Unnamed: 0 int64 0 41k | title stringlengths 4 274 | category stringlengths 5 18 | summary stringlengths 22 3.66k | theme stringclasses 8
values |
|---|---|---|---|---|
17,802 | Automatic Transferring between Ancient Chinese and Contemporary Chinese | cs.CL | During the long time of development, Chinese language has evolved a great
deal. Native speakers now have difficulty in reading sentences written in
ancient Chinese. In this paper, we propose an unsupervised algorithm that
constructs sentence-aligned ancient-contemporary pairs out of the abundant
passage-aligned corpus.... | computer science |
17,803 | Neural Architectures for Open-Type Relation Argument Extraction | cs.CL | In this work, we introduce the task of Open-Type Relation Argument Extraction
(ORAE): Given a corpus, a query entity Q and a knowledge base relation (e.g.,"Q
authored notable work with title X"), the model has to extract an argument of
non-standard entity type (entities that cannot be extracted by a standard named
enti... | computer science |
17,804 | Self-Attention with Relative Position Representations | cs.CL | Relying entirely on an attention mechanism, the Transformer introduced by
Vaswani et al. (2017) achieves state-of-the-art results for machine
translation. In contrast to recurrent and convolutional neural networks, it
does not explicitly model relative or absolute position information in its
structure. Instead, it requ... | computer science |
17,805 | CliNER 2.0: Accessible and Accurate Clinical Concept Extraction | cs.CL | Clinical notes often describe important aspects of a patient's stay and are
therefore critical to medical research. Clinical concept extraction (CCE) of
named entities - such as problems, tests, and treatments - aids in forming an
understanding of notes and provides a foundation for many downstream clinical
decision-ma... | computer science |
17,806 | An End-to-End Goal-Oriented Dialog System with a Generative Natural
Language Response Generation | cs.CL | Recently advancements in deep learning allowed the development of end-to-end
trained goal-oriented dialog systems. Although these systems already achieve
good performance, some simplifications limit their usage in real-life
scenarios.
In this work, we address two of these limitations: ignoring positional
information ... | computer science |
17,807 | Multimodal Emoji Prediction | cs.CL | Emojis are small images that are commonly included in social media text
messages. The combination of visual and textual content in the same message
builds up a modern way of communication, that automatic systems are not used to
deal with. In this paper we extend recent advances in emoji prediction by
putting forward a ... | computer science |
17,808 | Translating Questions into Answers using DBPedia n-triples | cs.CL | In this paper we present a question answering system using a neural network
to interpret questions learned from the DBpedia repository. We train a
sequence-to-sequence neural network model with n-triples extracted from the
DBpedia Infobox Properties. Since these properties do not represent the natural
language, we furt... | computer science |
17,809 | How Images Inspire Poems: Generating Classical Chinese Poetry from
Images with Memory Networks | cs.CL | With the recent advances of neural models and natural language processing,
automatic generation of classical Chinese poetry has drawn significant
attention due to its artistic and cultural value. Previous works mainly focus
on generating poetry given keywords or other text information, while visual
inspirations for poe... | computer science |
17,810 | Fact Checking in Community Forums | cs.CL | Community Question Answering (cQA) forums are very popular nowadays, as they
represent effective means for communities around particular topics to share
information. Unfortunately, this information is not always factual. Thus, here
we explore a new dimension in the context of cQA, which has been ignored so
far: checkin... | computer science |
17,811 | Neural Fine-Grained Entity Type Classification with Hierarchy-Aware Loss | cs.CL | The task of Fine-grained Entity Type Classification (FETC) consists of
assigning types from a hierarchy to entity mentions in text. The
state-of-the-art relies on distant supervision and is susceptible to noisy
labels that can be out-of-context or overly-specific relative to the training
example. Previous methods that ... | computer science |
17,812 | An Unsupervised Model with Attention Autoencoders for Question Retrieval | cs.CL | Question retrieval is a crucial subtask for community question answering.
