The LaBerge-Samuels model attempts to identify the components of the reading information processing system and trace how information moves through it. At the heart of the model is attention. The model emphasizes the role of attention in reading component processes. It shows how visual information is analyzed into features, letters, and words. With practice, these visual codes can be processed automatically without attention. The model also includes a phonological memory system that represents sounds. With more practice, readers can access word meanings directly from visual codes without going through phonological representations. The goal of reading development is to build automatic orthographic, phonological, and semantic systems for efficient comprehension.
Input processing (IP) is concerned with these situations, the reason being that acquisition is, to a certain degree, a by-product of comprehension (see, e.g., Truscott & Sharwood Smith, 2004)
Input processing (IP) is concerned with these situations, the reason being that acquisition is, to a certain degree, a by-product of comprehension (see, e.g., Truscott & Sharwood Smith, 2004)
It is a systematic instruction in readng skills and strategies.
It seeks to generate a positive attitude toward the reading process through the channg of the reaing habits; to change reading weakness into strengths; to let students become aware of learning techniques which can enable any person, student, or otherwise to become mre successful in
real life learnin situations.
This was part of our school's Teacher Development Workshop. In this particular workshop we delved into the realm of vocabulary teaching. We established some common grounds about vocabulary, then we went on to discuss common ways of teaching lexical items to students as well as testing them.
It is a systematic instruction in readng skills and strategies.
It seeks to generate a positive attitude toward the reading process through the channg of the reaing habits; to change reading weakness into strengths; to let students become aware of learning techniques which can enable any person, student, or otherwise to become mre successful in
real life learnin situations.
This was part of our school's Teacher Development Workshop. In this particular workshop we delved into the realm of vocabulary teaching. We established some common grounds about vocabulary, then we went on to discuss common ways of teaching lexical items to students as well as testing them.
A N H YBRID A PPROACH TO W ORD S ENSE D ISAMBIGUATION W ITH A ND W ITH...ijnlc
Word Sense Disambiguation is a classification of me
aning of word in a precise context which is a trick
y
task to perform in Natural Language Processing whic
h is used in application like machine translation,
information extraction and retrieval, automatic or
closed domain question answering system for the rea
son
that of its semantics perceptive. Researchers tried
for unsupervised and knowledge based learning
approaches however such approaches have not proved
more helpful. Various supervised learning
algorithms have been made, but in vain as the attem
pt of creating the training corpus which is a tagge
d
sense marked corpora is tricky. This paper presents
a hybrid approach for resolving ambiguity in a
sentence which is based on integrating lexical know
ledge and world knowledge. English Wordnet
developed at Princeton University, SemCor corpus an
d the JAWS library (Java API for WordNet
searching) has been used for this purpose.
Created by Sonia Babaee
Sources:
Mishan, F. & Timmis, I. (2015). Materials development for TESOL (pp. 99-120). Edinburg University Press. (Materials to develop reading and listening skills)
Mishan, F. & Timmis, I. (2015). Materials development for TESOL (pp. 121-140). Edinburg University Press. (Materials to develop speaking and writing skills)
OPTIMIZE THE LEARNING RATE OF NEURAL ARCHITECTURE IN MYANMAR STEMMERkevig
Morphological stemming becomes a critical step toward natural language processing. The process of
stemming is to reduce alternative forms to a common morphological root. Word segmentation for
Myanmar Language, like for most Asian Languages, is an important task and extensively-studied
sequence labelling problem. Named entity detection is one of the issues in Asian Language that has
traditionally required a large amount of feature engineering to achieve high performance. The new
approach is integrating them that would benefit in all these processes. In recent years, end-to-end
sequence labelling models with deep learning are widely used. This paper introduces a deep BiGRUCNN-CRF network that jointly learns word segmentation, stemming and named entity recognition tasks.
We trained the model using manually annotated corpora. State-of-the-art named entity recognition
systems rely heavily on handcrafted feature built in our new approach, we introduce the joint model that
relies on two sources of information: character level representation and syllable level representation.
OPTIMIZE THE LEARNING RATE OF NEURAL ARCHITECTURE IN MYANMAR STEMMERijnlc
Morphological stemming becomes a critical step toward natural language processing. The process of stemming is to reduce alternative forms to a common morphological root. Word segmentation for Myanmar Language, like for most Asian Languages, is an important task and extensively-studied sequence labelling problem. Named entity detection is one of the issues in Asian Language that has traditionally required a large amount of feature engineering to achieve high performance. The new approach is integrating them that would benefit in all these processes. In recent years, end-to-end sequence labelling models with deep learning are widely used. This paper introduces a deep BiGRUCNN-CRF network that jointly learns word segmentation, stemming and named entity recognition tasks. We trained the model using manually annotated corpora. State-of-the-art named entity recognition systems rely heavily on handcrafted feature built in our new approach, we introduce the joint model that relies on two sources of information: character level representation and syllable level representation.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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TESDA TM1 REVIEWER FOR NATIONAL ASSESSMENT WRITTEN AND ORAL QUESTIONS WITH A...
