WSD: bootstrapping methods
... learning algorithm with the seed set of labeled examples.
2. Apply the classifier to all the unlabeled examples. Find instances
that are classified with probability > threshold and add them to the set
of labeled examples.
3. Optional: Use the one-sense-per-discourse constraint to augment
the new exa ...
Parts of Speech File
... Traditional grammar classifies words based on eight parts of speech. These are
the words that you use to make a sentence. Each part of speech explains not
what the word is, but how the word is used. In fact, the same word can be a
noun in one sentence and a verb or adjective in the next.
Finding the Word - Lone Star College
... Words help process life-- an arsenal of words can serve to make sense of what goes on.
We remember words that make things happen. A word that is effective or meaningful is going
to be remembered in order to achieve something or understand new challenges.
English words are meaningful in context
It never entered my head to be sacred
... » occurrence of complementation patterns
suggestion that, decision as to whether, obligation to do
three important consequences – challenge to current views
▪ no distinction between pattern and meaning
▪ language: two principles of organization
▫ idiom principle
▫ open-choice principl ...
... Adverbs- A word that describes when, how,
where, how often, and how much.
Adverbs frequently end in “ly” and modify verbs,
adjectives, and other adverbs.
General linguistic terms you should know
... The following glossary should be used as a quick reference guide to the
key linguistic and literary terms you are expected to know. Always refer
back to your original notes for a full explanation of how to identify and
use these words in context.
Parts of Speech:
Noun – the name given to a person, p ...
... words in this paragraph?
This is an unusual paragraph in an
important way. It conforms to our
notions of grammar and syntax, but its
words vary from typical options. Can
you say how?
... Supervised Learning
Exploits machine learning techniques to
induce models of word usage from large
annotated corpora are tagged manually using
semantic classes chosen from a sense
each sense-tagged occurrence of a particular
word is transformed into a feature vector, ...
... Language is very difficult to put into words. -- Voltaire
What do we mean by “language”?
A system used to convey meaning made up of arbitrary
elements that are organized using a set of rules. -- Rader
Common Core Standards I Can… Statements
... L.8.4d – Verify the preliminary determination of the …verify the meaning of a word or
phrase by checking its context or
meaning of a word or phrase (e.g., by checking the
inferred meaning in context or in a dictionary).
looking it up in a dictionary.
Language Standards: Common Core Grade 2 –(Standards Fig
... Use glossaries and beginning dictionaries, both print and digital, to
determine or clarify the meaning of words and phrases.
Demonstrate understanding of word relationships and nuances in word meanings.
Identify real-life connections between words and their use (e.g. describe
foods that are spicy or ...
Vocab-o-gram pg. 2 of file
... Identify a word that points out a specific person,
place, or thing (for example, this, that, these,
... • Multi – many
• Media – ’something in the middle’
• Means to ’shoot’, process, distribute, store, present
and perceive information coded as
– Video, audio, animation, graphics, text,,,
... position of one thing in
relation to another.
Examples are above, over
... example, in the most recent edition of the OED, the word “run” has fifteen senses in adjective form, over fifty senses
in noun form, and over eighty senses in verb form! The task of choosing which word sense most accurately
represents the sense of a particular use of a word is known as Word Sense Di ...
What is a M.C. Cloze?
... Any more clues?
•Context (identify the clues by context) (justify
the choice as a result of the clues)
•Should very often be an immediate context
•Locate grammatical and discourse markers to
anticipate the correct words and form of that word)
Parts of Speech - Bardstown City Schools
... A preposition is a word that shows the relationship of a noun or pronoun to some other word in a
Examples of Commonly Used Prepositions
aboard, about, above, across, after, against, along, amid, among, around, as, at, before, behind, below,
beneath, beside, besides, between, beyond, by, co ...
... The speaker may want the audience to know only a certain
amount about a subject so may choose to use vague phrases.
If a speaker wishes to expound in great detail he or she will use
words that are specific and precise.
In computational linguistics, word-sense disambiguation (WSD) is an open problem of natural language processing and ontology. WSD is identifying which sense of a word (i.e. meaning) is used in a sentence, when the word has multiple meanings. The solution to this problem impacts other computer-related writing, such as discourse, improving relevance of search engines, anaphora resolution, coherence, inference et cetera.The human brain is quite proficient at word-sense disambiguation. The fact that natural language is formed in a way that requires so much of it is a reflection of that neurologic reality. In other words, human language developed in a way that reflects (and also has helped to shape) the innate ability provided by the brain's neural networks. In computer science and the information technology that it enables, it has been a long-term challenge to develop the ability in computers to do natural language processing and machine learning.To date, a rich variety of techniques have been researched, from dictionary-based methods that use the knowledge encoded in lexical resources, to supervised machine learning methods in which a classifier is trained for each distinct word on a corpus of manually sense-annotated examples, to completely unsupervised methods that cluster occurrences of words, thereby inducing word senses. Among these, supervised learning approaches have been the most successful algorithms to date.Current accuracy is difficult to state without a host of caveats. In English, accuracy at the coarse-grained (homograph) level is routinely above 90%, with some methods on particular homographs achieving over 96%. On finer-grained sense distinctions, top accuracies from 59.1% to 69.0% have been reported in recent evaluation exercises (SemEval-2007, Senseval-2), where the baseline accuracy of the simplest possible algorithm of always choosing the most frequent sense was 51.4% and 57%, respectively.