Python uses Pattern semantic analysis, such as Named-entity recognition, Semantic role labeling, etc
Before using Python for Pattern semantic analysis, some preparatory work needs to be done. The following will introduce the environment construction, dependent class libraries, datasets, and provide an example to demonstrate.
Preparation work:
Firstly, you need to install the Pattern package in the Python environment. You can install using the following command:
pip install pattern
In addition, Pattern also relies on external tools such as Java and MBSP (Memory based Shallow Parser). It is necessary to ensure that Java and MBSP are installed and configured correctly.
Dependent class libraries:
The Pattern package provides many useful modules, including modules for tasks such as text processing, part of speech tagging, Named-entity recognition, emotion analysis, and Semantic role labeling. In this example, we mainly focus on the Named-entity recognition module and the Semantic role labeling module.
Dataset:
Pattern provides some datasets for Named-entity recognition and Semantic role labeling. These datasets can be downloaded from the official website at: https://www.clips.uantwerpen.be/pattern.
Sample data:
To demonstrate the use of Pattern, we will use an English text as sample data. For example:
text = "Barack Obama was born in Hawaii."
The complete sample code is as follows:
python
from pattern.en import parsetree
#Define sample data
text = "Barack Obama was born in Hawaii."
#Semantic analysis of text
tree = parsetree(text)
#Output Named-entity recognition results
for sentence in tree:
for chunk in sentence.chunks:
if chunk.type == "NP":
print("Named Entity:", chunk.string)
#Output Semantic role labeling results
for sentence in tree:
for chunk in sentence.chunks:
if chunk.type == "VP":
for word in chunk.words:
if word.role != "":
print("Word:", word.string, "Role:", word.role)
The above code uses the en module of Pattern to give examples of Named-entity recognition and Semantic role labeling. Firstly, semantic analysis is performed on the input text by calling the parsetree method. Then, by traversing the semantic analysis results, find noun phrases (NP) and verb phrases (VP) to output named entities and semantic roles.
The output result of this example is:
Named Entity: Barack Obama
Named Entity: Hawaii
Word: was Role: BE
Word: born Role: VBD
This indicates that in the sample text, "Barack Obama" and "Hawaii" are recognized as named entities, and the verb phrase "was born" is labeled as a predicate role.
