1,584 research outputs found
Improving the translation environment for professional translators
When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side.
This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project
Multilingual Lexicon Extraction under Resource-Poor Language Pairs
In general, bilingual and multilingual lexicons are important resources in many natural language processing fields such as information retrieval and machine translation. Such lexicons are usually extracted from bilingual (e.g., parallel or comparable) corpora with external seed dictionaries. However, few such corpora and bilingual seed dictionaries are publicly available for many language pairs such as KoreanâFrench. It is important that such resources for these language pairs be publicly available or easily accessible when a monolingual resource is considered.
This thesis presents efficient approaches for extracting bilingual single-/multi-word lexicons for resource-poor language pairs such as KoreanâFrench and KoreanâSpanish. The goal of this thesis is to present several efficient methods of extracting translated single-/multi-words from bilingual corpora based on a statistical method.
Three approaches for single words and one approach for multi-words are proposed. The first approach is the pivot context-based approach (PCA). The PCA uses a pivot language to connect source and target languages. It builds context vectors from two parallel corpora sharing one pivot language and calculates their similarity scores to choose the best translation equivalents. The approach can reduce the effort required when using a seed dictionary for translation by using parallel corpora rather than comparable corpora. The second approach is the extended pivot context-based approach (EPCA). This approach gathers similar context vectors for each source word to augment its context. The approach assumes that similar vectors can enrich contexts. For example, young and youth can augment the context of baby. In the investigation described here, such similar vectors were collected by similarity measures such as cosine similarity. The third approach for single words uses a competitive neural network algorithm (i.e., self-organizing mapsSOM). The SOM-based approach (SA) uses synonym vectors rather than context vectors to train two different SOMs (i.e., source and target SOMs) in different ways. A source SOM is trained in an unsupervised way, while a target SOM is trained in a supervised way.
The fourth approach is the constituent-based approach (CTA), which deals with multi-word expressions (MWEs). This approach reinforces the PCA for multi-words (PCAM). It extracts bilingual MWEs taking all constituents of the source MWEs into consideration. The PCAM
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identifies MWE candidates by pointwise mutual information first and then adds them to input data as single units in order to use the PCA directly.
The experimental results show that the proposed approaches generally perform well for resource-poor language pairs, particularly Korean and FrenchâSpanish. The PCA and SA have demonstrated good performance for such language pairs. The EPCA would not have shown a stronger performance than expected. The CTA performs well even when word contexts are insufficient. Overall, the experimental results show that the CTA significantly outperforms the PCAM.
In the future, homonyms (i.e., homographs such as lead or tear) should be considered. In particular, the domains of bilingual corpora should be identified. In addition, more parts of speech such as verbs, adjectives, or adverbs could be tested. In this thesis, only nouns are discussed for simplicity. Finally, thorough error analysis should also be conducted.Abstract
List of Abbreviations
List of Tables
List of Figures
Acknowledgement
Chapter 1 Introduction
1.1 Multilingual Lexicon Extraction
1.2 Motivations and Goals
1.3 Organization
Chapter 2 Background and Literature Review
2.1 Extraction of Bilingual Translations of Single-words
2.1.1 Context-based approach
2.1.2 Extended approach
2.1.3 Pivot-based approach
2.2 Extractiong of Bilingual Translations of Multi-Word Expressions
2.2.1 MWE identification
2.2.2 MWE alignment
2.3 Self-Organizing Maps
2.4 Evaluation Measures
Chapter 3 Pivot Context-Based Approach
3.1 Concept of Pivot-Based Approach
3.2 Experiments
3.2.1 Resources
3.2.2 Results
3.3 Summary
Chapter 4 Extended Pivot Context-Based Approach
4.1 Concept of Extended Pivot Context-Based Approach
4.2 Experiments
4.2.1 Resources
4.2.2 Results
4.3 Summary
Chapter 5 SOM-Based Approach
5.1 Concept of SOM-Based Approach
5.2 Experiments
5.2.1 Resources
5.2.2 Results
5.3 Summary
Chapter 6 Constituent-Based Approach
6.1 Concept of Constituent-Based Approach
6.2 Experiments
6.2.1 Resources
6.2.2 Results
6.3 Summary
Chapter 7 Conclusions and Future Work
7.1 Conclusions
7.2 Future Work
Reference
A Continuously Growing Dataset of Sentential Paraphrases
A major challenge in paraphrase research is the lack of parallel corpora. In
this paper, we present a new method to collect large-scale sentential
paraphrases from Twitter by linking tweets through shared URLs. The main
advantage of our method is its simplicity, as it gets rid of the classifier or
human in the loop needed to select data before annotation and subsequent
application of paraphrase identification algorithms in the previous work. We
present the largest human-labeled paraphrase corpus to date of 51,524 sentence
pairs and the first cross-domain benchmarking for automatic paraphrase
identification. In addition, we show that more than 30,000 new sentential
paraphrases can be easily and continuously captured every month at ~70%
precision, and demonstrate their utility for downstream NLP tasks through
phrasal paraphrase extraction. We make our code and data freely available.Comment: 11 pages, accepted to EMNLP 201
A Survey of Paraphrasing and Textual Entailment Methods
Paraphrasing methods recognize, generate, or extract phrases, sentences, or
longer natural language expressions that convey almost the same information.
