NLP At Work: The Difference That Makes The Diff... ((BETTER))
Natural Language Processing (NLP) makes it possible for computers to understand the human language. Behind the scenes, NLP analyzes the grammatical structure of sentences and the individual meaning of words, then uses algorithms to extract meaning and deliver outputs. In other words, it makes sense of human language so that it can automatically perform different tasks.
NLP At Work: The Difference that Makes the Diff...
Natural Language Processing (NLP) is a field of Artificial Intelligence (AI) that makes human language intelligible to machines. NLP combines the power of linguistics and computer science to study the rules and structure of language, and create intelligent systems (run on machine learning and NLP algorithms) capable of understanding, analyzing, and extracting meaning from text and speech.
NLP and NLU are significant terms to design the machine that can easily understand the human language, whether it contains some common flaws. There is a small difference between the terms, that are very important for the developers to know if they want to create a machine that can interact with humans by giving them a human-like environment because the use of the correct technique at the right place is essential to succeed in systems created for Natural Language operations.
Bag of Words (BoW) simply counts the frequency of words in a document. Thus the vector for a document has the frequency of each word in the corpus for that document. The key difference between bag of words and TF-IDF is that the former does not incorporate any sort of inverse document frequency (IDF) and is only a frequency count (TF).
Word2Vec is an algorithm that uses shallow 2-layer, not deep, neural networks to ingest a corpus and produce sets of vectors. Some key differences between TF-IDF and word2vec is that TF-IDF is a statistical measure that we can apply to terms in a document and then use that to form a vector whereas word2vec will produce a vector for a term and then more work may need to be done to convert that set of vectors into a singular vector or other format. Additionally TF-IDF does not take into consideration the context of the words in the corpus whereas word2vec does.
BERT is an ML/NLP technique developed by Google that uses a transformer based ML model to convert phrases, words, etc into vectors. Key differences between TF-IDF and BERT are as follows: TF-IDF does not take into account the semantic meaning or context of the words whereas BERT does. Also BERT uses deep neural networks as part of its architecture, meaning that it can be much more computationally expensive than TF-IDF which has no such requirements.
In the embedding approach, we broke down the interviews into full dialogue turns (exchanges beginning with an interviewer prompt and followed by any number of participant sentences until the interviewer speaks again for the next turn). We generated a single embedding ei for the entire Interviewer dialogue turn ti; we did this by generating BERT embeddings for each word in t, and computed the mean embedding across all words. From there, we generated a sentence-level BERT embedding esj for each sentence sj in the subsequent subject turn and calculated the mean difference between ei and esj. The intuition behind this is that if tangentiality or derailment is present, participant responses are likely to move further away from the initial interview prompt compared to coherent exchanges. Simple linear regression models were fit on sentence-wise embedding distance to compare the slopes and intercepts of the embedding trajectories for the two groups. As BERT is specifically trained on sentence-to-sentence prediction, our analyses were done on the sentence-level and within-sentence incoherence was not analyzed.
Using morphology - defining functions of individual words, NLP tags each individual word in a body of text as a noun, adjective, pronoun, and so forth. What makes this tagging difficult is that words can have different functions depending on the context they are used in. For example, "bark" can mean tree bark or a dog barking; words such as these make classification difficult.
There are probably other small differences that I missed, but, after having read the paper Attention is all you need and quickly read some parts of the BERT paper, these seem to be the main differences.
Machine learning and deep learning often seem like interchangeable buzzwords, but there are differences between them. So, what exactly are these two concepts that dominate conversations about AI, and how are they different? Read on to find out.
ChatGPT and GPT-3 are two of the most advanced language processing models developed by OpenAI, an AI research and development company based in San Francisco, California. Both models utilize deep learning capabilities to produce human-like text, which makes them especially suitable for a wide range of language processing tasks like language translation, summarization, and text generation. However, despite their shared similarities, they have several key differences that make them suitable for different types of tasks.
Before comparing the differences between the two language models, it is important to know what they are in the first place. ChatGPT  is a large language model that was developed based on the GPT-3.5 language model. This incredible model can interact in the form of a conversational dialogue and provide human-like responses.
This makes multiple language NLP apps challenging. It takes a lot of labelled data, processes the information, learns patterns and produces prediction models. When we need to build NLP on a text containing different languages, we may look at multilingual word embeddings for NLP models that can effectively scale.
NLP stands for Natural Language Processing. It's the technology that allows chatbots to communicate with people in their own language. In other words, it's what makes a chatbot feel human. NLP achieves this by helping chatbots interpret human language the way a person would, grasping important nuances like a sentence's context.
Machine learning is a subfield of Artificial Intelligence (AI), which aims to develop methodologies and techniques that allow machines to learn. Learning is carried out through algorithms and heuristics that analyze data by equating it with human experience. This makes it possible to develop programs that are capable of identifying patterns in data.
One main difference is that the input sequence can be passed parallelly so that GPU can be used effectively and the speed of training can also be increased. It is also based on the multi-headed attention layer, so it easily overcomes the vanishing gradient issue. The paper applies the transformer to an NMT.
The feed-forward network accepts attention vectors one at a time. And the best thing here is, unlike the case of the RNN, each of these attention vectors is independent of one another. So, we can apply parallelization here, and that makes all the difference. 041b061a72