S15-1035 discourse deixis engine on top of NP coreference resolution . 2 Related Work Coreference
J08-3002 systems on the non-pronominal NP coreference resolution . We used as the baseline the
W06-0302 differs from traditional supervised NP coreference resolution in two important aspects . First
P09-1074 performance results reported for NP coreference resolution . For our investigations , we
P09-1074 studies complexity issues for NP coreference resolution using a " good " , i.e. near
P09-1074 examine the state-of-the-art in NP coreference resolution . We show the relative impact
W06-1640 resolution task can benefit from NP coreference resolution training data from a different
S15-1035 is independent from the NP -- NP coreference resolution component : competition between
P09-1074 statements about the state-of-theart in NP coreference resolution . In particular , it remains
W97-1309 extremely low compared to the NP coreference resolution result 65 because in the case
P04-1017 The module is a duplicate of the NP coreference resolution system by Soon et al. ( 2001
W12-2501 2010 ) . The task of automatic NP coreference resolution is to determine " which NPs in
C04-1033 string-matching is a crucial factor for NP coreference resolution . As shown in the flgure , the
W15-1503 involved anaphora resolution and NP coreference resolution ( Or˘asan et al. , 2008
P02-1014 Lappin and Leass ( 1994 ) ) and NP coreference resolution ( e.g. Grishman ( 1995 ) , Lin
P09-1074 light on the state-of-the-art in NP coreference resolution by teasing apart the differences
S15-1035 impact of our system on top of an NP coreference resolution engine , we consider the following
W06-0106 U.S. president Bill Clinton . NP coreference resolution is an important subtask in natural
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