Sap Se

Benjamin Schork

Michail Vasiltschenko

Jochen Geib

Yvonne Miklosch

Marcus Danei

Florian Hoffmann

Matthias Becker

Christian Lieske

Anton Snitko

Jens Scharnbacher

Annika Berger

Tetyana Chernenko

Thomas Alcantara Kliche

Lauritz Brandt

Mark Childress

Hanne Smaadahl

Robert Stuehler

Klaus Heitlinger

Ying Zou

Thomas Jargstorff

Term ambiguity is resolved by referencing a terminology database. An input is received comprising the term designated as ambiguous, and a string including the term. The term is posed as a query to the terminology database containing metadata of at least one type. Query results are returned including at least two possible meanings. Sequence(s) are extracted from the query results, each sequence including at least two pieces of metadata of a same type—one for each possible meaning of the ambiguous term. The metadata of each entry of a sequence is compared with the query result and corresponding scores are calculated. The scores are compared to determine a final meaning of the ambiguous term. Simpler embodiments considering one type of metadata (one sequence), may calculate and compare a listing of scores. Complex embodiments considering more than one type of metadata (multiple sequences), may calculate and compare a matrix of scores.

System and method performing terminology disambiguation

Systems and methods include acquisition of a plurality of text segments, each of the text segments associated with a flag value indicating whether the text segment is associated with a correct replacement text or an incorrect replacement text, determination of one or more n-grams of each text segment of the plurality of text segments, generation, based on the one or more n-grams of each text segment and the flag value associated with each text segment, a model to determine a flag value based on one or more input n-grams, reception of an input text segment, determination of a second one or more n-grams of the input text segment, determination, using the model, of an output flag value based on the determined second one or more n-grams, and presentation of the input text segment and the output flag value on a display.

Text verticalization categorization

Techniques and solutions are described for translating strings in a human language that are associated with code, such as code that declares user interface elements in association with which the strings can be displayed. The strings can be embedded in code, or can be included in files that are accessed by the code or used in creating executable code. Strings can be extracted from data, such as files, received in a translation request. After translation, such as by a human translator or using automatic translation, the translation results can be provided in response to the translation request. The translation process can be automated, such that changes to files in a code repository can trigger a translation request. Disclosed technologies can facilitate the production of programs that are localized using different human languages.

String extraction and translation service

An input terminology is received in a source language to determine a target language proposal. In a proposal engine when a configuration profile is available the following steps are performed. A set of target language equivalents are determined by applying a user-defined set of approaches to the input terminology. Statistics is computed corresponding to the set of target language equivalents. A target language proposal from the set of target language equivalents is determined based on the computed statistics. The target language proposal along with the computed statistics is displayed in a graphical user interface.

Terminology proposal engine for determining target language equivalents

Computer-assisted multilingual translations may utilize a concept database storing entries for a plurality of concepts, the entries including multilingual vectors of counterpart expressions for the respective concepts in a source language and multiple target languages. In various embodiments, for a given content item having an associated source-language expression, a set of concepts whose database entries match at least the source-language expression is identified, and target-language expressions for the identified concepts are used to iteratively update the set of concepts by identifying therein a subset of concepts that also match a user-selected one of the target-language expressions.

Concept recommendation based on multilingual user interaction

The embodiments may be adapted to accommodate a plurality of translations of a source text string into a limited available display area of a visual element in a graphical interface of a client system. Text strings in a source language may be received as input. A list of one or more languages selected for the text string to be translated into may also be received as input. The system may then be configured to obtain translations of the text string in the selected languages, to provide a set of translation vectors, and to calculate a minimum display area necessary for a visual element to display the longest translation of the translations contained in the translation vector. The display area of the visual element may then be adjusted to encompass the minimum display area so that the longest translation fits within the display area of the visual element.

Translation-based visual design

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Wilhelmsfeld, Germany

Benjamin Schork