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  • × theme_ss:"Computerlinguistik"
  1. Maschinelle Sprachsynthese (1996) 0.00 
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    Content
    Besteht aus folgenden Einzelbeiträgen: HESS, W.: Neuere Entwicklungen der Sprachsynthese; MÖBIUS, B. u. R. SPROAT: Linguistische Analyse in einem multilingualen Sprachsynthese-System; KRÖGER, B.J.: Von Sprechbewegungen zum akustischen Signal: artikulatorische Sprachsynthese; PORTELE, T.: Künstliche Sprache aus dem Baukasten: datenbasierte Sprachsynthese; KOHLER, K.J.: Modellgesteuerte Prosodie-Erzeugung
  2. WordNet : an electronic lexical database (language, speech and communication) (1998) 0.00 
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    Abstract
    WordNet, an electronic lexical database, is considerd to be the most important resource available to researchers in computational linguistics, text analysis, and many related areas. English nouns, verbs, adjectives, and adverbs are organized into synonym sets, each representing one underlying lexicalized concept. Different relations link the synonym sets. The purpose of this volume is twofold. First, it discusses the design of WordNet and the theoretical motivation behind it. Second, it provides a survey of representative applications, including word sense identification, information retrieval, selectional preferences of verbs, and lexical chains
  3. Brenner, E.H.: Beyond Boolean : new approaches in information retrieval; the quest for intuitive online search systems past, present & future (1995) 0.00 
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  4. Ruiz, M.E.; Srinivasan, P.: Combining machine learning and hierarchical indexing structures for text categorization (2001) 0.00 
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    Source
    Advances in classification research, vol.10: proceedings of the 10th ASIS SIG/CR Classification Research Workshop. Ed.: Albrechtsen, H. u. J.E. Mai
  5. Kuo, J.-S.; Li, H.; Yang, Y.-K.: Active learning for constructing transliteration lexicons from the Web (2008) 0.00 
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    Abstract
    This article presents an adaptive learning framework for Phonetic Similarity Modeling (PSM) that supports the automatic construction of transliteration lexicons. The learning algorithm starts with minimum prior knowledge about machine transliteration and acquires knowledge iteratively from the Web. We study the unsupervised learning and the active learning strategies that minimize human supervision in terms of data labeling. The learning process refines the PSM and constructs a transliteration lexicon at the same time. We evaluate the proposed PSM and its learning algorithm through a series of systematic experiments, which show that the proposed framework is reliably effective on two independent databases.
  6. Shen, M.; Liu, D.-R.; Huang, Y.-S.: Extracting semantic relations to enrich domain ontologies (2012) 0.00 
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    Abstract
    Domain ontologies facilitate the organization, sharing and reuse of domain knowledge, and enable various vertical domain applications to operate successfully. Most methods for automatically constructing ontologies focus on taxonomic relations, such as is-kind-of and is- part-of relations. However, much of the domain-specific semantics is ignored. This work proposes a semi-unsupervised approach for extracting semantic relations from domain-specific text documents. The approach effectively utilizes text mining and existing taxonomic relations in domain ontologies to discover candidate keywords that can represent semantic relations. A preliminary experiment on the natural science domain (Taiwan K9 education) indicates that the proposed method yields valuable recommendations. This work enriches domain ontologies by adding distilled semantics.
  7. Dias, G.: Multiword unit hybrid extraction (o.J.) 0.00 
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    Abstract
    This paper describes an original hybrid system that extracts multiword unit candidates from part-of-speech tagged corpora. While classical hybrid systems manually define local part-of-speech patterns that lead to the identification of well-known multiword units (mainly compound nouns), our solution automatically identifies relevant syntactical patterns from the corpus. Word statistics are then combined with the endogenously acquired linguistic information in order to extract the most relevant sequences of words. As a result, (1) human intervention is avoided providing total flexibility of use of the system and (2) different multiword units like phrasal verbs, adverbial locutions and prepositional locutions may be identified. The system has been tested on the Brown Corpus leading to encouraging results
  8. Toutanova, K.; Klein, D.; Manning, C.D.; Singer, Y.: Feature-rich Part-of-Speech Tagging with a cyclic dependency network (2003) 0.00 
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    Abstract
    We present a new part-of-speech tagger that demonstrates the following ideas: (i) explicit use of both preceding and following tag contexts via a dependency network representation, (ii) broad use of lexical features, including jointly conditioning on multiple consecutive words, (iii) effective use of priors in conditional loglinear models, and (iv) fine-grained modeling of unknown word features. Using these ideas together, the resulting tagger gives a 97.24%accuracy on the Penn TreebankWSJ, an error reduction of 4.4% on the best previous single automatically learned tagging result.
