| For example, the word bird
maps to Hawk through the is_a relationship. Duck also interlocks with
bird by the same is_a relationship. By sliding along these relationships,
NLPWin uses the knowledge stored in MindNet to identify the meaning
of words in relations to other words.
Discourse Component
The Discourse module, pioneered by Simon Corston-
Oliver, takes the data passed up from previous components and summarizes
it. For instance, it can summarize the essence of a book, similar
to Cliff Notes, presenting the key points of the book.
Meaning Representation Component
At the top of the NLP arch, the Meaning Represent
ation component represents the Holy Grail of computational linguists,
true language understanding. Once in this state, NLPWin has finished
the increasingly abstract parsing of the original text and it stores
the information in MindNet, it is possible to reverse the entire process
to produce meaningful responses.
In other words, the Generation component converts
the abstract, or logical, forms taken directly from NL Text back into
NL Text. By first dissecting and digesting text fed into it and then
synthesizing meaningful responses enables the systemto engage humans
in conversation (dialogue). While many of the previous attempts at
this type of system have |
|
focused on narrow vocabularies,
the NLP Group's ambition is to enable broad coverage of entire natural
languages, such as English, Spanish, Japanese, etc.
Applying NLPWin to Machine Translation
Although the research linguists at Microsoft have made groundbreaking
strides in developing the initial components of NLPWin (with the Word
grammar checker perhaps the most notable milestone), teaching computers
to actually understand language remains a distant goal. Given that
the language Generation module appears to depend on the Meaning Representation
component, the successive and cumulative nature of NLPWin implies
that language translation remains beyond the current capabilities
of the system.
Fortunately for the field of Machine
Translation (MT), the NLP Group has found a method to short-circuit
the process. Once it reaches the Logical Form stage, these highly
abstract constructs stored in MindNet it is possible to match or map
to their counterparts in another language. Thus, the system could
perform MT without the machine truly understanding the meaning of
the words.
The creation of the NLPWin Machine Translation
system takes place in two stages: training and runtime.
Training
Figure 3 presents an overview of the MT training
process. The system begins with a pair of equivalent sample sentences
from a database. |
