Sequence Learning,
Simply introduce sequence learning technique to do the temporal classification Task. Include recurrent neural network, long-short term memory, bidirectional neural network, connectionist Temporal Classification and our experiment on low-resource language.
Sequence Learning,
Simply introduce sequence learning technique to do the temporal classification Task. Include recurrent neural network, long-short term memory, bidirectional neural network, connectionist Temporal Classification and our experiment on low-resource language.
2P-Kt: logic programming with objects & functions in KotlinGiovanni Ciatto
Mainstream programming languages nowadays tends to be more and more multi-paradigm ones, by integrating diverse programming paradigms -- e.g., object-oriented programming (OOP) and functional programming (FP). Logic-programming (LP) is a successful paradigm that has contributed to many relevant results in the areas of symbolic AI and multi-agent systems, among the others. Whereas Prolog, the most successful LP language, is typically integrated with mainstream languages via foreign language interfaces, in this paper we propose an alternative approach based on the notion of domain-specific language (DSL), which makes LP available to OOP programmers straightforwardly within their OO language of choice. In particular, we present a Kotlin DSL for Prolog, showing how the Kotlin multi-paradigm (OOP + FP) language can be enriched with LP in a straightforward and effective way. Since it is based on the interoperable 2P-Kt project, our technique also enables the creation of similar DSL on top of other high-level languages such as Scala or JavaScript -- thus paving the way towards a more general adoption of LP in general-purpose programming environments.
This is an introduction of Topic Modeling, including tf-idf, LSA, pLSA, LDA, EM, and some other related materials. I know there are definitely some mistakes, and you can correct them with your wisdom. Thank you~
Masato Hagiwara, Satoshi Sekine
Rakuten Institute of Technology, New York
NEWS 2012, July 12 2012
Transliteration has been usually recognized by spelling-based supervised models. However, a single model cannot deal with mixture of words with different origins, such as “get” in “piaget” and “target”. Li et al. (2007) propose a class transliteration method, which explicitly models the source language origins and switches them to address this issue. In contrast to their model which requires an explicitly tagged training corpus with language origins, Hagiwara and Sekine (2011) have proposed the latent class transliteration model, which models language origins as latent classes and train the transliteration table via the EM algorithm. However, this model, which can be formulated as unigram mixture, is prone to over fitting since it is based on maximum likelihood estimation. We propose a novel latent semantic transliteration model based on Dirichlet mixture, where a Dirichlet mixture prior is introduced to mitigate the over fitting problem. We have shown that the proposed method considerably outperform the conventional transliteration models.
SPADE: Evaluation Dataset for Monolingual Phrase AlignmentYuki Arase
Presentation slides at LREC2018 about our evaluation dataset for syntactic phrase alignment on paraphrases.
The dataset is available here: https://catalog.ldc.upenn.edu/LDC2018T09
2P-Kt: logic programming with objects & functions in KotlinGiovanni Ciatto
Mainstream programming languages nowadays tends to be more and more multi-paradigm ones, by integrating diverse programming paradigms -- e.g., object-oriented programming (OOP) and functional programming (FP). Logic-programming (LP) is a successful paradigm that has contributed to many relevant results in the areas of symbolic AI and multi-agent systems, among the others. Whereas Prolog, the most successful LP language, is typically integrated with mainstream languages via foreign language interfaces, in this paper we propose an alternative approach based on the notion of domain-specific language (DSL), which makes LP available to OOP programmers straightforwardly within their OO language of choice. In particular, we present a Kotlin DSL for Prolog, showing how the Kotlin multi-paradigm (OOP + FP) language can be enriched with LP in a straightforward and effective way. Since it is based on the interoperable 2P-Kt project, our technique also enables the creation of similar DSL on top of other high-level languages such as Scala or JavaScript -- thus paving the way towards a more general adoption of LP in general-purpose programming environments.
This is an introduction of Topic Modeling, including tf-idf, LSA, pLSA, LDA, EM, and some other related materials. I know there are definitely some mistakes, and you can correct them with your wisdom. Thank you~
Masato Hagiwara, Satoshi Sekine
Rakuten Institute of Technology, New York
NEWS 2012, July 12 2012
Transliteration has been usually recognized by spelling-based supervised models. However, a single model cannot deal with mixture of words with different origins, such as “get” in “piaget” and “target”. Li et al. (2007) propose a class transliteration method, which explicitly models the source language origins and switches them to address this issue. In contrast to their model which requires an explicitly tagged training corpus with language origins, Hagiwara and Sekine (2011) have proposed the latent class transliteration model, which models language origins as latent classes and train the transliteration table via the EM algorithm. However, this model, which can be formulated as unigram mixture, is prone to over fitting since it is based on maximum likelihood estimation. We propose a novel latent semantic transliteration model based on Dirichlet mixture, where a Dirichlet mixture prior is introduced to mitigate the over fitting problem. We have shown that the proposed method considerably outperform the conventional transliteration models.
