We measured the performance of the parsers using the following metrics: unlabeled attachment score ( UAS ), labeled attachment score (LAS) and complete match (CM), which were defined by Hall et al.

Type Systems UAS CM Yamada and Matsumoto (2003) 90.3 38.7 McDonald et a1.

UAS Score (°/o) S S N 00

Case 84.1 85.6 74.3 76.5 Degree 97.9 98.0 90.1 90.1 UAS 67.4 68.7 — —

i all all non-null non-null POS 94.9 95.7 94.9 95.7 Person 98.7 99.0 92.2 94.6 Number 97.4 97.9 96.5 97.1 Tense 96.8 97.2 84.1 86.8 Mood 97.9 98.3 91.4 93.2 Voice 97.8 98.0 91.3 92.4 Gender 95.4 96.1 90.7 91.9 Case 95.9 96.3 92.0 92.6 Degree 99.8 99.9 33.3 55.6 UAS 68.0 70.5 — —

Tense 98.9 99.3 97.2 97.3 Mood 98.7 99.2 95.8 97.3 Case 96.7 97.0 94.5 94.9 Degree 97.9 98.1 87.5 88.6 UAS 78.2 78.8 — —

i all all non-null non-null POS 94.4 94.5 94.4 94.5 Person 99.4 99.5 97.1 97.6 Number 95.3 95.9 93.7 94.5 Tense 98.0 98.2 93.2 93.9 Mood 98.1 98.3 93.8 94.4 Voice 98.5 98.6 95.3 95.7 Gender 93.1 93.9 87.7 89.1 Case 89.3 90.0 79.9 81.2 Degree 99.9 99.9 86.4 90.8 UAS 61.0 61.9 — —

Use All Prefixes (UA): UA produces a cluster feature at every available bit length with the hope that the underlying supervised system can learn proper weights of different cluster features during training.

For example, if the full bit representation of “Apple” is “000”, UA would produce three cluster features: prefix] =0, prefix2=00 and prefix3=000.

UA 71.19 +0.49 1.5

Table 6 shows that all the 4 proposed methods improved baseline performance, with UA as the fastest and ES as the slowest.