Previous research focus on supervised models which depend heavily on training
data and manual feature engineering. In this paper, we propose a novel
unsupervised framework, namely reduced attentive matching network (RAMN), to
compute semantic mat... | computer science |
17,813 | The Importance of Being Recurrent for Modeling Hierarchical Structure | cs.CL | Recent work has shown that recurrent neural networks (RNNs) can implicitly
capture and exploit hierarchical information when trained to solve common
natural language processing tasks such as language modeling (Linzen et al.,
2016) and neural machine translation (Shi et al., 2016). In contrast, the
ability to model stru... | computer science |
17,814 | Hate Speech Detection: A Solved Problem? The Challenging Case of Long
Tail on Twitter | cs.CL | In recent years, the increasing propagation of hate speech on social media
and the urgent need for effective counter-measures have drawn significant
investment from governments, companies, and empirical research. Despite a large
number of emerging, scientific studies to address the problem, the performance
of existing ... | computer science |
17,815 | IcoRating: A Deep-Learning System for Scam ICO Identification | cs.CL | Cryptocurrencies (or digital tokens, digital currencies, e.g., BTC, ETH, XRP,
NEO) have been rapidly gaining ground in use, value, and understanding among
the public, bringing astonishing profits to investors. Unlike other money and
banking systems, most digital tokens do not require central authorities. Being
decentra... | computer science |
17,816 | We Built a Fake News & Click-bait Filter: What Happened Next Will Blow
Your Mind! | cs.CL | It is completely amazing! Fake news and click-baits have totally invaded the
cyber space. Let us face it: everybody hates them for three simple reasons.
Reason #2 will absolutely amaze you. What these can achieve at the time of
election will completely blow your mind! Now, we all agree, this cannot go on,
you know, som... | computer science |
17,817 | Language Identification of Bengali-English Code-Mixed data using
Character & Phonetic based LSTM Models | cs.CL | Language identification of social media text still remains a challenging task
due to properties like code-mixing and inconsistent phonetic transliterations.
In this paper, we present a supervised learning approach for language
identification at the word level of low resource Bengali-English code-mixed
data taken from s... | computer science |
17,818 | Path of Vowel Raising in Chengdu Dialect of Mandarin | cs.CL | He and Rao (2013) reported a raising phenomenon of /a/ in /Xan/ (X being a
consonant or a vowel) in Chengdu dialect of Mandarin, i.e. /a/ is realized as
[epsilon] for young speakers but [ae] for older speakers, but they offered no
acoustic analysis. We designed an acoustic study that examined the realization
of /Xan/ i... | computer science |
17,819 | Generating Bilingual Pragmatic Color References | cs.CL | Contextual influences on language exhibit substantial language-independent
regularities; for example, we are more verbose in situations that require finer
distinctions. However, these regularities are sometimes obscured by semantic
and syntactic differences. Using a newly-collected dataset of color reference
games in M... | computer science |
17,820 | Preparing Bengali-English Code-Mixed Corpus for Sentiment Analysis of
Indian Languages | cs.CL | Analysis of informative contents and sentiments of social users has been
attempted quite intensively in the recent past. Most of the systems are usable
only for monolingual data and fails or gives poor results when used on data
with code-mixing property. To gather attention and encourage researchers to
work on this cri... | computer science |
17,821 | Entity-Aware Language Model as an Unsupervised Reranker | cs.CL | In language modeling, it is difficult to incorporate entity relationships
from a knowledge-base. One solution is to use a reranker trained with global
features, in which global features are derived from n-best lists. However,
training such a reranker requires manually annotated n-best lists, which is
expensive to obtai... | computer science |
17,822 | Semantic Parsing Natural Language into SPARQL: Improving Target Language
Representation with Neural Attention | cs.CL | Semantic parsing is the process of mapping a natural language sentence into a
formal representation of its meaning. In this work we use the neural network
approach to transform natural language sentence into a query to an ontology
database in the SPARQL language. This method does not rely on handcraft-rules,
high-quali... | computer science |
17,823 | A Feature-Rich Vietnamese Named-Entity Recognition Model | cs.CL | In this paper, we present a feature-based named-entity recognition (NER)
model that achieves the start-of-the-art accuracy for Vietnamese language. We
combine word, word-shape features, PoS, chunk, Brown-cluster-based features,
and word-embedding-based features in the Conditional Random Fields (CRF) model.
