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In computational linguistics, coreference resolution is a well-studied problem in discourse. To derive the correct interpretation of a text, or even to estimate the relative importance of various mentioned subjects, pronouns and other referring expressions must be connected to the right individuals. Algorithms intended to resolve coreferences ...
Natural language processing (NLP) is a subfield of computer science and especially artificial intelligence.It is primarily concerned with providing computers with the ability to process data encoded in natural language and is thus closely related to information retrieval, knowledge representation and computational linguistics, a subfield of linguistics.
Given a sentence or larger chunk of text, coreference resolution determines which words ("mentions") refer to which objects ("entities") included in the text. Anaphora resolution – concerned with matching up pronouns with the nouns or names that they refer to. For example, in a sentence such as "He entered John's house through the front door ...
Coreference resolution understands whether multiple words in a text refer to the same entity. It can be useful, for example, to understand the word a pronoun refers to. Consider the following example: Paris is the capital of France. It is also the largest city in France.
As of 2020, BERT is a ubiquitous baseline in natural language processing (NLP) experiments. [3] BERT is trained by masked token prediction and next sentence prediction. As a result of this training process, BERT learns contextual, latent representations of tokens in their context, similar to ELMo and GPT-2. [4]
The Apache OpenNLP library is a machine learning based toolkit for the processing of natural language text. It supports the most common NLP tasks, such as language detection, tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing and coreference resolution.
anaphor resolution (see coreference resolution in IE below, but seen here as the task to create links between textual mentions rather than between the mention of an entity and an abstract representation of the entity) semantic role labelling (SRL, related to relation extraction; not to be confused with semantic annotation as described below)
In natural language processing a w-shingling is a set of unique shingles (therefore n-grams) each of which is composed of contiguous subsequences of tokens within a document, which can then be used to ascertain the similarity between documents.