Pre-built Pipelines¶
Some pre-built pipelines come with the library’s source code. They are stored
under the directory configs
. The script test.py
can load them with the
-c
flag, and server.py
automatically finds them and serves them in the web
service.
Summarization¶
This pipeline is used for extracting short summaries out of news documents.
%YAML 1.2
---
# Summarizes a text by extracting the most relevant sentences.
transformers:
- pos_extract
- sim_link
- extend
- unique
- sentences
transformer_args:
sempos: { noun: n }
unique_gram: { hyper: [ True ] }
extended_sentence_edges: [ HYP ]
operations:
- op: cluster
hubratio: 0.2
# - op: markov_cluster
# expand_factor: 10
# inflate_factor: 2
# max_loop: 10
# mult_factor: 1
# - op: louvain_cluster
linearizers:
- cluster_extract
linearizer_args:
summary_length: 100
summary_margin: 10
normalize_sentence_scores: True
Concept maps¶
This pipeline generates concept maps useful for conceptual blending. Additionally, it linearizes them into a prolog triplet format.
%YAML 1.2
---
# Extracts a concept map from a text.
transformers:
- pos_extract
- wordnet
- numerals
- adjectives
- negation
- genitive
- prepositions
- attr_class
- verb_collapse
- specific_edges
- unique
- lenient
transformer_args:
sempos:
noun: n
adjective: j
attach_adjectives: True
keep_attached_adj: True
operations:
- op: filter_edges
remove:
- isa
rename:
be: is
frequency:
max: 15
min: 0
- op: spot_domain
linearizers:
- prolog