Chinese researchers have built a deep learning machine that outperforms the average human ability to answer verbal reasoning questions for the first time.
They took each word and looked for other words that often appear nearby in a large corpus of text. They then use an algorithm to see how these words are clustered. The final step is to look up the different meanings of a word in a dictionary and then to match the clusters to each meaning.
This can be done automatically because the dictionary definition includes sample sentences in which the word is used in each different way. So by calculating the vector representation of these sentences and comparing them to the vector representation in each cluster, it is possible to match them.
The overall result is a way of recognizing the multiple different senses that some words can have.
They also identify the category of each question so that the computer then knows which answering strategy it should employ. This is straightforward since the questions in each category have similar structures.
Just over 100 years ago, the German psychologist William Stern introduced the intelligence quotient test as a way of evaluating human intelligence. Since then, IQ tests have become a standard feature of modern life and are used to determine children’s suitability for schools and adults’ ability to perform jobs.
These tests usually contain three categories of questions: logic questions such as patterns in sequences of images, mathematical questions such as finding patterns in sequences of numbers and verbal reasoning questions, which are based around analogies, classifications, as well as synonyms and antonyms.
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Arxiv - Solving Verbal Comprehension Questions in IQ Test by Knowledge-Powered Word Embedding
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They took each word and looked for other words that often appear nearby in a large corpus of text. They then use an algorithm to see how these words are clustered. The final step is to look up the different meanings of a word in a dictionary and then to match the clusters to each meaning.
This can be done automatically because the dictionary definition includes sample sentences in which the word is used in each different way. So by calculating the vector representation of these sentences and comparing them to the vector representation in each cluster, it is possible to match them.
The overall result is a way of recognizing the multiple different senses that some words can have.
They also identify the category of each question so that the computer then knows which answering strategy it should employ. This is straightforward since the questions in each category have similar structures.
Just over 100 years ago, the German psychologist William Stern introduced the intelligence quotient test as a way of evaluating human intelligence. Since then, IQ tests have become a standard feature of modern life and are used to determine children’s suitability for schools and adults’ ability to perform jobs.
These tests usually contain three categories of questions: logic questions such as patterns in sequences of images, mathematical questions such as finding patterns in sequences of numbers and verbal reasoning questions, which are based around analogies, classifications, as well as synonyms and antonyms.
Arxiv - Solving Verbal Comprehension Questions in IQ Test by Knowledge-Powered Word Embedding
Read more »
