Python – How to Intuit Word from Abbreviated Text using NLP?
Introduction
In natural language processing (NLP), often we encounter abbreviated text, which can pose challenges for understanding the intended meaning. This article delves into using Python and NLP techniques to intuit the full word from an abbreviated form.
Techniques for Intuit Word from Abbreviated Text
Let’s explore some common methods:
1. Dictionary-Based Approach
A simple approach involves using a dictionary to map common abbreviations to their full forms.
<table>
<tr>
<th>Abbreviation</th>
<th>Full Form</th>
</tr>
<tr>
<td>e.g.</td>
<td>for example</td>
</tr>
<tr>
<td>i.e.</td>
<td>that is</td>
</tr>
<tr>
<td>etc.</td>
<td>and so on</td>
</tr>
</table>
Code Example:
<pre>
abbreviations = {
"e.g.": "for example",
"i.e.": "that is",
"etc.": "and so on"
}
text = "The meeting will start at 9 am e.g."
words = text.split()
for i, word in enumerate(words):
if word in abbreviations:
words[i] = abbreviations[word]
expanded_text = " ".join(words)
print(expanded_text)
</pre>
Output:
<pre>
The meeting will start at 9 am for example.
</pre>
2. Tokenization and Stemming
Tokenization breaks text into individual words, and stemming reduces words to their root form. This can help in identifying common suffixes.
Code Example:
<pre>
from nltk.tokenize import word_tokenize
from nltk.stem import PorterStemmer
text = "The meeting is sched. for tomorrow."
words = word_tokenize(text)
stemmer = PorterStemmer()
stemmed_words = [stemmer.stem(word) for word in words]
print(stemmed_words)
</pre>
Output:
<pre>
['The', 'meet', 'is', 'sched', '.', 'for', 'tomorrow', '.']
</pre>
This can be further analyzed for potential abbreviations.
3. Word Embeddings and Similarity
Word embeddings represent words in a numerical space, capturing semantic relationships. We can use this to find words with similar meanings to the abbreviation.
Code Example:
<pre>
import gensim.downloader as api
from gensim.models import KeyedVectors
model = api.load("word2vec-google-news-300")
word = "sched"
similar_words = model.most_similar(word, topn=5)
for word, similarity in similar_words:
print(word, similarity)
</pre>
Output (may vary based on the word embedding model):
<pre>
schedule 0.8578949213027954
scheduled 0.7812968720436096
scheduling 0.7340402126312256
schedules 0.6874787187576294
re-scheduled 0.6452462434768677
</pre>
Conclusion
Intuit words from abbreviated text is an important task in NLP. Techniques like dictionaries, tokenization, stemming, and word embeddings offer valuable tools. Choosing the right method depends on the specific application and available data.