  9. Colace, F.; Santo, M. De; Greco, L.; Napoletano, P.: Weighted word pairs for query expansion (2015) 0.00 
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    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
  10. Rayson, P.; Piao, S.; Sharoff, S.; Evert, S.; Moiron, B.V.: Multiword expressions : hard going or plain sailing? (2015) 0.00 
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    Abstract
    Over the past two decades or so, Multi-Word Expressions (MWEs; also called Multi-word Units) have been an increasingly important concern for Computational Linguistics and Natural Language Processing (NLP). The term MWE has been used to refer to various types of linguistic units and expressions, including idioms, noun compounds, phrasal verbs, light verbs and other habitual collocations. However, while there is no universally agreed definition for MWE as yet, most researchers use the term to refer to those frequently occurring phrasal units which are subject to certain level of semantic opaqueness, or non-compositionality. Non-compositional MWEs pose tough challenges for automatic analysis because their interpretation cannot be achieved by directly combining the semantics of their constituents, thereby causing the "pain in the neck of NLP".
  11. Sikkel, K.: Parsing schemata : a framework for specification and analysis of parsing algorithms (1996) 0.00 
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    Abstract
    Parsing, the syntactic analysis of language, has been studied extensively in computer science and computational linguistics. Computer programs and natural languages share an underlying theory of formal languages and require efficient parsing algorithms. This introductions reviews the thory of parsing from a novel perspective, it provides a formalism to capture the essential traits of a parser that abstracts from the fine detail and allows a uniform description and comparison of a variety of parsers, including Earley, Tomita, LR, Left-Corner, and Head-Corner parsers. The emphasis is on context-free phrase structure grammar and how these parsers can be extended to unification formalisms. The book combines mathematical rigor with high readability and is suitable as a graduate course text
  12. Ferret, O.; Grau, B.; Hurault-Plantet, M.; Illouz, G.; Jacquemin, C.; Monceaux, L.; Robba, I.; Vilnat, A.: How NLP can improve question answering (2002) 0.00 
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    Abstract
    Answering open-domain factual questions requires Natural Language processing for refining document selection and answer identification. With our system QALC, we have participated in the Question Answering track of the TREC8, TREC9 and TREC10 evaluations. QALC performs an analysis of documents relying an multiword term searches and their linguistic variation both to minimize the number of documents selected and to provide additional clues when comparing question and sentence representations. This comparison process also makes use of the results of a syntactic parsing of the questions and Named Entity recognition functionalities. Answer extraction relies an the application of syntactic patterns chosen according to the kind of information that is sought, and categorized depending an the syntactic form of the question. These patterns allow QALC to handle nicely linguistic variations at the answer level.
  13. Atlam, E.-S.; Morita, K.; Fuketa, M.; Aoe, J.-i.: ¬A new method for selecting English field association terms of compound words and its knowledge representation (2002) 0.00 
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    Abstract
    This paper presents a strategy for building a morphological machine dictionary of English that infers meaning of derivations by considering morphological affixes and their semantic classification. Derivations are grouped into a frame that is accessible to semantic stem and knowledge base. This paper also proposes an efficient method for selecting compound Field Association (FA) terms from a large pool of single FA terms for some specialized fields. For single FA terms, five levels of association are defined and two ranks are defined, based on stability and inheritance. About 85% of redundant compound FA terms can be removed effectively by using levels and ranks proposed in this paper. Recall averages of 60-80% are achieved, depending on the type of text. The proposed methods are applied to 22,000 relationships between verbs and nouns extracted from the large tagged corpus.
  14. Radev, D.; Fan, W.; Qu, H.; Wu, H.; Grewal, A.: Probabilistic question answering on the Web (2005) 0.00 
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    Abstract
    Web-based search engines such as Google and NorthernLight return documents that are relevant to a user query, not answers to user questions. We have developed an architecture that augments existing search engines so that they support natural language question answering. The process entails five steps: query modulation, document retrieval, passage extraction, phrase extraction, and answer ranking. In this article, we describe some probabilistic approaches to the last three of these stages. We show how our techniques apply to a number of existing search engines, and we also present results contrasting three different methods for question answering. Our algorithm, probabilistic phrase reranking (PPR), uses proximity and question type features and achieves a total reciprocal document rank of .20 an the TREC8 corpus. Our techniques have been implemented as a Web-accessible system, called NSIR.
  15. Fautsch, C.; Savoy, J.: Algorithmic stemmers or morphological analysis? : an evaluation (2009) 0.00 
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    Abstract
    It is important in information retrieval (IR), information extraction, or classification tasks that morphologically related forms are conflated under the same stem (using stemmer) or lemma (using morphological analyzer). To achieve this for the English language, algorithmic stemming or various morphological analysis approaches have been suggested. Based on Cross-Language Evaluation Forum test collections containing 284 queries and various IR models, this article evaluates these word-normalization proposals. Stemming improves the mean average precision significantly by around 7% while performance differences are not significant when comparing various algorithmic stemmers or algorithmic stemmers and morphological analysis. Accounting for thesaurus class numbers during indexing does not modify overall retrieval performances. Finally, we demonstrate that including a stop word list, even one containing only around 10 terms, might significantly improve retrieval performance, depending on the IR model.
