3,164 research outputs found

    Acquiring Word-Meaning Mappings for Natural Language Interfaces

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    This paper focuses on a system, WOLFIE (WOrd Learning From Interpreted Examples), that acquires a semantic lexicon from a corpus of sentences paired with semantic representations. The lexicon learned consists of phrases paired with meaning representations. WOLFIE is part of an integrated system that learns to transform sentences into representations such as logical database queries. Experimental results are presented demonstrating WOLFIE's ability to learn useful lexicons for a database interface in four different natural languages. The usefulness of the lexicons learned by WOLFIE are compared to those acquired by a similar system, with results favorable to WOLFIE. A second set of experiments demonstrates WOLFIE's ability to scale to larger and more difficult, albeit artificially generated, corpora. In natural language acquisition, it is difficult to gather the annotated data needed for supervised learning; however, unannotated data is fairly plentiful. Active learning methods attempt to select for annotation and training only the most informative examples, and therefore are potentially very useful in natural language applications. However, most results to date for active learning have only considered standard classification tasks. To reduce annotation effort while maintaining accuracy, we apply active learning to semantic lexicons. We show that active learning can significantly reduce the number of annotated examples required to achieve a given level of performance

    Getting Past the Language Gap: Innovations in Machine Translation

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    In this chapter, we will be reviewing state of the art machine translation systems, and will discuss innovative methods for machine translation, highlighting the most promising techniques and applications. Machine translation (MT) has benefited from a revitalization in the last 10 years or so, after a period of relatively slow activity. In 2005 the field received a jumpstart when a powerful complete experimental package for building MT systems from scratch became freely available as a result of the unified efforts of the MOSES international consortium. Around the same time, hierarchical methods had been introduced by Chinese researchers, which allowed the introduction and use of syntactic information in translation modeling. Furthermore, the advances in the related field of computational linguistics, making off-the-shelf taggers and parsers readily available, helped give MT an additional boost. Yet there is still more progress to be made. For example, MT will be enhanced greatly when both syntax and semantics are on board: this still presents a major challenge though many advanced research groups are currently pursuing ways to meet this challenge head-on. The next generation of MT will consist of a collection of hybrid systems. It also augurs well for the mobile environment, as we look forward to more advanced and improved technologies that enable the working of Speech-To-Speech machine translation on hand-held devices, i.e. speech recognition and speech synthesis. We review all of these developments and point out in the final section some of the most promising research avenues for the future of MT

    Exploiting multi-word units in history-based probabilistic generation

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    We present a simple history-based model for sentence generation from LFG f-structures, which improves on the accuracy of previous models by breaking down PCFG independence assumptions so that more f-structure conditioning context is used in the prediction of grammar rule expansions. In addition, we present work on experiments with named entities and other multi-word units, showing a statistically significant improvement of generation accuracy. Tested on section 23 of the PennWall Street Journal Treebank, the techniques described in this paper improve BLEU scores from 66.52 to 68.82, and coverage from 98.18% to 99.96%

    Las Relaciones Semánticas Predicen la Desambiguación Estructural de las Unidades Terminológicas Poliléxicas con Tres Formantes

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    For English multiword terms (MWTs) of three or more constituents (e.g., sea level rise), a semantic analysis, based on linguistic and domain knowledge, is necessary to resolve the dependency between components. This structural disambiguation, often known as bracketing, involves the grouping of the dependent components so that the MWT is reduced to its basic form of modifier+head, as in [sea level] [rise]. Knowledge of these dependencies facilitates the comprehension of an MWT and its accurate translation into other languages. Moreover, the resolution of MWT bracketing provides a higher overall accuracy in machine translation systems and sentence parsers. This paper thus presents a pilot study that explored whether the bracketing of a ternary compound, when used as an argument in a sentence, can be predicted from the semantic information encoded in that sentence. It is shown that, with a random forest model, the semantic relation of the MWT to another argument in the same sentence, the lexical domain of the predicate, and the semantic role of the MWT were able to predict the bracketing of the 190 ternary compounds used as arguments in a sample of 188 semantically annotated sentences from a Coastal Engineering corpus (100% F1-score). Furthermore, only the semantic relation of an MWT to another argument in the same sentence proved enormous capability to predict ternary compound bracketing with a binary decision-tree model (94.12%F1-score).En unidades terminológicas poliléxicas (UTP) con tres o más formantes en lengua inglesa (p.ej., sea level rise), establecer la dependencia entre dichos formantes requiere de un análisis lingüístico y de conocimiento especializado del área concreta en que se emplean las UTP. Esta desambiguación estructural, o bracketing, implica el agrupamiento de los formantes para reducir la UTP a su estructura básica de modificador+núcleo, como en [sea level] [rise]. Conocer el bracketing de una UTP no solo facilita su comprensión y traducción a otras lenguas, sino que también mejora el desempeño de los sistemas de traducción automática y de los analizadores sintácticos. Por tanto, en este artículo presentamos un estudio piloto que explora si el bracketing de una UTP con tres formantes, al emplearse como argumento en una oración, puede predecirse a partir de la información semántica codificada en dicha oración. Se muestra que, con un modelo random forest, la relación semántica de la UTP con otro argumento en la misma oración, el dominio léxico del verbo y el rol semántico de la UTP son capaces de predecir el bracketing de las 190 UTP ternarias que se usan como argumento en una muestra de 188 oraciones, anotadas semánticamente y extraídas de un corpus sobre ingeniería de costas (con un valor de F1 del 100%). Además, únicamente la relación semántica que mantiene una UTP ternaria con otro argumento en la misma oración posee una enorme capacidad para predecir su bracketing mediante un árbol de decisión binario (con un valor de F1 del 94,12%).This research was carried out as part of projects PID2020-118369GB-I00, "Transversal Integration of Culture in a Terminological Knowledge Base on Environment" (TRANSCULTURE), funded by the Spanish Ministry of Science and Innovation; and A-HUM-600-UGR20, "Culture as Transversal Module in a Terminological Knowledge Base on the Environment" (CULTURAMA), funded by the Andalusian Ministry of Economy, Knowledge, Business, and University
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