Brent Kievit-Kylar Indiana University. A Visual Word Similarity Tool How can two words be compared?...

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Brent Kievit-Kylar Indiana University

Transcript of Brent Kievit-Kylar Indiana University. A Visual Word Similarity Tool How can two words be compared?...

Brent Kievit-KylarIndiana University

A Visual Word Similarity Tool

• How can two words be compared?– Similar letters (dog, god)– Similar looking objects (dog, wolf)– Similar context (dog, food)– Similar part of speech (dog, bird)– Similar location ([hot]dog, [hot]potato) – Similar meaning (dog, ?)

Context is Important

• Two words can be more or less similar depending on the context in which this question is asked.– At dinner: “plate” and “fork”– At baseball game: “plate” and “run”

• Even if the word is representative of the same object– At play: “paper” and “airplane”– At work: “paper” and “pen”

Natural Language Processing

• In NLP, we make tools to understand language.

• Try to make it “think” in the same way as humans do.

• Many algorithms to learn language representations from data.– No good way to compare algorithms /

learning data, or see how well they work.

NLP Visualization

• Word 2 Word visualizes these word similarity metrics.

• Network visualization where each word is a node and relations are connecting edges.

• Easy to see many words and relationships at the same time and compare them.

Visualizing (Step 1)

• Select “Comparator”.– Choose a similarity comparison

algorithm from a list of 24 well known word similarity metrics.

– Teach it by giving it text to read.• From a document• From the web• By entering text yourself

– Select filters (lower case, web, etc.)

Visualizing (Step 2)

• Select Words– Many useful tools to select the words

you wish to see.– Each “comparator” remembers the

words that it has learned.

Visualizing (Step 3)

• Select Layout and Visualize– Different layout managers make

organizing the words simple and powerful.

– Words can also be move with the mouse by clicking and dragging.

– The world can be moved or zoomed to explore the semantic space.

Sample Results