Sentence completion

1
Sentence completion

• Korinna Grabski Tobias Scheffer. Sentence
  completion.In the 27th Annual International ACM
  SIGIR Conference (SIGIR'2004), 2004.
• Presenter Suhan Yu

2
Introduction
Dont propose Propose All queries
Q query (The first words of sentences)
Dont propose
No
Retrieval algorithm
gt threshold ?
similarity
Propose
S retrieved sentence
a specific document collection
Yes
Cluster algorithm
accepted
Unaccepted
3
Problem Setting

• Problem setting
• Given a domain specific document collection.
• Given an initial document fragment.
• The sentence completion problem is to identify
  the remaining part of the sentence that the user
  currently intends to write.
• This problem setting can be generalized along
  several dimension
• Consider additional attributes of the current
  communication process.
• More natural to predict the uncertainty about the
  remaining words of the sentence increase.

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evaluate

• Evaluate the performance of sentence completion
  methods

5
Retrieval algorithm
length l
The number of sentences containing term t i
The jth sentence
6
Indexing algorithm

• Inverted index structure over the data
• Sorted
• appears in the document collection more
  frequently than
• Appear equally frequent and is
  alphabetically smaller than

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Retrieval algorithm

• Similarity between f (query) and s (sentence)

Between 01
query
8
Retrieval algorithm
9
Data compression by clustering

• Run EM algorithm with
• mixtures of two Gaussian
• model recursively.
• Each data element is assigned to the
• cluster with higher likelihood.
• Give a threshold, if a cluster falls below
• a threshold, or the variance within the
  cluster falls below a variance threshold
• The result of the clustering algorithm is a tree
  of clusters.

100
56
44
20
36
39
5
10
Data compression by clustering
fragment
100
56
44
36
39
20
24
15
15
21
15
16
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Data compression by clustering

• Characteristic sentences from the ten largest
  clusters.

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Empirical studies

• Use two collections
• Collection A
• Provided by an online education provider and
  contains emails that have been sent to students.
• Collection B
• Provided by a large online shop and contains
  emails that have been sent in reply to customer
  requests.
• Two collection have same size, around 10000
  sentences.
• Random split into a training set (75) and a test
  set (25).

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Empirical studies

• Collection A

14
Empirical studies

• Collection B

15
Empirical studies
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Conclusion

• Comparing the retrieval to the clustering
  approach we can conclude that the retrieval
  method, on average, has higher precision and
  recall.
• This paper investigate on methods that may
  predict some succeeding words, but not
  necessarily the complete remainder of the current
  sentence.
• Your order proceeds as will be shipped on but
  it may not be possible to predict whether the
  final word is Monday or Tuesday.