We also expl... | computer science |
17,824 | Monitoring Targeted Hate in Online Environments | cs.CL | Hateful comments, swearwords and sometimes even death threats are becoming a
reality for many people today in online environments. This is especially true
for journalists, politicians, artists, and other public figures. This paper
describes how hate directed towards individuals can be measured in online
environments us... | computer science |
17,825 | Enhanced Word Representations for Bridging Anaphora Resolution | cs.CL | Most current models of word representations(e.g.,GloVe) have successfully
captured fine-grained semantics. However, semantic similarity exhibited in
these word embeddings is not suitable for resolving bridging anaphora, which
requires the knowledge of associative similarity (i.e., relatedness) instead of
semantic simil... | computer science |
17,826 | Neural Lattice Language Models | cs.CL | In this work, we propose a new language modeling paradigm that has the
ability to perform both prediction and moderation of information flow at
multiple granularities: neural lattice language models. These models construct
a lattice of possible paths through a sentence and marginalize across this
lattice to calculate s... | computer science |
17,827 | MCScript: A Novel Dataset for Assessing Machine Comprehension Using
Script Knowledge | cs.CL | We introduce a large dataset of narrative texts and questions about these
texts, intended to be used in a machine comprehension task that requires
reasoning using commonsense knowledge. Our dataset complements similar datasets
in that we focus on stories about everyday activities, such as going to the
movies or working... | computer science |
17,828 | FEVER: a large-scale dataset for Fact Extraction and VERification | cs.CL | Unlike other tasks and despite recent interest, research in textual claim
verification has been hindered by the lack of large-scale manually annotated
datasets. In this paper we introduce a new publicly available dataset for
verification against textual sources, FEVER: Fact Extraction and VERification.
It consists of 1... | computer science |
17,829 | SentEval: An Evaluation Toolkit for Universal Sentence Representations | cs.CL | We introduce SentEval, a toolkit for evaluating the quality of universal
sentence representations. SentEval encompasses a variety of tasks, including
binary and multi-class classification, natural language inference and sentence
similarity. The set of tasks was selected based on what appears to be the
community consens... | computer science |
17,830 | Challenges in Discriminating Profanity from Hate Speech | cs.CL | In this study we approach the problem of distinguishing general profanity
from hate speech in social media, something which has not been widely
considered. Using a new dataset annotated specifically for this task, we employ
supervised classification along with a set of features that includes n-grams,
skip-grams and clu... | computer science |
17,831 | A Simple and Effective Approach to the Story Cloze Test | cs.CL | In the Story Cloze Test, a system is presented with a 4-sentence prompt to a
story, and must determine which one of two potential endings is the 'right'
ending to the story. Previous work has shown that ignoring the training set and
training a model on the validation set can achieve high accuracy on this task
due to st... | computer science |
17,832 | Advancing Connectionist Temporal Classification With Attention Modeling | cs.CL | In this study, we propose advancing all-neural speech recognition by directly
incorporating attention modeling within the Connectionist Temporal
Classification (CTC) framework. In particular, we derive new context vectors
using time convolution features to model attention as part of the CTC network.