SPADE: Evaluation Dataset for Monolingual Phrase AlignmentYuki Arase
Presentation slides at LREC2018 about our evaluation dataset for syntactic phrase alignment on paraphrases.
The dataset is available here: https://catalog.ldc.upenn.edu/LDC2018T09
Plenary lecture of the XIII SBPMat (Brazilian MRS) meeting, given on October 1st 2014 in João Pessoa (Brazil) by Roberto Dovesi, professor at Universita' degli Studi di Torino (Italy).
Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Opti...Fabian Pedregosa
Short presentation of the paper "Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization"
https://arxiv.org/abs/1707.06468
Description of the quality-assessment and validation of the third release of the Individual Brain Charting (IBC) dataset, namely on naturalistic stimuli using a fastSRM encoding experiment.
The Role of CNL and AMR in Scalable Abstractive Summarization for Multilingua...Normunds Grūzītis
In the era of Big Data and Deep Learning, there is a common view that machine learning approaches are the only way to cope with the robust and scalable information extraction and summarization. It has been recently proposed that the CNL approach could be scaled up, building on the concept of embedded CNL and, thus, allowing for CNL-based information extraction from e.g. normative or medical texts that are rather controlled by nature but still infringe the boundaries of CNL. Although it is arguable if CNL can be exploited to approach the robust wide-coverage semantic parsing for use cases like media monitoring, its potential becomes much more obvious in the opposite direction: generation of story highlights from the summarized AMR graphs, which is in the focus of this position paper.
The goal of this work is to present an efficient implementation of the Backpropagation (BP) algorithm to train Artificial Neural Networks with general feedforward topology. This will lead us to the "consecutive retrieval problem" that studies how to arrange
efficiently sets into a sequence so that every set appears contiguously in the sequence. The BP implementation is analyzed, comparing efficiency results with another similar tool. Together with the BP implementation, the data description and manipulation features of our toolkit facilitates the development of experiments in numerous
fields.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
MATATAG CURRICULUM: ASSESSING THE READINESS OF ELEM. PUBLIC SCHOOL TEACHERS I...NelTorrente
In this research, it concludes that while the readiness of teachers in Caloocan City to implement the MATATAG Curriculum is generally positive, targeted efforts in professional development, resource distribution, support networks, and comprehensive preparation can address the existing gaps and ensure successful curriculum implementation.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
3. 14/06/05 Copyright (C) 2014 by Yusuke Oda, AHC-Lab, IS, NAIST 3
Synchronous Context Free Grammar
(SCFG)
4. 14/06/05 Copyright (C) 2014 by Yusuke Oda, AHC-Lab, IS, NAIST 4
Learning SCFG
●
Synchronous rules are retrieved from each parallel corpora and their
word alignment .
●
: Source sentence
●
: Target sentence
●
: Set of word alignment
5. 14/06/05 Copyright (C) 2014 by Yusuke Oda, AHC-Lab, IS, NAIST 5
Closed Phrase Pair under Word Alignment
●
A phrase pair is closed under its word alignment
●
Phrase pair and alignment satisfy below:
he
will
dissolve
the
diet
in
the
near
future 彼
は
近い
うち
に
国会
を
解散
する
(国会 を → the diet)
6. 14/06/05 Copyright (C) 2014 by Yusuke Oda, AHC-Lab, IS, NAIST 6
Extracting Abstract Rules
●
We can make more abstract synchronous rules by replacing some words
in a phrase pair into a non-terminal symbol, when the phrase pair
covers other "small" phrase pair.
dissolve
the
diet
in
the
near
future
近い
うち
に
国会
を
解散
する
dissolve
in
the
に
解散
する
(国会 を, the diet)
(近い うち, near future)
(近い うち ... 解散 する, dissolve the ... near future)
(X1 に X2 解散 する, dissolve X2 in the X1)
7. 14/06/05 Copyright (C) 2014 by Yusuke Oda, AHC-Lab, IS, NAIST 7
Hiero Grammar
●
Hierarchical phrase grammar (Hiero Grammar):
– Set of all synchronous rule in the parallel corpus
●
Algorithm:
1.
where is the set of all possible phrase pair in the parallel corpora.
