Grammars

Question 1

Use of CFGs in NLP

Answer 1

• CFGs tend to be effective for hierarchical strucutres of language
• Probabilistic extensions(PCFGs) capture the likeliness of structures
• CFGs usually define the basis of syntactic parsing

Question 2

Syntactic parsing

Answer 2

• The text analysis that determines the syntactic structure of a sentence
• Used in NLP as preprocessing for many tasks, e.g., relation extraction

Question 3

Constituencies vs Dependencies parsing

Answer 3

Constituency parsing: Infers the structure of the phrases in a sentence
Dependency parsing: infers the structure of the words’ dependencies

Question 4

Transformation into normal form

Answer 4

• Cleaning: Empties and unaries are removed recursively
• Binarization: n-ary rules are divided by using new non-terminals, n>2
• Any CFG can be tranformed into CNF without changing the language
• This may result in different parse trees for words the language

Question 5

Constituency parsing

Answer 5

• The text analysis that determines the phrase structure of a sentence with respect to a given grammar
• Often used in NLP as preprocessing where syntax is important
• Parsing works robust across domains of well-formatted texts.

Question 6

Downstream tasks(tareas posteriores) based on parsing

Answer 6

Named entity recognition in complex domains
Relationship extraction: both for semantic and temporal relations
Coreference resolution: to identify candidate matching references
Opinion mining regarding aspects of products or similar
Machine translation, to analyze the source sentence
Quiestion answering, particularly in high-precision scenarios

Question 7

Attachment ambiguity
Key parsing problem

Answer 7

Correct attachment of the various constituents in a sentence, such as prepositional phrases, adverbial phrases, infinitives, ….

Question 8

How to find the correct attachment?

Answer 8

Potential attachments grow exponentially with number n of constituents

Question 9

Limitations of standard PCFGs

Answer 9

-PCFGs assume that the plausibility of structures is independent of the words used, i.e., each rule has a fixed probability
-However, specific words may make certain rules particularly (un)likely

Question 10

Dependency Grammar

Answer 10

• A grammar that odels the syntactic structure of a sentence by linking its tokens with binary asymmetric relations
• Relations, called dependencies, define grammatical connections

Question 11

Graph representation of dependency grammar

Answer 11

-Each node is a token
- An edge connects a head with a dependent node
- The nodes and edges form a fully connected, acyclic tree with a single head(if available, the main verb of the first main clause is the head)

Question 12

Identification of Dependencies:
Selected features of dependencies

Answer 12

• Breaks: Dependencies rarely span intervening verbs or punctuation
• Valency: heads have usual numbers of dependents on each side
• Affinities: Some dependencies are more plausible than others(For example: “Issues -> the” rather than “the -> issues”

Question 13

Parsing Methods

Answer 13

Dynamic programming
Graph algorithmus
Transition-based parsing