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Papers in this group tagged with "named entity recognition"

  1. We describe an approach to reducing the computational cost of identifying coreferent instances in heterogeneous semantic graphs where the underlying ontologies may not be informative or even known. The problem is similar to coreference resolution in…
  2. This paper briefly describes the components of theARCOMEMarchitecture concerned with the extraction, enrichment, consolidation and dynamics analysis of entities, topics and events, deploying text mining, NLP, and semantic data integration…
  3. Recognizing names and linking them to structured data is a fundamental task in text analysis. Existing approaches typically perform these two steps using a pipeline architecture: they use a NamedEntity Recognition (NER) system to find the boundaries…
  4. Identifying upcoming topics from a news stream is a challenging and time consuming task for editors since they have to recognize proper keywords, actively search with them, and need to browse the located media assets. To this end, our goal is to…
  5. Last year’s competition demonstrated that the NER context contains important information that should not be ignored in entity linking. State-of-the-art approaches anchor on unambiguous entities, look for overlap in categories, or approximate a joint…
  6. Information overload on the Internet motivates the need for filtering tools. Recommender systems play a significant role in such a scenario, as they provide automatically generated suggestions. In this paper, we propose a novel recommendation…
  7. With the rapidly increasing pace at which Web content is evolving, particularly social media, preserving the Web and its evolution over time becomes an important challenge. Meaningful analysis of Web content lends itself to an entity-centric view to…
  8. Tweets represent a critical source of fresh information, in which named entities occur frequently with rich variations. We study the problem of named entity normalization (NEN) for tweets. Two main challenges are the errors propagated from named…
  9. Entity Recognition (ER) is a key component of relation extraction systems and many other natural-language processing applications. Unfortunately, most ER systems are restricted to produce labels from to a small set of entity classes, e.g., person,…
  10. Classically, training relation extractors relies on high-quality, manually annotated training data, which can be expensive to obtain. To mitigate this cost, NLU researchers have considered two newly available sources of less expensive (but…
  11. Classifying blog posts by topics is useful for applications such as search and marketing. However, topic classification is time consuming and error prone, especially in an open domain such as the blogosphere. The state-of-the-art relies on…
  12. Disambiguating named entities in natural- language text maps mentions of ambiguous names onto canonical entities like people or places, registered in a knowledge base such as DBpedia or YAGO. This paper presents a robust method for collective…
  13. The use of domain knowledge is generally found to improve query efficiency in content filtering applications. In particular, tangible benefits have been achieved when using knowledge-based approaches within more specialized fields, such as medical free…