Two-step Approach to Unsupervised Morpheme Segmentation
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.
| 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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