A better formatted version is at http://sites.google.com/site/yaoziyuan/ideas
Foreign Language Learning
Automatic Code-Switching (ACS)
- The computer automatically selects a few words in a user's native
language communication (such as a web page being viewed), and
supplements or even replaces them with their foreign language
counterparts, thus naturally building up his vocabulary. For example,
if a sentence "他是一个好学生。" (Chinese for "He is a good student.") appears in a
Chinese person's Web browser, the computer can insert student after 学生
(optionally with additional information such as student's pronunciation in
IPA): 他是一个好学生 (student)。After several times of such teaching, the computer can
directly replace future occurrences of 学生 with student. Ambiguous words such as
the 看 (Chinese for "see", "look", "watch", "read", etc.) in 他在电视前看书 (Chinese
for "He is reading a book before the TV.") can also be automatically handled by
listing all context-possible translations: 他在电视前看 (阅读: read; 观看: watch)
书。Practice is also possible: 他在电视前 [read; watch] 书。Because
the computer would only teach and/or practice foreign language elements
at a small number of positions in the native language article the user
is viewing, the user wouldn't find it too intrusive. Automatic
code-switching can also teach grammatical knowledge in similar ways.
Progressive Word Acquisition (PWA) - In ACS,
long words are optionally split into small segments (usually two
syllables long) and taught progressively, and even practiced
progressively. For example, when 科罗拉多州 (Chinese for "Colorado") first appears
in a Chinese person's Web browser, the computer inserts Colo' after it
(optionally with Colo's pronunciation): 科罗拉多州 (Colo'); when 科罗拉多州 appears for
the second time, the computer may decide to test the user's memory about Colo'
so it replaces 科罗拉多州 with Colo' (US state). Note that a hint such as "US state"
is necessary in order to differentiate this Colo' from other words beginning
with Colo. For the third occurrence of 科罗拉多州, the computer teaches the full
form, Colorado, by inserting it after the Chinese occurrence: 科罗拉多州 (Colorado);
at the fourth time, the computer may totally replace 科罗拉多州 with Colorado. Not
only the foreign language element (Colorado) can emerge gradually, the original
native language element (科罗拉多州) can also gradually fade out, either visually or
semantically (e.g. 科罗拉多州 -> 美国某州 -!
> 地名
-> ∅). This prevents the learner from suddenly losing the Chinese
clue, while also engages him in active recalls of the occurrence's
complete meaning (科罗拉多州) with gradually reduced clues.
Subword Familiarization (SWF) - In ACS, word roots (e.g. pro-, scrib-) and
meaningless word fragments (e.g. -ot)
are optionally treated as two special kinds of standalone words and
taught and practiced in the user's incoming native language
information. Meaningless fragments are considered abbreviations and
acronyms of real, meaningful words. Getting the learner familiar with
all these subword units can facilitate the acquisition of longer, real
words that contain them.
Phonetics-Enhanced English (PEE)
- The computer can add non-intrusive diacritical marks (e.g. the mark
in á) above normal English words to better reflect their
pronunciations. Unlike radical spelling reform proposals, a word's
original literal form is always preserved. Unlike annotating words with
their IPA forms above, diacritical marks are closely integrated with
letters so a learner can "read once and learn both the literal and the
phonetic form." In inputting English, the learner still uses the
original literal form only.
Computer-Assisted Foreign Language Writing
Input-Driven Syntax Aid (IDSA) - As a non-native English user inputs a word,
e.g. search, the word's sentence-making syntaxes are prompted by the computer,
e.g.
v. search: n. searcher search~ [n. search scope] [for n. search target]
so he can now write a syntactically valid sentence like "I'm searching the room
for the cat."
Input-Driven Ontology Aid (IDOA) - As a non-native English user inputs a word,
e.g. badminton,
things and relations that normally co-exist with the word in the same
scenario or domain are prompted as a systematic ontology graph by the
computer, e.g. entities like racquet, shuttlecock and playing court, relations
like alternate, serve and strike, and even full-scripted composition templates
like template: a badminton game. The benefits of the ontology aid are twofold.
First, the ontology helps the user verify that the "seed word", badminton,
is a valid concept in the intended scenario (or context); second, the
ontology pre-emptively exposes other valid words in this context to the
user, preventing him from using a wrong word, e.g. bat (instead of racquet),
from the very beginning.
