Nested Entity Mention

AKA: Nested Mention.
Context:
- It can be a noun phrase composed of noun phrases.
Example(s):
- The noun phrase “The president of Bombardier” is a Nominal Entity Mention for a Person Entity, but it also contains the name of an Organization Entity “Bombardier”, that is: “[The president of [Bombardier]]”.
- The noun phrase “The historian who taught herself Java” mentions the same Person Entity three times: 1) the entire phrase, and the two Pronouns “herself” and "who". It can be Annotated as "[The historian [who] taught [herself] Cobol]”.
- The Sentence “He is the man who killed the president of the United States.” mentions two People and one Semantic Relation, and has two Nested Entity Mention. It can be Annotated as "[He] is [[the man] [who] killed [the president of [the United States]]).”
- (Note the Chunking Structure: [He/PRP] [is/AUX] [the/DT man/NN] [who/WP] killed/VBD [the/DT president/NN] of/IN [the/DT United/NNP States/NNPS] ./.).
- …
Counter-Example(s):
- A Noun, such as: “Bombardier”.
- A Base Noun Phrase, such as: “the president”, in the Sentence "the president of Bombardier."
- a Pronoun, such as “I”.
- a Complex Relation Mention, such as:
  - "John's black cat has long whiskers".
See: Semantic Relation Mention, Unnested Entity Mention.

References

(MagniniPPS, 2006) ⇒ Bernardo Magnini, Emanuele Pianta, Octavian Popescu, and Manuela Speranza. (2006). “Ontology Population from Textual Mentions: Task Definition and Benchmark.” In: Proceedings of the Ontology Population and Learning Workshop at ACL/Coling 2006.
- We focused on mentions referring to INDIVIDUAL PERSON (Mentions in Table 3), excluding from the dataset both mentions referring to different entity types (e.g. ORGANIZATION) and PERSON GROUP. In addition, for the purposes of this work we decided to filter out the following mentions: (i) mentions consisting of a single pronoun; (ii) nested mentions, (in particular in the case where a larger mention, e.g. “President Ciampi”, contained a smaller one, e.g. “Ciampi”, only the larger mention was considered).

(Doddington et al., 2004) ⇒ George Doddington, A. Mitchell, M. Przybocki, L. Ramshaw, S. Strassel, and R. Weischedel. (2004). “The Automatic Content Extraction (ACE) Program – Tasks, Data, and Evaluation.” In: Proceedings of Conference on Language Resources and Evaluation (LREC 2004).
- Annotators tag all mentions of each entity within a document, whether named, nominal or pronominal. For every mention, the annotator identifies the maximal extent of the string that represents the entity and labels the head of each mention. Nested mentions are also captured. Each entity is classified according to its type and subtype. Each entity mention is further tagged according to its class – specific, generic, attributive, negatively quantified or underspecified. During the LNK annotation task, annotators review the entire document to group mentions of the same entity together; they also label cases of metonymy, where the name of one entity is used to refer to another entity (or entities) related to it.