  16. Kim, S.; Ko, Y.; Oard, D.W.: Combining lexical and statistical translation evidence for cross-language information retrieval (2015) 0.00 
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    Abstract
    This article explores how best to use lexical and statistical translation evidence together for cross-language information retrieval (CLIR). Lexical translation evidence is assembled from Wikipedia and from a large machine-readable dictionary, statistical translation evidence is drawn from parallel corpora, and evidence from co-occurrence in the document language provides a basis for limiting the adverse effect of translation ambiguity. Coverage statistics for NII Testbeds and Community for Information Access Research (NTCIR) queries confirm that these resources have complementary strengths. Experiments with translation evidence from a small parallel corpus indicate that even rather rough estimates of translation probabilities can yield further improvements over a strong technique for translation weighting based on using Jensen-Shannon divergence as a term-association measure. Finally, a novel approach to posttranslation query expansion using a random walk over the Wikipedia concept link graph is shown to yield further improvements over alternative techniques for posttranslation query expansion. Evaluation results on the NTCIR-5 English-Korean test collection show statistically significant improvements over strong baselines.
  17. Doko, A.; Stula, , M.; Seric, L.: Improved sentence retrieval using local context and sentence length (2013) 0.00 
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    Abstract
    In this paper we propose improved variants of the sentence retrieval method TF-ISF (a TF-IDF or Term Frequency-Inverse Document Frequency variant for sentence retrieval). The improvement is achieved by using context consisting of neighboring sentences and at the same time promoting the retrieval of longer sentences. We thoroughly compare new modified TF-ISF methods to the TF-ISF baseline, to an earlier attempt to include context into TF-ISF named tfmix and to a language modeling based method that uses context and promoting retrieval of long sentences named 3MMPDS. Experimental results show that the TF-ISF method can be improved using local context. Results also show that the TF-ISF method can be improved by promoting the retrieval of longer sentences. Finally we show that the best results are achieved when combining both modifications. All new methods (TF-ISF variants) also show statistically significant better results than the other tested methods.
  18. Abu-Salem, H.; Al-Omari, M.; Evens, M.W.: Stemming methodologies over individual query words for an Arabic information retrieval system (1999) 0.00 
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    Abstract
    Stemming is one of the most important factors that affect the performance of information retrieval systems. This article investigates how to improve the performance of an Arabic information retrieval system by imposing the retrieval method over individual words of a query depending on the importance of the WORD, the STEM, or the ROOT of the query terms in the database. This method, called Mxed Stemming, computes term importance using a weighting scheme that use the Term Frequency (TF) and the Inverse Document Frequency (IDF), called TFxIDF. An extended version of the Arabic IRS system is designed, implemented, and evaluated to reduce the number of irrelevant documents retrieved. The results of the experiment suggest that the proposed method outperforms the Word index method using the TFxIDF weighting scheme. It also outperforms the Stem index method using the Binary weighting scheme but does not outperform the Stem index method using the TFxIDF weighting scheme, and again it outperforms the Root index method using the Binary weighting scheme but does not outperform the Root index method using the TFxIDF weighting scheme
  19. Savoy, J.: Text representation strategies : an example with the State of the union addresses (2016) 0.00 
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    Abstract
    Based on State of the Union addresses from 1790 to 2014 (225 speeches delivered by 42 presidents), this paper describes and evaluates different text representation strategies. To determine the most important words of a given text, the term frequencies (tf) or the tf?idf weighting scheme can be applied. Recently, latent Dirichlet allocation (LDA) has been proposed to define the topics included in a corpus. As another strategy, this study proposes to apply a vocabulary specificity measure (Z?score) to determine the most significantly overused word-types or short sequences of them. Our experiments show that the simple term frequency measure is not able to discriminate between specific terms associated with a document or a set of texts. Using the tf idf or LDA approach, the selection requires some arbitrary decisions. Based on the term-specific measure (Z?score), the term selection has a clear theoretical basis. Moreover, the most significant sentences for each presidency can be determined. As another facet, we can visualize the dynamic evolution of usage of some terms associated with their specificity measures. Finally, this technique can be employed to define the most important lexical leaders introducing terms overused by the k following presidencies.
  20. Zhai, X.: ChatGPT user experience: : implications for education (2022) 0.00 
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    Abstract
    ChatGPT, a general-purpose conversation chatbot released on November 30, 2022, by OpenAI, is expected to impact every aspect of society. However, the potential impacts of this NLP tool on education remain unknown. Such impact can be enormous as the capacity of ChatGPT may drive changes to educational learning goals, learning activities, and assessment and evaluation practices. This study was conducted by piloting ChatGPT to write an academic paper, titled Artificial Intelligence for Education (see Appendix A). The piloting result suggests that ChatGPT is able to help researchers write a paper that is coherent, (partially) accurate, informative, and systematic. The writing is extremely efficient (2-3 hours) and involves very limited professional knowledge from the author. Drawing upon the user experience, I reflect on the potential impacts of ChatGPT, as well as similar AI tools, on education. The paper concludes by suggesting adjusting learning goals-students should be able to use AI tools to conduct subject-domain tasks and education should focus on improving students' creativity and critical thinking rather than general skills. To accomplish the learning goals, researchers should design AI-involved learning tasks to engage students in solving real-world problems. ChatGPT also raises concerns that students may outsource assessment tasks. This paper concludes that new formats of assessments are needed to focus on creativity and critical thinking that AI cannot substitute.

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