Foreign Language Reading without Learning that Language
Full-Automatic Layered-Quality Machine Translation (FALQ-MT)
- Lexical and syntactic ambiguities are translated to fuzzy concepts
and structures instead of precise but error-prone results. Less information is
better than misinformation.
If the reader can't guess the meaning of a fuzzy occurrence from its
context, he can "zoom in" and see more detailed translation
possibilities if he feels that occurrence is important.
Natural Disambiguation (NATDISAMB)
- A native reader perceives a multi-sense word not by enumerating all
possible senses of that word one by one in his mind and validating each
one against the context. Instead, the word is a multi-faceted body in
his mind, each facet orienting to one domain and displaying a sense
corresponding to that domain, and the context is a driving force that
positions the mind's eye into an anticipated domain and from there the
mind's eye sees only one facet of the body. Other facets (senses) are
mentally inhibited right from the beginning. This process can be
simulated for a person who doesn't know a language at all to "act like"
the native reader: The computer translates the source language document
to the person's native language, presenting unresolved ambiguous words
as multi-row objects, each row corresponding to a formerly defined
domain and displaying a sense corresponding to that domain. The reader,
previously instructed which row corresponds to what domain, doesn't
have to read every row of such a multi-row object and find the sense
that fits the context, but just needs to predict, based on previous
context, which domain the upcoming word will come from, and accordingly
look at the corresponding row, and voila! He got the right sense.
Foreign Language Writing without Learning that Language
Formal Language Writing and Machine Translation (FLW)
- A person not knowing a target language can generate information in
that language by composing in a formal language based on his native
language and having the composition machine-translated. Tools such as
the input-driven syntax aid and input-driven ontology aid can be borrowed to
assist the person in formal language writing. The idea of natural disambiguation
can also be adapted here, so that the author can intuitively specify an
intended sense for a word he has just entered, without going through
all candidates.
Ontology-Based Resource Sharing
Wikipedia-Based Resource Sharing (WP-RES) - A useful property of Wikipedia is
that each Wikipedia article or category can serve as a unique address, or
"coordinates",
for the topic it corresponds to. With this
property, we can enable people with the same interest to rendezvous at
the same Wikipedia page and therefore talk with each other. People
could also register resources at a Wikipedia page's External Links
section so that other people with the same interest can find them.
People could even "subscribe" to a Wikipedia page for new and updated
resources and opportunities on that topic.Ontology-Based Problem-Solving Skills
Sharing
Wikipedia: From Knowledgebase to Strategybase (STRABASE) - If
we're solving a problem, say, a math problem, we choose a seemingly
promising strategy from our "strategy bases" in our minds, according to
the problem's
main type and characteristic conditions. Such a "strategy base" is
something we can build up externally using a wiki. A
"strategy" is a special kind of knowledge that caters to certain
problem
characteristics and provides certain problem-solving frameworks. The
wiki can store and categorize strategies and domain knowledge by their
intended problem types and characteristics, so the human can better
evaluate, select and apply strategies relevant to his problem.Miscellaneous
Chinese Pinyin Input Method Revisited (PYIME)
- Today's Chinese pinyin input methods inherit the single-row
candidates window from the DOS era. If we categorize candidate
characters into multiple rows according to some criteria, the user can
more easily home in on his desired character. For example, each row
contains characters that have the same phonetic radical, and one row
reads "马 吗 妈 码 玛", while another row reads "麻 嘛 䗫". Rows can also
correspond to the five possible tones in Chinese. Still, there can be a
special, first row for the most frequently used words and characters.
A Politically Correct New Name for English (ARCS) - As technology like
automatic code-switching
would make English a much cheaper commodity for non-native people to
acquire, for the first time it will become possible for most people in
the world to use decent English. But nationalist sentiments can be a
negative factor for some people to adopt English. While it is logically
recognized by everybody that all natural languages are actually made of equally
random syllables, emotionally people can still more or less feel unequal
that one language is more international than others. A reason for this
paradox is that languages are named by their nations of origin:
English, French, Spanish, etc. Therefore, we can use a "renaming"
technique to better reflect a language's random nature rather than
nationalist connotation. Actually, the word "language" itself already
has a strong nationalist connotation, and I propose the term "code
system" to eliminate that connotation. As for English, let's rename it
as "A Random Code System", or ARCS for short.
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