Abstract The use-mention distinction is elaborated into a four-way between use, formal mention, material mention and pragmatic mention. notion of motivated through the problem monsters in Kaplanian indexical semantics. It then formalized applied an account schemata languages.

Abstract Named entity recognition (NER) is a fundamental task for natural language processing, which aims to detect mentions of real-world entities from text and classifying them into predefined types. Recently, research on overlapped discontinuous named has received increasing attention. However, we note that few studies have considered both entities. In this paper, proposed novel sequence-to-...

Scientific publications contain many references to method terminologies used during scientific experiments. New terms are constantly created within the research community, especially in the biomedical domain where thousands of papers are published each week. In this study we report our attempt to automatically extract such method terminologies from scientific research papers, using rule-based a...

When humans communicate via natural language, they frequently make use of metalanguage to clarify what they mean and promote a felicitous exchange of ideas. One key aspect of metalanguage is the mention of words and phrases, as distinguished from their use. This paper presents ongoing work on identifying and categorizing instances of languagemention, with the goal of building a system capable o...

A mention may or may not be coreferred elsewhere in the document. Identifying those mentions that are corefered (called coreferents) is an important step in many NLP tasks, like coreference resolution. To classify a mention as singleton or coreferent using just one sentence is a challenging problem, but previous work suggests that there are cues in a sentence which can be used to predict if a m...

Entity disambiguation, or mapping a phrase to its canonical representation in a knowledge base, is a fundamental step in many natural language processing applications. Existing techniques based on global ranking models fail to capture the individual peculiarities of the words and hence, either struggle to meet the accuracy requirements of many real-world applications or they are too complex to ...