命名实体识别（Standford）

nouncements? You will need to know at least the name of the airline, the nature of

the proposed fare hike, the dates of the announcement, and possibly the response of

other airlines. Fortunately, these can be all found in news articles like this one:

entity type is application specific; these commonly include people, places, and or-

ganizations but also more specific entities from the names of genes and proteins

(Cohen and Demner-Fushman, 2014) to the names of college courses (McCallum,

2005).

the text entities, often binary relations like spouse-of, child-of, employment, part-

whole, membership, and geospatial relations. Relation extraction has close links to

populating a relational database.

The text contains 13 mentions of named entities including 5 organizations, 4 loca-

tions, 2 times, 1 person, and 1 mention of money.

also need to use specific entity types like proteins, genes, commercial products, or

works of art.

cause of the ambiguity of segmentation; we need to decide what’s an entity and what

isn’t, and where the boundaries are. Another difficulty is caused by type ambiguity.

The mention JFK can refer to a person, the airport in New York, or any number of

schools, bridges, and streets around the United States. Some examples of this kind

of cross-type confusion are given in Figures 21.2 and 21.3.

sequence classifier like an MEMM or CRF is trained to label the tokens in a text

with tags that indicate the presence of particular kinds of named entities. Consider

the following simplified excerpt from our running example.