Advanced uses of NLP
Most of the NLP that you will do on a day-to-day basis will probably fall into one of the simpler uses, but let’s also discuss a few advanced uses. In some cases, what I am describing as advanced uses is really a combination of simpler uses to deliver a more complicated solution. So, let’s discuss some of the more advanced uses, such as chatbots and conversational agents, language modeling, information retrieval, text summarization, topic discovery and modeling, text-to-speech and speech-to-text conversion, MT, and personal assistants.
Chatbots and conversational agents
A chatbot is a program that can hold a conversation with its user. These have existed for years, with the earliest chatbots being created in the 1960s, but they have been steadily improving and are now an effective means of forwarding a user to a more specific form of customer support, for instance. If you go to a website’s support section, you may be presented with a small chatbox popup that’ll say something like, “What can we help you with today?”. You may then type, “I want to pay off my credit card balance.” When the application receives your answer, it’ll be able to use it to determine the correct form of support that you need.
While a chatbot is built to handle human text, a conversational agent can handle voice audio. Siri and Alexa are examples of conversational agents. You can talk to them and ask them questions.
Chatbots and conversational agents are not limited to text, though; we often run into similar switchboards when we call a company with our phones. We will get the same set of questions, either looking for a word answer or numeric input. So, behind the scenes, where voice is involved, there will be a voice-to-text conversion element in place. Applications also need to determine whether someone is asking a question or making a statement, so there is likely text classification involved as well.
Finally, to provide an answer, text summarization could convert search results into a concise statement to return as text or speech to the user to complete the interaction.
Chatbots are not just rudimentary question-and-answer systems, though. I believe that they will be an effective way for us to interact with text. For instance, you could build a chatbot around the book Alice’s Adventures in Wonderland (or the Bible) to give answers to questions specifically about the book. You could build a chatbot from your own private messages and talk to yourself. There’s a lot of room for creativity here.
Language modeling
Language modeling is concerned with predicting the next word given a sequence of words. For instance, what would come after this: “The cow jumped over the ______.” Or this: “Green eggs and _____.” If you go to Google and start typing in the search bar, take note that the next predicted word will show in a drop-down list to speed up your search.
We can see that Google has predicted the next word to be ham but that it is also finding queries that are related to what you have already typed. This looks like a combination of language modeling as well as clustering or topic modeling. They are looking to predict the next word before you even type it, and they are even going a step further to find other queries that are similar to the text you have already typed.
Data scientists are also able to use language modeling as part of the creation of generative models. In 2020, I trained a model on thousands of lines of many Christmas songs and trained it to write Christmas poetry. The results were rough and humorous, as I only spent a couple of days on it, but it was able to take seed text and use it to create entire poems. For instance, seed text could be, “Jingle bells,” and then the model would continuously take previous text and use it to create a poem until it reached the end limits for words and lines. Here is my favorite of the whole batch:
youre the angel and the manger is the king of kings and sing the sea of door to see to be the christmas night is the world and i know i dont want to see the world and much see the world is the sky of the day of the world and the christmas is born and the world is born and the world is born to be the christmas night is the world is born and the world is born to you to you to you to you to you to a little child is born to be the world and the christmas tree is born in the manger and everybody sing fa la la la la la la la la la la la la la la la la la la la la la la la la la la la la la la
I built the generative process to take a random first word from any of the lines of the training data songs. From there, it would create lines of 6 words for a total of 25 lines. I only trained it for 24 hours, as I wanted to quickly get this done in time for Christmas. There are several books on creating generative models, so if you would like to use AI to augment your own creativity, then I highly suggest looking into them. It feels like a collaboration with a model rather than replacing ourselves with a model.
These days, generative text models are becoming quite impressive. ChatGPT – released in November 2022 – has grabbed the attention of so many people with its ability to answer most questions and give realistic-looking answers. The answers are not always correct, so generative models still have a long way to go, but there is now a lot of hype around generative models, and people are considering how they can use them in their own work and lives and what they mean for our future.
Text summarization
Text summarization is pretty much self-explanatory. The goal is to take text as input and return a summary as output. This can be very powerful when you are managing thousands or millions of documents and want to provide a concise sentence about what is in each. It is essentially returning similar to an “abstract” section you would find in an academic article. Many of the details are removed, and only the essence is returned.
