Term Candidate Sample Clauses

Term Candidate. Identification As just explained, term candidates are identified by running the P2G tool on the phrase tables of the adapted DCU system (v1) First attempt The P2G tool was run on the adapted DCU phrase table (v1), with a threshold of P(tl|sl) of 0.6. The result was a term candidate list of 101,400 entries. A first look into the data, sorted by frequency, showed that the majority of entries of the highest frequency was incorrect, so the data could not be simply used. Examples are given in fig. 3-4; the main reason is incorrect single word – multiword correspondences. Adaptation of the P2G extraction tool Two improvement measures were taken to get a useful glossary: In addition to the probability P(tl|sl) of 0.6, term candidates must also meet the inverse relation P(sl|tl) of 0.6. Also, the frequencies of source and target term should be in a ‘good’ relation: Obviously, a term candidate with source frequency of 1000 and target frequency of 2 is not a good candidate pair. To find out a sound relationship between source and target frequency, a glossary of automotive terms was extracted from the DCU v2 phrase table, and manually evaluated for errors. Then several extraction runs were made, restricting the distance in frequency from the arithmetic means of source and target frequency, from 20% (frequencies must be rather similar) to 90% (frequencies can be quite different).The idea is to increase precision by using more restrictive frequency distance. The result is shown in table 3-6. Table 3-6: recall and precision for different frequency distances No restriction in difference creates a glossary of 2600 terms, the most restrictive measure the glossary is 1100 entries big. However, the reduction in recall does not improve precision; there is only an increase in precision if a 90% distance is used as opposed to no distance change at all. Too restricted frequency distance only reduces recall but does not increase precision. The best compromise then seems to be to use a 0.9 distance threshold; it increases precision a bit, and reduces recall not so much. Second attempt As a consequence of the tests with frequency, the P2G tool was re-run with P(t|s) = 0.6, P(s|t) = 0.6, and FRQdistance = 0.9. The run resulted in a term candidate list of about 35,100 entries6. As such a candidate list will contain incorrect entries, the candidates with the highest frequency (all entries with frequency > 40) were inspected manually. Extraction quality is given in table 3-7. errors nr...
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