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PASCAL Challenges Workshop 2

Two-step Approach to Unsupervised Morpheme Segmentation

author: Stefan Bordag, University of Leipzig

Description

This paper describes two steps of a morpheme boundary segmentation algorithm. The task is solely to find boundaries between morphemes bar and any further analysis such as phoneme deletions, insertions or alternations that may occur between or within morphemes. The algorithm presented here was designed under the premise that it is not supposed to utilize any knowledge about the language it should analyse. Neither is it supposed to rely on any kind of human supervision. The first step is to use a high-precision, low-recall algorithm to find a relatively small number of mostly correct segmentations, see (Bordag, 2005). In the second step, these segmentations are used to train a classification, which is then applied to all words to find morpheme boundaries within them.

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Slides
0:00 Unsupervised Knowledge-Free Morpheme Boundary Detection
1:23 Example: clearly early
2:26 1. Three approaches to morpheme boundary detection
2:27 2. New solution in two parts
3:14 2.1. First part: Generating training data with LSV and distributed Semantics
3:29 Neighbors of “clearly“
3:55 2.2.New solution as combination of two existing approaches
4:15 Similar words to “clearly“
4:28 2.3. New solution as combination of two existing approaches
4:38 Similar words to “clearly“ sorted by edit distance
5:42 2.4. New solution as combination of two existing approaches
5:50 Similar words to “clearly“ sorted by edit distance
5:53 2.4. New solution as combination of two existing approaches
5:53 2.5. Letter successor variety
7:19 2.5.1. Balancing factors
8:01 2.5.2. Balancing factors: Frequency
8:42 2.5.3. Balancing factors: Multiletter Phonemes
9:29 2.5.4. Balancing factors: Bigrams
11:13 2.5.3. Balancing factors: Multiletter Phonemes
11:30 2.5.4. Balancing factors: Bigrams
11:55 2.5.5. Sample computation
15:34 3. Second Part: Training and Applying classifier
16:06 3.1. PCT as a Classificator
17:48 4. Evaluation
19:02 Evaluating LSV Precision vs. Recall
19:04 Evaluating LSV F-measure
19:06 Evaluating combination Precision vs. Recall
19:08 Evaluating combination F-measure
20:08 Comparing combination with global LSV
20:09 4.1. Results
20:11 4.2. Statistics
21:53 4.3. Assessing true error rate
22:09 4.4. Real example
22:52 5. Further research
23:51 6. References
23:53 6. References II
31:43 - Questions

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