## 4 Experiments and Results
### Additional Analysis
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Case 2**} \\ \hline
**Model** & **Result** \\ \hline Sentence & [CLS] He found the lane to the farm and drove up into the farm \#yard, where he was \\  & met by the farmer [SEP] \\ \hline Gold & (1, 1, “He’, ’PER’) (3, 7, ’the lane to the farm’, ’FAC’) (6, 7, ’the farm’, ’FAC’) (12, 22, ’the farm \#yard, \\  & where he was met by the farmer’, ’FAC’) (16, 16, ’where’, ’FAC’) (17, 17, ’he’, ’PER’) \\  & (21, 22, ’the farmer’, ’PER’) \\ \hline Biaffine & **(1, 1, “He’, ’PER’)** (16, 16, ’where’, ‘LOC’) (17, 17, ’he’, ’PER’) \\ \hline \(\text{LDE}_{\text{w Label Feature}}\) & **(1, 1, ’He’, ’PER’)** (1, 17, ’He found the lane to the farm and drove up into the \\  & farm \#fyard, where he’, ’PER’) \\ \hline \(\text{LDE}_{\text{w Label Word}}\) & **(1, 1, ’He’, ’PER’)** (16, 16, ’where’, ‘LOC’) (17, 17, ’he’, ’PER’) (21, 22, ’the farmer’, ’PER’) \\ \hline \(\text{LDE}_{\text{w Label Description}}\) & **(1, 1, ’He’, ’PER’)** (3, 7, ’the lane to the farm’, ’FAC’) (6, 7, ’the farm’, ‘LOC’) (16, 16, ’where’, ‘LOC’) (17, 17, ’he’, ’PER’) (21, 22, ’the farmer’, ’PER’)** \\ \hline \multicolumn{2}{c}{**Case 3**} \\ \hline
**Model** & **Result** \\ \hline Sentence & [CLS] You see that barge down there on the river ’[SEP] \\ \hline Gold & (1, 1, ’You’, ’PER’) (3, 9, ’that barge down there on the river’, ’VEH’) (8, 9, ’the river’, ’LOC’) \\ \hline Biaffine & (6, 6, ’there’, ’LOC’) \\ \hline \(\text{LDE}_{\text{w Label Feature}}\) & \\ \hline \(\text{LDE}_{\text{w Label Word}}\) & \\ \hline \(\text{LDE}_{\text{w Label Description}}\) & (3, 9, ’that barge down there on the river’, ’FAC’) (6, 6, ’there’, ’LOC’) **(8, 9, ’the river’, ’LOC’)** \\ \hline \multicolumn{2}{c}{**Case 4**} \\ \hline
**Model** & **Result** \\ \hline Sentence & [CLS] All \##egation \#\#s have come to light that several OS \##U players received illegal benefits \\  & including cash, access to cars, etc. [SEP] \\ \hline Gold & (9, 12, ’several OS \#U players’, ’PER’) (10, 11, ’OS \#U’, ’ORG’) (21, 21, ’cars’, ’VEH’) \\ \hline Biaffine & \\ \hline \(\text{LDE}_{\text{w Label Feature}}\) & (9, 11, ’several OS \##U’, ’PER’) (9, 12, ’several OS \#U players’, ’PER’) \\ \hline \(\text{LDE}_{\text{w Label Word}}\) & **(9, 12, ’several OS \#U players’, ’PER’)** \\ \hline \(\text{LDE}_{\text{w Label Description}}\) & **(9, 12, ’several OS \#\#U players’, ’PER’)** (10, 11, ’OS \#\#U’, ’ORG’)** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._Case 3 provides a compelling example of the model's capability to handle rare entity types. The gold standard identifies \"that barge down there on the river\" as a vehicle entity with \"the river\" nested within it as a location. While most model variants fail to recognize anything beyond basic location entities, the \(\mathrm{LDE_{w}}\) Label Description model successfully identifies both the outer entity (though misclassified as a facility) and the nested location entity. This demonstrates how label descriptions provide contextual clues that assist in recognizing uncommon entity types, even with minimal training examples. Finally, Case 4 shows a perfect nested entity recognition scenario where \"several OS ##U players\" contains the organization \"OS ##U\" within it. Only the \(\mathrm{LDE_{w}}\) Label Description model correctly identifies both the outer person entity and the nested organization entity, capturing the hierarchical relationship exactly as in the gold standard. This precision in detecting nested structures highlights how the semantic richness of label descriptions enables the model to better understand entity boundaries and their relationships.