Syntactic Text Processing is a subfield of Natural Language Processing (NLP) that deals with the analysis of the syntax and grammatical structure of natural language texts.

Tree typically refers to a hierarchical data structure.

Knowledge Bases refer to structured collections of information or data that are organized and stored in a way that makes it accessible and usable for computational analysis and reasoning. Knowledge bases typically consist of facts, entities, relationships, and attributes. These knowledge bases can be manually curated or automatically generated from large text corpora and are essential for enhancing the understanding and reasoning abilities of NLP systems.

Structured Data in NLP is a subfield of Multimodality that deals with the processing of structured data such as tables or graphs in conjunction with textual data. It involves the development of algorithms and models that can extract, represent, and use structured data in natural language form.

Natural Language Processing (NLP) is an interdisciplinary subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data. The goal is a computer capable of 'understanding' the contents of documents, including the contextual nuances of the language within them. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves.

Knowledge Representation is a subfield of Semantic Text Processing that deals with the representation, organization, and management of knowledge and information in a form that can be processed and used by computers in conjunction with natural language texts. It involves developing formal languages and frameworks, such as ontologies, semantic networks, and knowledge graphs, that can capture and represent the semantics, structure, and relations of concepts and entities in a domain-specific or general knowledge base.

Tagging is a subfield of Syntactic Text Processing that deals with automatically assigning labels or tags to words or other units in a natural language texts, based on their grammatical or syntactic properties.

Semantic Text Processing is a subfield of Natural Language Processing (NLP) that attempts to derive meaning from natural language and helps machines interpret textual data semantically. It involves developing algorithms and models that can understand and represent the semantics of words and sentences, and the relationships between them.

Multimodality is a subfield of Natural Language Processing (NLP) that refers to the capability of a system or method to process input of different types or “modalities”, such as natural language text, speech, audio, images, video, and programming languages in NLP applications. It involves developing algorithms and models that can process and analyze information from multiple modalities, and integrate them to form a unified representation of the input.

Tree Adjoining Grammars (TAG) are known not to be powerful enough to deal with scrambling in free word order languages. The TAG-variants proposed so far in order to account for scrambling are not entirely satisfying. Therefore, an alternative extension of TAG is introduced based on the notion of node sharing. Considering data from German and Korean, it is shown that this TAG-extension can adequately analyse scrambling data, also in combination with extraposition and topicalization.

Tree-local MCTAG with Shared Nodes: An Analysis ofWord Order Variation in German and Korean

Journées d'Etudes sur la Parole / Traitement Automatique de la Langue Naturelle / Rencontres des Etudiants Chercheurs en Informatique et Traitement Automatique des Langues

Publication:

Tree-local MCTAG with Shared Nodes: An Analysis ofWord Order Variation in German and Korean

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