records users' in-world activities. Such a record could allow us to analyze how the language of interactions is linked to the users actions. For example, we can make the language analysis of the users dialogues more effective by taking into account the context of the corresponding action or we can predict or detect users actions by analyzing the content of conversations. Thirdly, a joined analysis of both the language and the actions would empower us to build effective modes of the users and their behavior. In this paper we present a corpus constructed from logs from an online multiplayer game BladeMistress. We describe the original logs, annotations that we created on the data, and summarize some of the experiments.