other,37-1-A92-1027,bq |
We present an efficient algorithm for
<term>
chart-based phrase structure parsing
</term>
of
<term>
natural language
</term>
that is tailored to the problem of extracting specific information from
<term>
unrestricted texts
</term>
where many of the
<term>
words
</term>
are unknown and much of the
<term>
text
</term>
is irrelevant to the task .
|
#17580
We present an efficient algorithm for chart-based phrase structure parsing of natural language that is tailored to the problem of extracting specific information from unrestricted texts where many of the words are unknown and much of thetext is irrelevant to the task. |
other,42-6-A92-1027,bq |
A further
<term>
reduction in the search space
</term>
is achieved by using
<term>
semantic
</term>
rather than
<term>
syntactic categories
</term>
on the
<term>
terminal and non-terminal edges
</term>
, thereby reducing the amount of
<term>
ambiguity
</term>
and thus the number of
<term>
edges
</term>
, since only
<term>
edges
</term>
with a valid
<term>
semantic
</term>
interpretation are ever introduced .
|
#17745
A further reduction in the search space is achieved by using semantic rather than syntactic categories on the terminal and non-terminal edges, thereby reducing the amount of ambiguity and thus the number of edges, since only edges with a validsemantic interpretation are ever introduced. |
other,27-3-A92-1027,bq |
As each new
<term>
edge
</term>
is added to the
<term>
chart
</term>
, the algorithm checks only the topmost of the
<term>
edges
</term>
adjacent to it , rather than all such
<term>
edges
</term>
as in conventional treatments .
|
#17627
As each new edge is added to the chart, the algorithm checks only the topmost of the edges adjacent to it, rather than all suchedges as in conventional treatments. |
other,30-4-A92-1027,bq |
The resulting
<term>
spanning edges
</term>
are insured to be the correct ones by carefully controlling the order in which
<term>
edges
</term>
are introduced so that every final
<term>
constituent
</term>
covers the longest possible
<term>
span
</term>
.
|
#17663
The resulting spanning edges are insured to be the correct ones by carefully controlling the order in which edges are introduced so that every final constituent covers the longest possiblespan. |
other,18-6-A92-1027,bq |
A further
<term>
reduction in the search space
</term>
is achieved by using
<term>
semantic
</term>
rather than
<term>
syntactic categories
</term>
on the
<term>
terminal and non-terminal edges
</term>
, thereby reducing the amount of
<term>
ambiguity
</term>
and thus the number of
<term>
edges
</term>
, since only
<term>
edges
</term>
with a valid
<term>
semantic
</term>
interpretation are ever introduced .
|
#17721
A further reduction in the search space is achieved by using semantic rather than syntactic categories on theterminal and non-terminal edges, thereby reducing the amount of ambiguity and thus the number of edges, since only edges with a valid semantic interpretation are ever introduced. |
other,7-5-A92-1027,bq |
This is facilitated through the use of
<term>
phrase boundary heuristics
</term>
based on the placement of
<term>
function words
</term>
, and by
<term>
heuristic rules
</term>
that permit certain kinds of
<term>
phrases
</term>
to be deduced despite the presence of
<term>
unknown words
</term>
.
|
#17672
This is facilitated through the use ofphrase boundary heuristics based on the placement of function words, and by heuristic rules that permit certain kinds of phrases to be deduced despite the presence of unknown words. |
other,15-5-A92-1027,bq |
This is facilitated through the use of
<term>
phrase boundary heuristics
</term>
based on the placement of
<term>
function words
</term>
, and by
<term>
heuristic rules
</term>
that permit certain kinds of
<term>
phrases
</term>
to be deduced despite the presence of
<term>
unknown words
</term>
.
|
#17680
This is facilitated through the use of phrase boundary heuristics based on the placement offunction words, and by heuristic rules that permit certain kinds of phrases to be deduced despite the presence of unknown words. |
other,11-1-A92-1027,bq |
We present an efficient algorithm for
<term>
chart-based phrase structure parsing
</term>
of
<term>
natural language
</term>
that is tailored to the problem of extracting specific information from
<term>
unrestricted texts
</term>
where many of the
<term>
words
</term>
are unknown and much of the
<term>
text
</term>
is irrelevant to the task .
|
#17554
We present an efficient algorithm for chart-based phrase structure parsing ofnatural language that is tailored to the problem of extracting specific information from unrestricted texts where many of the words are unknown and much of the text is irrelevant to the task. |
other,34-6-A92-1027,bq |
A further
<term>
reduction in the search space
</term>
is achieved by using
<term>
semantic
</term>
rather than
<term>
syntactic categories
</term>
on the
<term>
terminal and non-terminal edges
</term>
, thereby reducing the amount of
<term>
ambiguity
</term>
and thus the number of
<term>
edges
</term>
, since only
<term>
edges
</term>
with a valid
<term>
semantic
</term>
interpretation are ever introduced .
|
#17737
A further reduction in the search space is achieved by using semantic rather than syntactic categories on the terminal and non-terminal edges, thereby reducing the amount of ambiguity and thus the number ofedges, since only edges with a valid semantic interpretation are ever introduced. |
other,8-3-A92-1027,bq |
As each new
<term>
edge
</term>
is added to the
<term>
chart
</term>
, the algorithm checks only the topmost of the
<term>
edges
</term>
adjacent to it , rather than all such
<term>
edges
</term>
as in conventional treatments .
