Pronoun Resolution
Jurafsky and Martin [JM] suggest an algorithm by Lappine and Leass (1994) for pronoun interpretation which takes many concerns into consideration. There are two types of operations that this algorithm performs:
Discourse model update
When a noun phrase evokes a new entity, a representation
for it is added to the model
and a degree of salience for it is computed.
[Actually a vector of salience factors is created. The
salience value is a weighted sum of the component of this
vector.]
As each sentence is processed, each factor of the computed value is reduced by half.
| Sentence recency | 100 |
| Subject emphasis | 80 |
| Existential emphasis | 70 |
| Accusative (direct object) emphasis | 50 |
| Indirect object and oblique complement emphasis | 40 |
| Non-adverbial emphasis | 50 |
| Head noun emphasis | 80 |
Pronoun resolution
Any pronoun encountered is then processed using
the mechanisms presented above to determine the
referrent with the highest salience.†
† In practice, each referent is associated with an equivalence class of all expressions which reference it. The referent's salience is then the sum of the max of each factor of elements within the equivalence class.
Examples
18.62 An Acura Integra is parked in the lot. (subject)
18.63 There is an Acura Integra parked in the lot.
(existential predicate nominal)
18.64 John parked an Acura Integra in the lot.
(object)
18.65 John gave his Acura Integra a bath.
(indirect object)
18.66 Inside his Acura Integra, John showed Susan
his new CD player.
(demarcated adverbial prepositional phrase)