@ -20,7 +20,6 @@ This is possible because a coder wrote a program to do this. A few decades ago,
At this point, you may be remembering school classes where the teacher covered the parts of grammar in a sentence. In some countries, students are taught grammar and linguistics as a dedicated subject, but in many, these topics are included as part of learning a language: either your first language in primary school (learning to read and write) and perhaps a second language in post-primary, or high school. Don't worry if you are not an expert at differentiating nouns from verbs, or adverbs from adjectives!
If you struggle with the difference between the *simple present* and *present progressive*, you are not alone. This is a challenging thing for many people, even native speakers of a language. The good news is that computers are really good at applying formal rules, and you will learn to write code that can *parse* a sentence as well as a human. The greater challenge you will examine later is understanding the *meaning*, and *sentiment*, of a sentence.
## Prerequisites
For this lesson, the main prerequisite is being able to read and understand the language of this lesson. There are no math problems or equations to solve. While the original author wrote this lesson in English, it is also translated into other languages, so you could be reading a translation. There are examples where a number of different languages are used (to compare the different grammar rules of different languages). These are *not* translated, but the explanatory text is, so the meaning should be clear.
@ -43,7 +42,9 @@ In this section, you will need:
## Conversing with Eliza
The history of trying to make computers understand human language goes back decades, and one of the earliest scientists to consider natural language processing was *Alan Turing*. When Turing was researching *Artificial Intelligence* in the 1950's, he considered if a conversational test could be given to a human and computer (via typed correspondence) where the human in the conversation was not sure if they were conversing with another human or a computer. If, after a certain length of conversation, the human could not determine that the answers were from a computer or not, then could the computer be said to be *thinking*?
The history of trying to make computers understand human language goes back decades, and one of the earliest scientists to consider natural language processing was *Alan Turing*. When Turing was researching *Artificial Intelligence* in the 1950's, he considered if a conversational test could be given to a human and computer (via typed correspondence) where the human in the conversation was not sure if they were conversing with another human or a computer. If, after a certain length of conversation, the human could not determine that the answers were from a computer or not, then could the computer be said to be *thinking*?
[](https://youtu.be/QD8mQXaUFG4 "Chatting with Eliza")
The idea for this came from a party game called *The Imitation Game* where an interrogator is alone in a room and tasked with determining which of two people (in another room) are male and female respectively. The interrogator can send notes, and must try to think of questions where the written answers reveal the gender of the mystery person. Of course, the players in the other room are trying to trick the interrogator by answering questions in such as way as to mislead or confuse the interrogator, whilst also giving the appearance of answering honestly.
@ -51,9 +52,9 @@ In the 1960's an MIT scientist called *Joseph Weizenbaum* developed [*Eliza*](ht
> Note: You can read the original description of [Eliza](https://cacm.acm.org/magazines/1966/1/13317-elizaa-computer-program-for-the-study-of-natural-language-communication-between-man-and-machine/abstract) published in 1966 if you have an ACM account. Alternately, read about Eliza on [wikipedia](https://en.wikipedia.org/wiki/ELIZA)
### Task: Coding a simple conversational bot
### Task: Coding a basic conversational bot
A conversational bot, like Eliza, is a program that elicits user input and seems to understand and respond intelligently. Unlike Eliza, our bot will not have several rules giving it the appearance of having an intelligent conversation. Instead, out bot will have one ability only, to keep the conversation going with random responses that might work in just about any trivial conversation.
A conversational bot, like Eliza, is a program that elicits user input and seems to understand and respond intelligently. Unlike Eliza, our bot will not have several rules giving it the appearance of having an intelligent conversation. Instead, out bot will have one ability only, to keep the conversation going with random responses that might work in almost any trivial conversation.
Your steps when building a conversational bot:
@ -62,7 +63,7 @@ Your steps when building a conversational bot:
1. Accept user input
2. If user has asked to exit, then exit
3. Process user input and determine response (in this case, the response is a random choice from a list of possible generic responses)
4. Print response and
4. Print response
3. loop back to step 2
Create this bot yourself in Python with the following random responses:
@ -76,7 +77,7 @@ random_responses = ["That is quite interesting, please tell me more.",
"Did you catch the game last night?"]
```
Here is some sample output to guide you (user input is on the lines starting with >):
Here is some sample output to guide you (user input is on the lines starting with `>`):
```
Hello, I am Marvin, the simple robot.
@ -117,7 +118,6 @@ In the next lesson, you'll learn about a number of other approaches to parsing n
## Review & Self Study
Take a look at the references below as further reading opportunities.
### References
1. Schubert, Lenhart, "Computational Linguistics", *The Stanford Encyclopedia of Philosophy* (Spring 2020 Edition), Edward N. Zalta (ed.), URL = <https://plato.stanford.edu/archives/spr2020/entries/computational-linguistics/>.
Jen Looper
3 years ago