Various techniques can be leveraged in natural language processing. Below are some of the core techniques that we use at bridge_ci.
Latent Dirichlet Allocation (LDA) is a probabilistic method for topic modeling in natural language processing (NLP). It is a generative statistical model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar. LDA assumes that each document is a mixture of topics and that each topic is a probability distribution over words.
The LDA algorithm works by assigning words at random to topics for each document, then iteratively updating the topic assignments based on two factors:
The probability of a word belonging to a topic
The probability of a topic occurring in a document
The algorithm converges when the topic assignments no longer change significantly. LDA is widely used in text mining and information retrieval to extract high-quality information from unstructured data. It can identify the underlying topics in a corpus of text, which can be useful for tasks such as document classification, clustering, and summarization.
In practice, LDA requires the number of topics to be specified in advance, and the choice of this parameter can significantly impact the quality of the results. Additionally, LDA assumes that the order of words in a document does not matter, which can limit its effectiveness in some applications.
The bag of words approach is a simplification used to represent text data for NLP tasks. In this representation, a text (such as a sentence or document) is represented as a bag (or multiset) of its words, disregarding grammar and word order but keeping multiplicity. This simplification allows various NLP techniques, such as topic modeling, to be applied to the text data.
For example, consider the following two sentences:
"The quick brown fox jumps over the lazy dog."
"The lazy dog is jumped over by the quick brown fox."
Using the bag of words approach, these sentences would be represented as:
Sentence 1: {"the": 2, "quick": 1, "brown": 1, "fox": 1, "jumps": 1, "over": 1, "lazy": 1, "dog": 1}
Sentence 2: {"the": 2, "lazy": 1, "dog": 1, "is": 1, "jumped": 1, "over": 1, "by": 1, "quick": 1, "brown": 1, "fox": 1}
As you can see, the bag of words approach ignores the order of the words and the grammatical structure of the sentences, but it keeps track of the frequency of each word.
Relationship between LDA and Bag of Words
LDA can be applied to the bag-of-words representation of text data to discover the underlying topics in a corpus. In this context, LDA treats each document as a mixture of topics and each topic as a probability distribution over words. The bag-of-words representation simplifies the text data by ignoring grammar and word order, making it suitable for topic modeling techniques like LDA.
The difference is that the bag-of-words approach is a simplification used to represent text data that ignores grammar and word order. At the same time, LDA is a topic modeling algorithm that can be applied to the bag-of-words representation of text data to discover the underlying topics in a corpus.
These techniques can extract central themes and insights from PDFs, Glassdoor reviews, or social media posts to find key subject matters related to an issue. This can be incredibly valuable for gaining a deep and nuanced understanding of complex subjects and their interrelationships. Furthermore, it can inform decision-making and strategy development.