1. 1 CSC 594 Topics in AI – Text Mining and Analytics Fall 2015/16 3. Word Association

2. What is Word Association? Word association is a relation that exists between two words. There are two types of relations: Paradigmatic and Syntagmatic. Paradigmatic: A & B have paradigmatic relation if they can be substituted for each other (i.e., A & B are in the same class) –e.g. “cat” and “dog”; “Monday” and “Tuesday” Syntagmatic: A & B have syntagmatic relation if they can be combined with each other (i.e., A & B are related semantically) –e.g. “cat” and “scratch”; “car” and “drive” These two basic and complementary relations can be generalized to describe relations of any items in a language Coursera “Text Mining and Analytics”, ChengXiang Zhai 2

3. 3 Why Mine Word Associations? They are useful for improving accuracy of many NLP tasks –POS tagging, parsing, entity recognition, acronym expansion –Grammar learning They are directly useful for many applications in text retrieval and mining –Text retrieval (e.g., use word associations to suggest a variation of a query) –Automatic construction of topic map for browsing: words as nodes and associations as edges –Compare and summarize opinions (e.g., what words are most strongly associated with “battery” in positive and negative reviews about iPhone 6, respectively?)

4. Word Context Coursera “Text Mining and Analytics”, ChengXiang Zhai 4

5. Word Co-occurrence Coursera “Text Mining and Analytics”, ChengXiang Zhai 5

6. Mining Word Associations Paradigmatic –Represent each word by its context –Compute context similarity –Words with high context similarity likely have paradigmatic relation Syntagmatic –Count how many times two words occur together in a context (e.g., sentence or paragraph) –Compare their co-occurrences with their individual occurrences –Words with high co-occurrences but relatively low individual occurrences likely have syntagmatic relation Paradigmatically related words tend to have syntagmatic relation with the same word  joint discovery of the two relations These ideas can be implemented in many different ways! Coursera “Text Mining and Analytics”, ChengXiang Zhai 6

7. Word Context as “Pseudo Document” Coursera “Text Mining and Analytics”, ChengXiang Zhai 7

8. Computing Similarity of Word Context Coursera “Text Mining and Analytics”, ChengXiang Zhai 8

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10. Syntagmatic Relation – Word Collocation Syntagmatic relation is word co-occurrence – called Collocation –If two words occur together in a context more often than chance, they are in the syntagmatic relation (i.e., related words). Coursera “Text Mining and Analytics”, ChengXiang Zhai 10

11. Word Probability Word probability – how likely would a given word appear in a text/context? Coursera “Text Mining and Analytics”, ChengXiang Zhai 11

12. Binomial Distribution Word (occurrence) probability is modeled by Binomial Distribution. Coursera “Text Mining and Analytics”, ChengXiang Zhai 12

13. Entropy as a Measure of Randomness Entropy is a measure in Information Theory, and indicates purity or (un)even/skewed distribution -- a large entropy means the distribution is even/less skewed. Entropy takes on a value [0, 1] (between 0 and 1 inclusive). Entropy of a collection S with respect to the target attribute which takes on c number of values is calculated as: This is the average number of bits required to encode an instance in the dataset. For a boolean classification, the entropy function yields: 13

14. Entropy for Word Probability Coursera “Text Mining and Analytics”, ChengXiang Zhai 14

15. Mutual Information (MI) as a Measure of Word Collocation Mutual Information is a concept in probability theory, and indicates the two random variables' mutual dependence – or the reduction of entropy. How much reduction in the entropy of X can we obtain by knowing Y? (where reduction give more predictability) 15

16. Mutual Information (MI) and Word Collocation 16 Coursera “Text Mining and Analytics”, ChengXiang Zhai 16

17. Probabilities in MI 17 Coursera “Text Mining and Analytics”, ChengXiang Zhai 17

18. Estimation of Word Probability 18 Coursera “Text Mining and Analytics”, ChengXiang Zhai 18

19. Point-wise Mutual Information http://www.let.rug.nl/nerbonne/teach/rema-stats-meth-seminar/presentations/Suster-2011-MI-Coll.pdf 19 Point-wise Mutual Information (PMI) is often used in place of MI. PMI is a specific event of the two random variables.

20. Other Word Collocation Measures 20

21. Conditional Counts: Concept Linking diabetes (63/63) Concept linked term: a term that co-occurs with a centered term  In this diagram, the centered term is diabetes, which occurred in 63 documents. The term insulin (and its stemmed variations) occurred in 58 documents, 14 of which also contained diabetes. +insulin (14/58) Centered term: a term that is chosen to investigate

22. continued... The term diabetes occurs in 63 documents.

23. The term insulin and its variants occur in 58 documents, and 14 of those documents also contain the term diabetes.

24. Terms that are primary associates of insulin are secondary associates of diabetes.