Information Extraction

Question 1

task

Answer 1

activity of populating a structure information repository (database) from an unstructured/free text information source

Question 2

the difference in strengths between information extraction and information retrieval

Answer 2

IR:

• can search huge collections quickly
• insensitive to genre & domain of texts
• relatively straight forward to implement

IE:

• extracts facts from texts, not just texts from text collection
• resulting structured data source has many applications

Question 3

Difference in weakness between IR and IE

Answer 3

IR:

• returns documents not information/answers so further reading required
• not discriminating enough

IE:
systems = genre/domain & porting new ones = time consuming/difficult
limited accuracy
computionally demanding

Question 4

information extraction task

Answer 4

document collection and predefined set of entities, relations and events
return: structured representation of all mentions of specified entities, relations and or events

Question 5

Named Entity Recognition

Answer 5

For each textual mention of an entity of one of a fixed set of types, identify its extent (position interval of the word in the text) and the type (organisation, person)

Question 6

Types of entities

Answer 6

named individuals, named kinds (objects), times, measures

Question 7

Coreference task

Answer 7

link together all different textual expression that refer to the same real world entity regardless of whether the surfare form

Question 8

relation extraction

Answer 8

identify all assertions of relations, usually binary between entities identified in entity extraction divided into 2 subtasks

Question 9

relation detection

Answer 9

find pairs of entities between which a relation holds

Question 10

relation classification

Answer 10

for those pairs of entities, determine what the relation is

Question 11

event detection & event classification

Answer 11

identify all reports of event instance, typically of a small set of classes divided into 2 subtasks

finds mentions of events in a text
assign detected events to one of a set of classes

Question 12

knowledge engineering approaches

Answer 12

use manually authored rules and can be divided into:

• deep: linguistically inspired language understanding systems.
• shallow: systems engineered to the IE task, typically using pattern-action rules

Question 13

supervised learning approaches

Answer 13

for each entity/relation in a given text, create a training instance represented in term of features so systems may learn patterns that match extraction targets and classifiers that classify tokens

Question 14

bootstrapping approaches

Answer 14

minimally supervised systems are given seed tuples and/or seed patterns to search the text for:
- occurrences of seed tuples then extract a pattern that - - - matches the context of the seed tuples from which it harvests new tuples

Question 15

distance supervision approaches

Answer 15

assumes a semi-structured data source which contains tuples of entities standing in the relation and a point to a source text

Question 16

Evaluation of the IE system

Answer 16

Keys = correct answers, produced manually for each extraction task
Responses = scoring of systems results vs keys done automatically