Named Entity

material ID: namedentity

Related Papers

A disambiguation resource extracted from Wikipedia for semantic annotation

Authors: nan

Publication: TIPSTER (2012)

DOI: nan

AbstractThe Semantic Annotation (SA) task consists in establishing the relation between a textual entity (word or group of words designating a named entity of the real world or a concept) …...

13432

TaLAPi — A Thai Linguistically Annotated Corpus for Language Processing

Authors: nan

Publication: TIPSTER (2014)

DOI: nan

AbstractThis paper discusses a Thai corpus, TaLAPi, fully annotated with word segmentation (WS), part-of-speech (POS) and named entity (NE) information with the aim to provide a high-quality and sufficiently large …...

16243

Extracting Headless MWEs from Dependency Parse Trees: Parsing, Tagging, and Joint Modeling Approaches

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractAn interesting and frequent type of multi-word expression (MWE) is the headless MWE, for which there are no true internal syntactic dominance relations; examples include many named entities (“Wells Fargo”) …...

37205

Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractQuestion Answering (QA) is in increasing demand as the amount of information available online and the desire for quick access to this content grows. A common approach to QA has …...

37617

Commonsense and Named Entity Aware Knowledge Grounded Dialogue Generation

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractGrounding dialogue on external knowledge and interpreting linguistic patterns in dialogue history context, such as ellipsis, anaphora, and co-reference is critical for dialogue comprehension and generation. In this paper, we …...

21361

Why Does Surprisal From Larger Transformer-Based Language Models Provide a Poorer Fit to Human Reading Times?

Authors: nan

Publication: TIPSTER (2023)

DOI: nan

AbstractThis work presents a linguistic analysis into why larger Transformer-based pre-trained language models with more parameters and lower perplexity nonetheless yield surprisal estimates that are less predictive of human reading …...

27018