Reading Automaticity by David LaBerge and S Jay Samuels
1. David LaBERGE & S.Jay SAMUELS
University of Minnesota
Prepared and presented by:
• Ikram BENZOUINE
• Doha ZALLAG
2.
3. Introduction
Attention Mechanisms in Information Processing
Model of automaticity in Reading ( Grapheme Learning Model )
1. Learning of the visual code
2. Examples
3. The role of the visual system
4. The acquisition of Automaticity
Theoretical relationship between Visual & Phonological systems
Theoretical relationship between Visual, Phonological & Semantic systems
1. Indicators of automatic associative processing
2. General model of automaticity in reading
Development of Automaticity
Implications of the Model for Research in Reading Instruction
Comprehension
6. ♧ Refers to the active
attempt to come in
contact with sources of
information.
♧ Vigilance
( LaBerge and Samuels 293-323)
7. Example :
As you read this line, are you aware that
the lines above and below are also on
your retina ?
We are generally unaware of this, and the
process of selective attention enables us
to choose which line we will process.
♤ Following is a
passage that shows
how selective
attention operates in
the visual mode.
( LaBerge and Samuels 293-323)
8. I’ll put a
picture here
• The human mind has
limited capacity to
process information.
• Limited capacity =
limited amount of
attention available
• Attention = the effort
/ energy used to
process Info.
• Examples : ??
9. The LaBerge–Samuels model
a t t e m p t s t o i d e n t i f y
components in the information-
processing system, trace the
routes that information takes
as it passes through the
system, and identify changes in
the form of the information as
i t m o v e s f r o m t h e
surface of the page into the
deeper semantic-linguistic
centers of the brain. At the
heart of the model is attention.
(LaBerge and Samuels 293-323)
The present model emphasizes heavily on the role of attention
in the component processes of reading.
ATTENTION
ExternalInternal
• Teacher ~ Student
• Orienting behavior
• Alertness (Posner & Boies
1971)
• Selectivity
• Capacity limitation
In this model, ATTENTION can selectively activate
codes at any level of the system :
Meaning Level + Visual Level + Auditory Level
10.
11. This model is based on
the assumption that :
The transformation of
written stimuli into
meanings involves a
sequence of stages of
information
processing.
(Posner et al., 1972)
I _ The Visual Memory
II _ The Phonological Memory
III _ The Semantic Memory
12. Incoming information from the words in print first strikes the
sensory surface of the eye, where detectors process features
such as : lines, curves, angles, intersections, and relational
features.
14. Example :
b VS p
p
Circle to the right
At the top of the
vertical line
b
Circle to the right
At the Bottom of the
vertical line
15. Different kinds of information gets processed.
The model shows how the visual information is analyzed by
detectors into features, which at the next level are combined
to form letters.
16. Letter combinations such as :
sh, th, bl, -ing, and anti- may be combined to
form spelling patterns, and the spelling
patterns feed into word codes.
There are two additional features in this
model of VM, labeled f1 and f2.
Unlike the other features that lead into
letters, f1 and f2 indicate that features other
than letters may be used in the identification
of a word.
For example, word configuration and length
may be used in combination with other
sources of textual information in word
recognition.
Assume that the words we wish to identify
are hippopotamus, dog, and cat. The use of the term codes in the model
refers to the form in which information is represented.
Thus there may be letter
codes, spelling-pattern codes, and word codes.
18. Attention: is considered essential in the early stages of
learning a perceptual code such as a letter.
An individual is free to focus attention at various levels of the
VM model—on features, letters, spelling patterns, or whole
words.
With continued activation and processing of these poorly
learned codes, an individual develops a level of skill such that
attention is no longer required for their processing.
The main conclusion is that what is being improved with
practice is AUTOMATICITY. (not accuracy)
A student may be accurate without being automatic.
Pg. 12
2nd article
19.
20. This learning is conceptualized as a two-step process.
1) the first step is to analyze and select the relevant features of the letter.
2) n the next stage of perceptual learning, students must combine these separate
features into a single letter code, a process that at first requires attention.
3) With practice, students will unitize the separate features into a single letter code;
skilled readers, for example, see b and not “l” plus “o.”
4) With extended practice at letter identification, students’ unitization of the features
occurs without attention.
Greater amount of exposure to the graphemes are necessary before the child can
recognize letters automatically. A feat he must learn to do if he is to acquire new skills
involving combinations of these letters .
21. Phonological memory (PM) is where the auditory
representations of the visual codes are processed.
The phonological memory system is assumed to contain units
related to [ acoustic + articulatory ] inputs.
We will be referring to these inputs using the general term the phonological system.
