Python is a versatile programming language that offers a wide range of functionalities. One of its strengths lies in its ability to store and parse structured data. In this article, we will explore how to store and parse structured LLM (Lexical Level Markup) output in Python.
LLM is a markup language used to annotate text with linguistic information. It provides a way to represent linguistic structures such as part-of-speech tags, syntactic dependencies, and named entities. Storing and parsing LLM output can be useful for various natural language processing tasks, such as information extraction, sentiment analysis, and machine translation.
To begin, let’s consider an example of LLM output:
“`
1 The DT 2 det
2 cat NN 3 nsubj
3 sat VBD 0 root
4 on IN 3 prep
5 the DT 6 det
6 mat NN 4 pobj
“`
Each line represents a token in the input text. The columns represent different properties of the token, such as the token index, word form, part-of-speech tag, dependency head index, and dependency relation.
To store this structured LLM output in Python, we can use a data structure such as a list of dictionaries. Each dictionary represents a token and contains key-value pairs for the token properties. Here’s an example of how we can store the LLM output mentioned above:
“`python
llm_output = [
{‘index’: 1, ‘word’: ‘The’, ‘pos’: ‘DT’, ‘head’: 2, ‘rel’: ‘det’},
{‘index’: 2, ‘word’: ‘cat’, ‘pos’: ‘NN’, ‘head’: 3, ‘rel’: ‘nsubj’},
{‘index’: 3, ‘word’: ‘sat’, ‘pos’: ‘VBD’, ‘head’: 0, ‘rel’: ‘root’},
{‘index’: 4, ‘word’: ‘on’, ‘pos’: ‘IN’, ‘head’: 3, ‘rel’: ‘prep’},
{‘index’: 5, ‘word’: ‘the’, ‘pos’: ‘DT’, ‘head’: 6, ‘rel’: ‘det’},
{‘index’: 6, ‘word’: ‘mat’, ‘pos’: ‘NN’, ‘head’: 4, ‘rel’: ‘pobj’}
]
“`
In this representation, each dictionary corresponds to a token, and the keys represent the token properties.
Now that we have stored the LLM output in a structured format, we can easily parse and manipulate the data using Python. For example, we can extract all the nouns from the LLM output:
“`python
nouns = [token[‘word’] for token in llm_output if token[‘pos’] == ‘NN’]
print(nouns)
“`
This will output: `[‘cat’, ‘mat’]`, which are the nouns present in the LLM output.
We can also find the head of a specific token by its index. For instance, to find the head of the token with index 4:
“`python
token_index = 4
head_index = next(token[‘head’] for token in llm_output if token[‘index’] == token_index)
print(head_index)
“`
This will output: `3`, indicating that the head of the token with index 4 is the token with index 3.
In addition to extracting specific information, we can perform more complex operations on the LLM output. For example, we can construct a dependency tree using the LLM output:
“`python
class Node:
def __init__(self, index, word):
self.index = index
self.word = word
self.children = []
def add_child(self, child):
self.children.append(child)
def __str__(self):
return f'{self.word} ({self.index})’
def construct_tree(llm_output):
nodes = {token[‘index’]: Node(token[‘index’], token[‘word’]) for token in llm_output}
root = None
for token in llm_output:
index = token[‘index’]
head_index = token[‘head’]
if head_index == 0:
root = nodes[index]
else:
nodes[head_index].add_child(nodes[index])
return root
tree_root = construct_tree(llm_output)
“`
In this example, we define a `Node` class to represent each token in the LLM output. We then iterate over the LLM output and construct the dependency tree by connecting the nodes based on the head indices.
By storing and parsing structured LLM output in Python, we can easily access and manipulate linguistic information for various natural language processing tasks. Whether it’s extracting specific properties, performing complex operations, or constructing linguistic structures, Python
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