Here are 14 ideas on language, information and intelligence. 2 of them are
newly added (OEE and FLPM). An HTML version is at
http://www.bytecool.com/ideas.html .
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):
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:
他是一个好 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) - Again 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 derived from 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.
* Orthography-Enhanced English (OEE) - Sometimes spelling a word based on
its pronunciation can be hard, even for native speakers. For example, is it
"Lawrence" or "Lawrance"? Is it "porridge" or "porrige"? We can slightly change
a word's visual form to help recall its correct spelling. For example, when the
computer displays a word that has the "-ance" suffix (e.g. "instance"), it can
lower the letter "a" a little, just like Intel has a trademark "intel" with a
lowered "e". Such a new visual form can help people recall that the unclear
letter in "inst*nce" is "a" because "a" is always lowered in "-ance". Similarly
we can let the computer display "porridge" in a new form by adding an arc
(Unicode U+035C) below "dg" to indicate this sound corresponds to two letters
instead of one.
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 (entities) 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.
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 vocabulary 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. Manual word sense disambiguation (WSD) can be conducted after
the composition is finished, on a domain-to-domain basis, because it is
cognitively easier for the writer to focus on a single domain at a time and
answer a series of questions "Does <word_i> belong to this domain?" Another
approach to manual WSD is to borrow the idea in Machine Translation with
Natural Disambiguation.
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, as most mainland Chinese don't type tones.
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.
* Foreign Language Proficiency Measurement (FLPM) - How does a non-native
speaker introduce his language level to a native speaker in an understandable
manner? The computer can test his proficiency and compare it with native
speakers at different ages. Introductions like "My English level is like a
10-year-old American child" should be understood well by a native speaker.
_________________________________________________________________
Hotmail® is up to 70% faster. Now good news travels really fast.
http://windowslive.com/online/hotmail?ocid=PID23391::T:WLMTAGL:ON:WL:en-US:WM_HYGN_faster:082009_______________________________________________
Mt-list mailing list