However, this is not a perfect art, so be aware that if you use this, the algorithm may throw away important concepts from the text while keeping those that are of less importance. ML is not perfect, so keep an eye on the results.
However, this is not for search so much as it is for returning a short summary. You can use topic modeling and classification to determine document similarity and then use this to summarize the final set of documents.
If you were to take the content of this entire book and feed it to a text summarization algorithm, I would hope that it would capture that the marriage of NLP and network analysis is powerful and approachable by everyone. You do not need to be a genius to work with ML, NLP, or social network analysis. I hope this book will spark your creativity and make you more effective at problem solving and thinking critically about things. There are a lot of important details in this text, but that is the essence.
Topic discovery and modeling
Topic discovery and modeling is very similar to clustering. This is used in Latent Semantic Indexing (LSI), which can be powerful for identifying themes (topics) that exist in text, and it can also be an effective preprocessing step for text classification, allowing models to be trained on context rather than words alone. I mentioned previously, in the Clustering and Community detection subsections, how this could be used to identify subtle groups within communities based on the words and hashtags they place into their account descriptions.
For instance, topic modeling would find similar strings in topics. If you were to do topic modeling on political news and social media posts, you will notice that in topics, like attracts like. Words will be found with other similar words. For instance, 2A may be spelled out as the Second Amendment, USA may be written in its expanded form (United States of America), and so on.
Text-to-speech and speech-to-text conversion
This type of NLP model aims to convert text into speech audio or audio into text transcripts. This is then used as input into classification or conversational agents (chatbots, personal assistants).
What I mean by that is that you can’t just feed audio to a text classifier. Also, it’s difficult to capture context from audio alone without any language analysis components, as people speak in different dialects, in different tones, and so on.
The first step is often transcribing the audio into text and then analyzing the text itself.
MT
Judging by the history of NLP, I think it is safe to say that translating from language A to language B has probably been on the minds of humans for as long as we have had to interact with other humans who use a different language. For instance, there is even a story in the Bible about the Tower of Babel and we lost the ability to understand each other’s words when it was destroyed. MT has so many useful implications, for collaboration, security, and even creativity.
For instance, for collaboration, you need to be able to share knowledge, even if team members do not share the same language. In fact, this is useful anywhere that sharing knowledge is desirable, so you will often find a see translation link in social media posts and comments. Today, MT seems almost perfect, though there are occasionally entertaining mistakes.
For security, you want to know what your enemies are planning. Spying is probably not very useful if you can’t actually understand what your enemies are saying or planning on doing. Translation is a specialized skill and is a long and manual process when humans are involved. MT can greatly speed up analysis, as the other language can be translated into your own.
And for creativity, how fun would it be to convert text from one language into your own created language? This is completely doable.
Due to the importance of MT and text generation, massive neural networks have been trained to handle text generation and MT.
Personal assistants
Most of us are probably aware of personal assistants such as Alexa and Siri, as they have become an integral part of our lives. I suspect we are going to become even more dependent on them, and we will eventually talk to our cars like on the old TV show Knight Rider (broadcast on TV from 1982 to 1986). “Hey car, drive me to the grocery store” will probably be as common as “Hey Alexa, what’s the weather going to be like tomorrow?”
Personal assistants use a combination of several NLP techniques previously mentioned. They may use classification to determine whether your query is a question or a statement. They may then search the internet to find web content most related to the question that you asked. It can then capture the raw text from one or more of the results and then use summarization to build a concise answer. Finally, it will convert text to audio and speak the answer back to the user.
Personal assistants use a combination of several NLP techniques mentioned previously:
- They may use classification to determine whether your query is a question or a statement.
- They may then search the internet to find web content that is most related to the question that you asked.
- They can then capture the raw text from one or more of the results and then use summarization to build a concise answer.
- Finally, they will convert text to audio and speak the answer back to the user.
I am very excited about the future of personal assistants. I would love to have a robot and car that I can talk to. I think that creativity is probably our only limitation for the different types of personal assistants that we can create or for the modules that they use.