|
#17608
As each new edge is added to thechart, the algorithm checks only the topmost of the edges adjacent to it, rather than all such edges as in conventional treatments. |
other,30-1-A92-1027,bq |
We present an efficient algorithm for
<term>
chart-based phrase structure parsing
</term>
of
<term>
natural language
</term>
that is tailored to the problem of extracting specific information from
<term>
unrestricted texts
</term>
where many of the
<term>
words
</term>
are unknown and much of the
<term>
text
</term>
is irrelevant to the task .
|
#17573
We present an efficient algorithm for chart-based phrase structure parsing of natural language that is tailored to the problem of extracting specific information from unrestricted texts where many of thewords are unknown and much of the text is irrelevant to the task. |
other,11-6-A92-1027,bq |
A further
<term>
reduction in the search space
</term>
is achieved by using
<term>
semantic
</term>
rather than
<term>
syntactic categories
</term>
on the
<term>
terminal and non-terminal edges
</term>
, thereby reducing the amount of
<term>
ambiguity
</term>
and thus the number of
<term>
edges
</term>
, since only
<term>
edges
</term>
with a valid
<term>
semantic
</term>
interpretation are ever introduced .
|
#17714
A further reduction in the search space is achieved by usingsemantic rather than syntactic categories on the terminal and non-terminal edges, thereby reducing the amount of ambiguity and thus the number of edges, since only edges with a valid semantic interpretation are ever introduced. |
other,27-5-A92-1027,bq |
This is facilitated through the use of
<term>
phrase boundary heuristics
</term>
based on the placement of
<term>
function words
</term>
, and by
<term>
heuristic rules
</term>
that permit certain kinds of
<term>
phrases
</term>
to be deduced despite the presence of
<term>
unknown words
</term>
.
|
#17692
This is facilitated through the use of phrase boundary heuristics based on the placement of function words, and by heuristic rules that permit certain kinds ofphrases to be deduced despite the presence of unknown words. |
other,24-1-A92-1027,bq |
We present an efficient algorithm for
<term>
chart-based phrase structure parsing
</term>
of
<term>
natural language
</term>
that is tailored to the problem of extracting specific information from
<term>
unrestricted texts
</term>
where many of the
<term>
words
</term>
are unknown and much of the
<term>
text
</term>
is irrelevant to the task .
|
#17567
We present an efficient algorithm for chart-based phrase structure parsing of natural language that is tailored to the problem of extracting specific information fromunrestricted texts where many of the words are unknown and much of the text is irrelevant to the task. |
tech,6-1-A92-1027,bq |
We present an efficient algorithm for
<term>
chart-based phrase structure parsing
</term>
of
<term>
natural language
</term>
that is tailored to the problem of extracting specific information from
<term>
unrestricted texts
</term>
where many of the
<term>
words
</term>
are unknown and much of the
<term>
text
</term>
is irrelevant to the task .
|
#17549
We present an efficient algorithm forchart-based phrase structure parsing of natural language that is tailored to the problem of extracting specific information from unrestricted texts where many of the words are unknown and much of the text is irrelevant to the task. |
other,14-6-A92-1027,bq |
A further
<term>
reduction in the search space
</term>
is achieved by using
<term>
semantic
</term>
rather than
<term>
syntactic categories
</term>
on the
<term>
terminal and non-terminal edges
</term>
, thereby reducing the amount of
<term>
ambiguity
</term>
and thus the number of
<term>
edges
</term>
, since only
<term>
edges
</term>
with a valid
<term>
semantic
</term>
interpretation are ever introduced .
|
#17717
A further reduction in the search space is achieved by using semantic rather thansyntactic categories on the terminal and non-terminal edges, thereby reducing the amount of ambiguity and thus the number of edges, since only edges with a valid semantic interpretation are ever introduced. |
other,28-6-A92-1027,bq |
A further
<term>
reduction in the search space
</term>
is achieved by using
<term>
semantic
</term>
rather than
<term>
syntactic categories
</term>
on the
<term>
terminal and non-terminal edges
</term>
, thereby reducing the amount of
<term>
ambiguity
</term>
and thus the number of
<term>
edges
</term>
, since only
<term>
edges
</term>
with a valid
<term>
semantic
</term>
interpretation are ever introduced .
|
#17731
A further reduction in the search space is achieved by using semantic rather than syntactic categories on the terminal and non-terminal edges, thereby reducing the amount ofambiguity and thus the number of edges, since only edges with a valid semantic interpretation are ever introduced. |
other,20-5-A92-1027,bq |
This is facilitated through the use of
<term>
phrase boundary heuristics
</term>
based on the placement of
<term>
function words
</term>
, and by
<term>
heuristic rules
</term>
that permit certain kinds of
<term>
phrases
</term>
to be deduced despite the presence of
<term>
unknown words
</term>
.
|
#17685
This is facilitated through the use of phrase boundary heuristics based on the placement of function words, and byheuristic rules that permit certain kinds of phrases to be deduced despite the presence of unknown words. |
other,18-4-A92-1027,bq |
The resulting
<term>
spanning edges
</term>
are insured to be the correct ones by carefully controlling the order in which
<term>
edges
</term>
are introduced so that every final
<term>
constituent
</term>
covers the longest possible
<term>
span
</term>
.
|
#17651
The resulting spanning edges are insured to be the correct ones by carefully controlling the order in whichedges are introduced so that every final constituent covers the longest possible span. |
other,3-3-A92-1027,bq |
As each new
<term>
edge
</term>
is added to the
<term>
chart
</term>
, the algorithm checks only the topmost of the
<term>
edges
</term>
adjacent to it , rather than all such
<term>
edges
</term>
as in conventional treatments .
|
#17603
As each newedge is added to the chart, the algorithm checks only the topmost of the edges adjacent to it, rather than all such edges as in conventional treatments. |