2. If a rule and a phrase pair satisfies then
3.
8. 14/06/05 Copyright (C) 2014 by Yusuke Oda, AHC-Lab, IS, NAIST 8
Constraints of Hiero Rules
●
To suppress size and ambiguity of Hiero grammar, we can introduce
some constraints for rule extraction.
●
Minimal phrase pair
– (国会 を, the diet) ... BAD
– (国会, the diet) ... GOOD
●
Phrase length
– (奈良 先端 科学 技術 大学院 大学 情報 科学 研究 科 自然 言語 処理 学 研究 室, ...) BAD (too many words)
●
Number of symbol
– X → 〈あらゆる X1 を 全て X2 の 方 へ ねじ曲げ た の だ, ...〉 BAD (too many symbols)
●
Rank of rule
– X → 〈X1 が X2 で X3 に X4 した, ...〉 BAD (too many non-terminals)
the
diet
国会
を
9. 14/06/05 Copyright (C) 2014 by Yusuke Oda, AHC-Lab, IS, NAIST 9
Glue Rules
●
To make large size sentence using small rules, we introduce glue rules
as below:
10. 14/06/05 Copyright (C) 2014 by Yusuke Oda, AHC-Lab, IS, NAIST 10
Introducing Syntax Labels
●
Up to here, we considered basic ideas of Hiero rules.
– non-terminal symbol are only and .
●
This model is very simple, but very ambiguous.
●
Next, we introduce syntax information into Hiero rules.
= Syntax-augmented machine translation (SAMT)
S
NP VP
PRP VBZ DT NN
this is a pen
NP
Hiero Syntax
+ → SAMT
11. 14/06/05 Copyright (C) 2014 by Yusuke Oda, AHC-Lab, IS, NAIST 11
Combinatorial Categorical Grammar (CCG)
●
SAMT uses categories (≒partial structure of syntax label) based on
the idea of combinatorial categorical grammar (CCG) .
●
Categories:
: Syntax label with absence of right-side child
: Syntax label with absence of left-side child
: Concatenation of two syntax labels and
12. 14/06/05 Copyright (C) 2014 by Yusuke Oda, AHC-Lab, IS, NAIST 12
Extracting SAMT Rules
dissolve
the
diet
in
the
near
future
近い
うち
に
国会
を
解散
する
VP
VB
NP
PP
DT
NNP
IN
NP
DT
JJ
NN
NP
NP
PP
VP
NPDT
IN+DT
VP/PP
VPVB
VP → 〈NPDT1 に NP2 解散 する, dissolve NP2 in the NPDT1〉
VP → 〈近い うち IN+DT1 国会 を VB2, VB2 the diet IN+DT1 near future〉
etc...
13. 14/06/05 Copyright (C) 2014 by Yusuke Oda, AHC-Lab, IS, NAIST 13
Probabilistic Formalization of Hiero Model
●
We consider that the translation problem using Hiero grammar is
maximization of posterior probability (similar to phrase based model):
●
And we assume the probability is modeled as log-linear model:
: Set of derivation (≒ set of used synchronous rules)
: Weights
: Feature functions
14. 14/06/05 Copyright (C) 2014 by Yusuke Oda, AHC-Lab, IS, NAIST 14
Features of Hiero Model (1)
●
Generative model: likelihoods of translation probability
Forward model:
Backward model:
where
Forward
Backward
15. 14/06/05 Copyright (C) 2014 by Yusuke Oda, AHC-Lab, IS, NAIST 15
Features of Hiero Model (2)
●
Generative model: likelihoods of translation probability
Syntax model (f):
Syntax model (e):
where
Syntax (f) Syntax (e)
16. 14/06/05 Copyright (C) 2014 by Yusuke Oda, AHC-Lab, IS, NAIST 16
Features of Hiero Model (3)
●
Lexical translation model: goodness of phrase alignment
Forward model:
Backward model:
where
Forward
Backward
17. 14/06/05 Copyright (C) 2014 by Yusuke Oda, AHC-Lab, IS, NAIST 17
Features of Hiero Model (4)
●
Language model: measuring fluency of hypothesis
Out-of-vocabulary (OOV) penalty: adjusting LM
●
Length penalty: adjusting number of words in hypothesis
Glueing penalty: adjusting number of glue rules in derivation
18. 14/06/05 Copyright (C) 2014 by Yusuke Oda, AHC-Lab, IS, NAIST 18
Decoding of Hiero Model
●
Now input sentence and set of SCFG rules are given, we find
the optimal output sequence :