The acoustic units in PM are features, phonemes, syllables,
and words. These units in PM are counterparts of the features,
letters, spelling patterns, and words found in VM.
Just as the units in visual memory are arranged in a
hierarchy, so too are the units in phonological memory.
Add an image of the
schema p 560
22. Example : Acoustic features are represented by contrasts
+ Bilabial + Velar + Alveolar
+ Plosive + Plosive + Plosive
* Place of articulation or by Manner of articulation
Each of these phonemes signals a change in word meaning.
Example :
/pa/ /ga/ /ta/
Cambridge
dictionary
23. In both visual and phonological memory, information processing may
move from features up to words or from words down to features. When
going from a whole word to features, a decomposition into parts takes
place; teachers often call this process “analysis.”
(S.Jay SAMUELS 1994)
For example, when a teacher asks students to listen for the difference
between /sat/ and /sad/, the process requires a top-down analysis from
whole to parts.
On the other hand, when students sound out a new word letter-by-
letter and blend the sounds to form a word, they are engaging in a
bottom-up process of synthesizing a word from its parts to a whole.
The hierarchy in visual
memory as moving from
features to letters to
spelling patterns and
finally to words.
=
The hierarchy in the
phonological memory
system moves from
features to phonemes to
syllables and finally to
words.
25. La Berge and Samuels maintain that the goal of reading development must be for children
to develop orthographic system that allows meanings to be accessed quickly and efficiently.
For them, reading involves the coordination of multiple systems (orthographic = visual look
of a word, phonological= language sounds, and semantic= meaning of words).
The notion of semantic meaning unit
Main
Assumptions
26. ‘Once a visual word code makes contact with the phonological word
code in reading, we assume that the meaning of the word can be
elicited by means of a direct associative connection between the
phonological unit, p(w1), and the semantic meaning unit, m(w1) …’
Samuels & La Berge
27. Samuels & La Berge further highlight that a word
should elicit its meaning automatically, with enough
practice.
Attention is needed during the process of practice so
as to better activate the association between the
afore-mentioned systems.
With practice, a visual unit may activate its meaning
without meditation through the phonological system.
In other words, we can quickly recognize the
difference in the meaning of such homonyms as ‘two’
and ‘too’.
Role of memory!
You might depend in
part on semantic
processing to know
that when you read
‘cat’, it means or refers
to that warm, furry
thing that jumps on
your lap and meows.
Thus, there is a quick
recognition of a visual
word without visiting
the phonological
system.
28. Samuels & La Berge hold that ‘latency
serves as the critical indicator’:
‘Latency’ or ‘Naming Latency’ is the time it takes a
subject to name a given visual stimulus.
It is worth-mentioning here that automaticity is a
characteristic of cognitive processing. During this
processing, practiced behaviors are performed rapidly,
with minimal efforts or with automatic allocation of
attention to the processing of stimulus.
In a nutshell, reading involves a combination of
automatic and controlled processes. Automatic
processing occurs effortlessly and make minimal
demands on attention.
29. DEVELOPMENT OF
AUTOMATICITY
La Berge & Samuels (1974)
o According to them, ‘practice leads to
automaticity’
o ‘For example, recognizing letters of
alphabet apparently becomes
automatic by successive exposure’
Automaticity refers to knowing how
to perform some arbitrary task at a
competent level without requiring
conscious effort — i.e., it is a form
of unconscious competence.
Edmond Huey (1908)
• He emphasized the role of repititions
in the development of automaticity.
• For him, perception of new words
‘requires considerable time, close
attention and is likely to be
imperfectly done,(…)’
• He maintains that repitition not only
is a time saver, but it also ‘frees the
mind from attention to details.’
30. Implications of the Model for Research in
Reading Instruction
Questions whether reading is wholistic
process or cluster of sub-skills
It is essential to understand
automaticity and how it is achieved to
better a student's performance. This is
important for teachers because
automaticity should be focused on in
early years to ensure higher level
reading skills in adolescence.
All readers must go through similar
stages of learning to read but do so at
different stages,
Reading acquisitions is viewed a series
of skills.
Role of attention in comprehension
31. In order to comprehend what we read, we have to
relate the information in the text to the knowledge
stored in our schemata.
The authors refer to comprehension as organization
of word meanings. These meaning units are scanned
by attention and organized as a ‘coherent whole.’
One does not need to switch to the visual system of
decoding as long as the focus of attention remains at
the semantic level: Attention is key.
32. CONCLUSIONS
According to the LaBerge & Samuels’s
model, a major factor in reading difficulty is
lack of automaticity in decoding, which
overloads the attentional system, leads to
the use of small, meaningless visual
processing units, places heavy demands on
short-term memory, and interferes with
comprehension.
In order to build reading fluency and
automaticity, practice is required, and the
method of repeated reading is suggested.