“UBY”的意思、由来-开放百科全书

UBY^[1] is a large-scale lexical-semantic resource for natural language processing (NLP) developed at the Ubiquitous Knowledge Processing Lab (UKP) in the department of Computer Science of the Technische Universität Darmstadt .

UBY is based on the ISO standard Lexical Markup Framework (LMF) and combines information from several expert-constructed and collaboratively constructed resources for English and German.

UBY applies a word sense alignment approach (subfield of word sense disambiguation) for combining information about nouns and verbs.^[2]

Included resources

Format

UBY-LMF^[3]^[4] is a format for standardizing lexical resources for Natural Language Processing (NLP).^[5] UBY-LMF

conforms to the ISO standard for lexicons: LMF, designed within the ISO-TC37, and constitutes a so-called serialization of this abstract standard.^[6] In accordance with the LMF, all attributes and other linguistic terms introduced in UBY-LMF refer to standardized descriptions of their meaning in ISOCat.

Availability and versions

UBY is available as part of the open resource repository DKPro. DKPro UBY is a Java framework for creating and accessing sense-linked lexical resources in accordance with the UBY-LMF lexicon model. While the code of UBY is licensed under a mix of free licenses such as GPL and CC by SA, some of the included resources are under different licenses such as academic use only.

There is also a Semantic Web version of UBY called lemonUby.^[7] lemonUby is based on the lemon model as proposed in the Monnet project. lemon is a model for modeling lexicon and machine-readable dictionaries and linked to the Semantic Web and the Linked Data cloud.

UBY vs. BabelNet

BabelNet is an automatically lexical semantic resource that links Wikipedia to the most popular computational lexicons such as WordNet. At first glance, UBY and BabelNet seem to be identical and competitive projects; however, the two resources follow different philosophies.

In its early stage, BabelNet was primarily based on the alignment of WordNet and Wikipedia, which by the very nature of Wikipedia implied a strong focus on nouns, and especially named entities. Later on, the focus of BabelNet was shifted more towards other parts of speech. UBY, however, was focused from the very beginning on verb information, especially, syntactic information, which is contained in resources, such as VerbNet or FrameNet.

Another main difference is that UBY models other resources completely and independently from each other, so that UBY can be used as wholesale replacement of each of the contained resources. A collective access to multiple resources is provided through the available resource alignments. Moreover, the LMF model in UBY allows unified way of access for all as well as individual resources. Meanwhile, BabelNet follow an approach similar to WordNet and bakes selected information types into so called Babel Synsets. This makes access and processing of the knowledge more convenient, however, it blurs the lines between the linked knowledge bases. Additionally, BabelNet enriches the original resources, e.g., by providing automatically created translations for concepts which are not lexicalized in a particular language. Although this provides a great boost of coverage for multilingual applications, the automatic inference of information is always prone to a certain degree of error.

In summary, due to the listed differences between the two resources, the usage of one or the other might be preferred depending on the particular application scenario. In fact, the two resources can be used to provide extensive lexicographic knowledge, especially, if they are linked together. The open and well-documented structure of the two resource provide a crucial milestone to achieve this goal.

Applications

UBY has been successfully used in different NLP tasks such as Word Sense Disambiguation,^[8] Word Sense Clustering,^[9] Verb Sense Labeling ^[10] and Text Classification.^[11] UBY also inspired other projects on automatic construction of lexical semantic resources.^[12] Furthermore, lemonUby was used to improve machine translation results, especially, finding translations for unknown words.^[13]

See also

External links

References

1. ^{{cite Q | Q51752742 | Iryna Gurevych, Judith Eckle-Kohler, Silvana Hartmann, Michael Matuschek, Christian M. Meyer, Christian Wirth: UBY – a large-scale unified lexical-semantic resource based on LMF, Proceedings of EACL, pp. 580–590, 2012, Avignon, France.}}
2. ^Matuschek, Michael: Word Sense Alignment of Lexical Resources. Technische Universität, Darmstadt [Dissertation], (2015)
3. ^Judith Eckle-Kohler, Iryna Gurevych, Silvana Hartmann, Michael Matuschek, Christian M Meyer: UBY-LMF – exploring the boundaries of language-independent lexicon models, in Gil Francopoulo, LMF Lexical Markup Framework, ISTE / Wiley 2013 ({{ISBN|978-1-84821-430-9}})
4. ^Judith Eckle-Kohler, Iryna Gurevych, Silvana Hartmann, Michael Matuschek and Christian M. Meyer. UBY-LMF – A Uniform Model for Standardizing Heterogeneous Lexical-Semantic Resources in ISO-LMF. In: Nicoletta Calzolari and Khalid Choukri and Thierry Declerck and Mehmet Uğur Doğan and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis: Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC), p. 275--282, May 2012.
5. ^Gottfried Herzog, Laurent Romary, Andreas Witt: Standards for Language Resources. Poster Presentation at the META-FORUM 2013 – META Exhibition, September 2013, Berlin, Germany.
6. ^Laurent Romary: TEI and LMF crosswalks. CoRR abs/1301.2444 (2013)
7. ^Judith Eckle-Kohler, John Philip McCrae and Christian Chiarcos: lemonUby – a large, interlinked, syntactically-rich lexical resource for ontologies. In: Semantic Web Journal, vol. 6, no. 4, p. 371-378, 2015.
8. ^Christian M. Meyer and Iryna Gurevych: To Exhibit is not to Loiter: A Multilingual, Sense-Disambiguated Wiktionary for Measuring Verb Similarity, in: Proceedings of the 24th International Conference on Computational Linguistics (COLING), Vol. 4, p. 1763–1780, December 2012. Mumbai, India.
9. ^Michael Matuschek, Tristan Miller and Iryna Gurevych: A Language-independent Sense Clustering Approach for Enhanced WSD. In: Josef Ruppert and Gertrud Faaß: Proceedings of the 12th Konferenz zur Verarbeitung natürlicher Sprache (KONVENS 2014), p. 11-21, Universitätsverlag Hildesheim, October 2014.
10. ^Kostadin Cholakov and Judith Eckle-Kohler and Iryna Gurevych : Automated Verb Sense Labelling Based on Linked Lexical Resources. In: Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2014), p. 68-77, Association for Computational Linguistics
11. ^Lucie Flekova and Iryna Gurevych: Personality Profiling of Fictional Characters using Sense-Level Links between Lexical Resources, in: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP), September 2015.
12. ^José Gildo de A. Júnior, Ulrich Schiel, and Leandro Balby Marinho. 2015. An approach for building lexical-semantic resources based on heterogeneous information sources. In Proceedings of the 30th Annual ACM Symposium on Applied Computing (SAC '15). ACM, New York, USA, 402-408. DOI=10.1145/2695664.2695896 http://doi.acm.org/10.1145/2695664.2695896
13. ^J. P. McCrae, P. Cimiano: Mining translations from the web of open linked data, in: Proceedings of the Joint Workshop on NLP&LOD and SWAIE: Semantic Web, Linked Open Data and Information Extraction, pp 9-13 (2013).