: Set of possible derivation given a grammar
: Sequence of terminal symbols in given derivationn
19. 14/06/05 Copyright (C) 2014 by Yusuke Oda, AHC-Lab, IS, NAIST 19
Decoding Process
1. Calculate intersection between and .
•
= Generating syntax forest using CYK algorithm
2. Transform syntax forest into corresponding translation forest .
3. Output the sequence of terminal symbols in that maximizes model
score.
S
NP VP
PP NP V
NP P NP
が
犬
本
の
上に
座った
S
NP VP
the dog V NP PP
sat NP of P NP
the upper on the book
"犬 が 本 の 上 に 座った"
"the dog sat on the book"
20. 14/06/05 Copyright (C) 2014 by Yusuke Oda, AHC-Lab, IS, NAIST 20
Synchronous Tree Substitution Grammar
(STSG)
21. 14/06/05 Copyright (C) 2014 by Yusuke Oda, AHC-Lab, IS, NAIST 21
Synchronous Tree Substitution Grammar
●
STSG is a extension of Tree Substitution Grammar (TSG) for bilingual
analysis.
●
STSG is a subset of Synchronous Tree Adjoining Grammar (STAG).
●
Definition:
SCFG (Hiero)
STSG
STAG
U
U
Set of non-terminal symbol
Start symbol
Set of terminal symbol
Set of rules
Weight semiring
22. 14/06/05 Copyright (C) 2014 by Yusuke Oda, AHC-Lab, IS, NAIST 22
Synchronous Rules of STSG
●
Definition:
where : Elementary tree (source language)
: Elementary tree (target language)
: Association between and
●
All rules are also associated a weight:
S
x1:NP VP
x2:NP V
開けた
S
x1:NP VP
VBD x2:NP
opened
frontier
23. 14/06/05 Copyright (C) 2014 by Yusuke Oda, AHC-Lab, IS, NAIST 23
Expressive Power of STSG
●
SCFG cannot express the difference of syntax, but STSG can treat it.
●
Example:
– This synchronous rule cannot generate using more smaller SCFG rules
because these trees not corresponds any structure.
– STSG framework can treat these correspondence of tree structure directly.
NP
NP PP
N P x1:CD PC
犬 が 匹
NP
NNSx1:CD
dogs
24. 14/06/05 Copyright (C) 2014 by Yusuke Oda, AHC-Lab, IS, NAIST 24
Translation Models under STSG Framework
●
In the STSG framework, we can use the sequence of frontier nodes
(leaves of synchronous rule) instead of full tree.
●
4 translation models are available when we choose either tree or
sequence of frontier as data structure about source and target
language.
Target : frontier Target : tree
Source : frontier
String-to-string
translation
(= SCFG)
String-to-tree
translation
Source : tree
Tree-to-string
translation
Tree-to-tree
translation
S
x1:NP
VP
x2:NP
V
開けた
Tree
sequence of frontier nodes
25. 14/06/05 Copyright (C) 2014 by Yusuke Oda, AHC-Lab, IS, NAIST 25
Retrieving STSG Synchronous Rules
●
Heuristic method (similar to SCFG rule extraction)
: Syntax tree generated
from source sentence
: Syntax tree generated
from target sentence
dissolve
the
diet
in
the
near
future
近い
うち
に
国会
を
解散
する
VP VB
NP
PP
DT
NNP
IN
NP
DT
JJ
NN
VP
PP NP VP
N P
NP
NP VP P
VP
x1:PP x2:NP VP
V P
解散 する
VP
x1:PPx2:NPVB
dissolve
26. 14/06/05 Copyright (C) 2014 by Yusuke Oda, AHC-Lab, IS, NAIST 26
GHKM Algorithm
●
Galley-Hopkins-Kinght-Marcu (GHKM) Algorithm
– Generating STSG synchronous rules (string-to-tree rules) by composing minimal
rules using inside-outside algorithm.
Minimal rule
Syntax tree
1.
Detecting minimal rules
from target syntax trees.
2.
Generating large synchronous
rules by composing minimal
rules.