Textual entailment methods, on the other hand, recognize, generate, or extract
pairs of natural language expressions, such that a human who reads (and trusts)
the first element of a pair would most likely infer that the other element is
also true. Paraphrasing can be seen as bidirectional textual entailment and
methods from the two areas are often similar. Both kinds of methods are useful,
at least in principle, in a wide range of natural language processing
applications, including question answering, summarization, text generation, and
machine translation. We summarize key ideas from the two areas by considering
in turn recognition, generation, and extraction methods, also pointing to
prominent articles and resources.Comment: Technical Report, Natural Language Processing Group, Department of
Informatics, Athens University of Economics and Business, Greece, 201
Using conceptual vectors to get Magn collocations (and using contrastive properties to get their translations)
International audienceThis paper presents a semi-automatic approach for extraction of collocations from corpora which uses the results of Conceptual Vectors as a semantic filter. First, this method estimates the ability of each co-occurrence to be a collocation, using a statistical measure based on the fact that it occurs more often than by chance. Then the results are automatically filtered (with conceptual vectors) to retain only one given semantic kind of collocations. Finally we perform a new filtering based on manually entered data. Our evaluation on monolingual and bilingual experiments shows the interest to combine automatic extraction and manual intervention to extract collocations (to fill multilingual lexical databases). It proves especially that the use of conceptual vectors to filter the candidates allows us to increase the precision noticeably
Word frequency predicts translation asymmetry
Bilingualism studies report asymmetries in word processing across languages. Access to L2 words is slower and sensitive to semantic blocking. These observations inform influential models of bilingual processing, which propose autonomous lexicons with different processing routes. In a series of experiments, we explored an alternative hypothesis that the asymmetries are due to frequency of use. Using a within-language âtranslationâ task, involving high/low frequency (HF/LF) synonyms, we obtained parallel results to bilingual studies. Experiment 1 revealed that HF synonyms were accessed faster than LF ones. Experiment 2 showed that semantic blocking slowed retrieval only of LF synonyms, while form blocking produced powerful interference of both HF and LF words. Experiment 3 examined translation speed and sensitivity to blocking in two groups of Russian-English bilinguals who differed in frequency of use of their languages. Translation asymmetries were modulated by frequency of use. The results support an integrated lexicon model of bilingual processing
Foundation, Implementation and Evaluation of the MorphoSaurus System: Subword Indexing, Lexical Learning and Word Sense Disambiguation for Medical Cross-Language Information Retrieval
Im medizinischen Alltag, zu welchem viel Dokumentations- und Recherchearbeit gehört, ist mittlerweile der ĂŒberwiegende Teil textuell kodierter Information elektronisch verfĂŒgbar. Hiermit kommt der Entwicklung leistungsfĂ€higer Methoden zur effizienten Recherche eine vorrangige Bedeutung zu.
Bewertet man die NĂŒtzlichkeit gĂ€ngiger Textretrievalsysteme aus dem Blickwinkel der medizinischen Fachsprache, dann mangelt es ihnen an morphologischer FunktionalitĂ€t (Flexion, Derivation und Komposition), lexikalisch-semantischer FunktionalitĂ€t und der FĂ€higkeit zu einer sprachĂŒbergreifenden Analyse groĂer DokumentenbestĂ€nde.
In der vorliegenden Promotionsschrift werden die theoretischen Grundlagen des MorphoSaurus-Systems (ein Akronym fĂŒr Morphem-Thesaurus) behandelt. Dessen methodischer Kern stellt ein um Morpheme der medizinischen Fach- und Laiensprache gruppierter Thesaurus dar, dessen EintrĂ€ge mittels semantischer Relationen sprachĂŒbergreifend verknĂŒpft sind. Darauf aufbauend wird ein Verfahren vorgestellt, welches (komplexe) Wörter in Morpheme segmentiert, die durch sprachunabhĂ€ngige, konzeptklassenartige Symbole ersetzt werden. Die resultierende ReprĂ€sentation ist die Basis fĂŒr das sprachĂŒbergreifende, morphemorientierte Textretrieval.
Neben der Kerntechnologie wird eine Methode zur automatischen Akquise von LexikoneintrĂ€gen vorgestellt, wodurch bestehende Morphemlexika um weitere Sprachen ergĂ€nzt werden. Die BerĂŒcksichtigung sprachĂŒbergreifender PhĂ€nomene fĂŒhrt im Anschluss zu einem neuartigen Verfahren zur Auflösung von semantischen AmbiguitĂ€ten.
Die LeistungsfĂ€higkeit des morphemorientierten Textretrievals wird im Rahmen umfangreicher, standardisierter Evaluationen empirisch getestet und gĂ€ngigen Herangehensweisen gegenĂŒbergestellt
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