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.

Discourse & Pragmatics is a subfield of Semantic Text Processing that deals with the analysis and understanding of language use in context, beyond the surface-level meaning of individual words or sentences. Discourse Analysis focuses on the organization and structure of text and conversation, and how they convey coherence, cohesion, and discourse relations, such as topic shifts, references, and implicatures. Pragmatics focuses on the study of language use in social and communicative situations, and how speakers and listeners use context, knowledge, and inference to convey meaning and achieve their communicative goals.

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.

Syntactic Parsing is a subfield of Syntactic Text Processing that deals with automatically analyzing the syntactic structure of a sentence or text, and representing it in a formal or graphical form, such as a parse tree or dependency graph. It involves developing algorithms and models that can identify and classify the syntactic constituents, such as noun phrases and verb phrases, and their relationships in a sentence, and generate a structured representation of the sentence.

A Treebank is a corpus or collection of text samples that have been manually annotated usually with syntactic or grammatical structure in the form of parse trees. These parse trees represent the hierarchical structure of sentences, showing how words are related and organized in phrases and clauses. In addition to syntactic treebanks, semantic treebanks combine syntactical trees of sentences with the formal representation of their meaning in logical form.

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.

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.

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.

Tree typically refers to a hierarchical data structure.

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.

We present the second version of the Penn Discourse Treebank, PDTB-2.0, describing its lexically-grounded annotations of discourse relations and their two abstract object arguments over the 1 million word Wall Street Journal corpus. We describe all aspects of the annotation, including (a) the argument structure of discourse relations, (b) the sense annotation of the relations, and (c) the attribution of discourse relations and each of their arguments. We list the differences between PDTB-1.0 and PDTB-2.0. We present representative statistics for several aspects of the annotation in the corpus.

Publication:

The Penn Discourse TreeBank 2.0.

Related Fields of Study

Citations

References