27. 14/06/05 Copyright (C) 2014 by Yusuke Oda, AHC-Lab, IS, NAIST 27
GHKM: Alignment Span (1)
●
Alignment span :
– Set of indexes of words in source sentence aligned to partial tree
●
Complement alignment span :
– Set of indexes of words in source sentence aligned to other than
●
Closure :
– Minimum range that covers the alignment span
28. 14/06/05 Copyright (C) 2014 by Yusuke Oda, AHC-Lab, IS, NAIST 28
GHKM: Alignment Span (2)
he
will
dissolve
the
diet
in
the
near
future
彼
は
近い
うち
に
国会
を
解散
する
VP VB
NP
PP
DT
NNP
IN
NP
DT
JJ
NN
S
NP
PRP
MD
29. 14/06/05 Copyright (C) 2014 by Yusuke Oda, AHC-Lab, IS, NAIST 29
GHKM: Admissible Node
●
Admissible node:
– Node in target syntax tree that satisfies:
he
will
dissolve
the
diet
in
the
near
future
VP VB
NP
PP
DT
NNP
IN
NP
DT
JJ
NN
S
NP
PRP
MD
彼
は
近い
うち
に
国会
を
解散
する
30. 14/06/05 Copyright (C) 2014 by Yusuke Oda, AHC-Lab, IS, NAIST 30
GHKM: Minimal Rule
●
Split the syntax tree by admissible node
he
will
dissolve
the
diet
in
the
near
future
VP VB
NP
PP
DT
NNP
IN
NP
DT
JJ
NN
S
NP
PRP
MD
彼
は
近い
うち
に
国会
を
解散
する
VP
x1:PP x2:NP x3:VB
x
x3 x2 x1
VP
the near future
x
近い うち
DT JJ NN
31. 14/06/05 Copyright (C) 2014 by Yusuke Oda, AHC-Lab, IS, NAIST 31
Extension for Tree-to-tree Model (1)
●
We need to extract node pairs of two syntax trees that are admissible
each other.
●
First, find admissible nodes in given .
●
A node pair satisfies below then they are
bidirectional admissible:
●
Span :
– Minimum range over sentence that covers all terminal symbols in
32. 14/06/05 Copyright (C) 2014 by Yusuke Oda, AHC-Lab, IS, NAIST 32
Extension for Tree-to-tree Model (2)
dissolve
the
diet
in
the
near
future
近い
うち
に
国会
を
解散
する
VP VB
NP
PP
DT
NNP
IN
NP
DT
JJ
NN
VP
PP NP VP
N P
NP
NP VP P
33. 14/06/05 Copyright (C) 2014 by Yusuke Oda, AHC-Lab, IS, NAIST 33
Features of STSG Model (1)
●
Generative model: likelihoods of translation probability
34. 14/06/05 Copyright (C) 2014 by Yusuke Oda, AHC-Lab, IS, NAIST 34
Features of STSG Model (2)
●
Lexical translation model: goodness of phrase alignment
35. 14/06/05 Copyright (C) 2014 by Yusuke Oda, AHC-Lab, IS, NAIST 35
Features of STSG Model (3)
●
Height penalty: adjusting depth of derivation
●
Internal node penalty: adjusting total size of derivation
●
Some features introduced to Hiero model are also available
36. 14/06/05 Copyright (C) 2014 by Yusuke Oda, AHC-Lab, IS, NAIST 36
Decoding of STSG Model
●
STSG decoding is basically same method as Hiero decoding:
Depends on translation model
37. 14/06/05 Copyright (C) 2014 by Yusuke Oda, AHC-Lab, IS, NAIST 37
Difference of Formalization of Each Model
●
String-to-string model
– Same model as Hiero (SCFG) model.
●
String-to-tree model
– Never use any informations from syntax of source sentence.
●
Tree-to-string model
●
Tree-to-tree model
– Explicitly use syntax informations of source sentence.
– Translation process can be divided into syntax analysis and decoding.
Source
sentence
Syntax tree
of source sentence
Translation
hypothesi(e)s
Syntax
analyzer Decoder
Non-syntax-based
translation
Syntax(tree)-based
translation
38. 14/06/05 Copyright (C) 2014 by Yusuke Oda, AHC-Lab, IS, NAIST 38
Formalization of Syntax-based Translation
●
Syntax-based translation model uses the syntax tree of source
sentence.
●
We can ignore because is already decided while syntax
analysis.
39. 14/06/05 Copyright (C) 2014 by Yusuke Oda, AHC-Lab, IS, NAIST 39
Questions & Discussions