To further improve ... | computer science |
17,833 | Advancing Acoustic-to-Word CTC Model | cs.CL | The acoustic-to-word model based on the connectionist temporal classification
(CTC) criterion was shown as a natural end-to-end (E2E) model directly
targeting words as output units. However, the word-based CTC model suffers from
the out-of-vocabulary (OOV) issue as it can only model limited number of words
in the outpu... | computer science |
17,834 | Achieving Human Parity on Automatic Chinese to English News Translation | cs.CL | Machine translation has made rapid advances in recent years. Millions of
people are using it today in online translation systems and mobile applications
in order to communicate across language barriers. The question naturally arises
whether such systems can approach or achieve parity with human translations. In
this pa... | computer science |
17,835 | Word2Bits - Quantized Word Vectors | cs.CL | Word vectors require significant amounts of memory and storage, posing issues
to resource limited devices like mobile phones and GPUs. We show that high
quality quantized word vectors using 1-2 bits per parameter can be learned by
introducing a quantization function into Word2Vec. We furthermore show that
training with... | computer science |
17,836 | HFL-RC System at SemEval-2018 Task 11: Hybrid Multi-Aspects Model for
Commonsense Reading Comprehension | cs.CL | This paper describes the system which got the state-of-the-art results at
SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge. In
this paper, we present a neural network called Hybrid Multi-Aspects (HMA)
model, which mimic the human's intuitions on dealing with the multiple-choice
reading comprehens... | computer science |
17,837 | Structure Regularized Neural Network for Entity Relation Classification
for Chinese Literature Text | cs.CL | Relation classification is an important semantic processing task in the field
of natural language processing. In this paper, we propose the task of relation
classification for Chinese literature text. A new dataset of Chinese literature
text is constructed to facilitate the study in this task. We present a novel
model,... | computer science |
17,838 | RUSSE'2018: A Shared Task on Word Sense Induction for the Russian
Language | cs.CL | The paper describes the results of the first shared task on word sense
induction (WSI) for the Russian language. While similar shared tasks were
conducted in the past for some Romance and Germanic languages, we explore the
performance of sense induction and disambiguation methods for a Slavic language
that shares many ... | computer science |
17,839 | RUSSE: The First Workshop on Russian Semantic Similarity | cs.CL | The paper gives an overview of the Russian Semantic Similarity Evaluation
(RUSSE) shared task held in conjunction with the Dialogue 2015 conference.
There exist a lot of comparative studies on semantic similarity, yet no
analysis of such measures was ever performed for the Russian language.
Exploring this problem for t... | computer science |
17,840 | Enriching Frame Representations with Distributionally Induced Senses | cs.CL | We introduce a new lexical resource that enriches the Framester knowledge
graph, which links Framnet, WordNet, VerbNet and other resources, with semantic
features from text corpora. These features are extracted from distributionally
induced sense inventories and subsequently linked to the manually-constructed
frame rep... | computer science |
17,841 | RankME: Reliable Human Ratings for Natural Language Generation | cs.CL | Human evaluation for natural language generation (NLG) often suffers from
inconsistent user ratings. While previous research tends to attribute this
problem to individual user preferences, we show that the quality of human
judgements can also be improved by experimental design. We present a novel
rank-based magnitude e... | computer science |
17,842 | Decision support with text-based emotion recognition: Deep learning for
affective computing | cs.CL | Emotions widely affect the decision-making of humans and, hence, affective
computing takes emotional states into account with the goal of tailoring
decision support to individuals. However, the accurate recognition of emotions
within narrative materials presents a challenging undertaking due to the
complexity and ambig... | computer science |
17,843 | Experiments with Neural Networks for Small and Large Scale Authorship
Verification | cs.CL | We propose two models for a special case of authorship verification problem.
The task is to investigate whether the two documents of a given pair are
written by the same author. We consider the authorship verification problem for
both small and large scale datasets. The underlying small-scale problem has two
main chall... | computer science |
17,844 | Dear Sir or Madam, May I introduce the YAFC Corpus: Corpus, Benchmarks
and Metrics for Formality Style Transfer | cs.CL | Style transfer is the task of automatically transforming a piece of text in
one particular style into another. A major barrier to progress in this field
has been a lack of training and evaluation datasets, as well as benchmarks and
automatic metrics. In this work, we create the largest corpus for a particular
stylistic... | computer science |
17,845 | Sentiment Analysis of Code-Mixed Indian Languages: An Overview of
SAIL_Code-Mixed Shared Task @ICON-2017 | cs.CL | Sentiment analysis is essential in many real-world applications such as
stance detection, review analysis, recommendation system, and so on. Sentiment
analysis becomes more difficult when the data is noisy and collected from
social media. India is a multilingual country; people use more than one
languages to communicat... | computer science |
17,846 | Acoustic feature learning using cross-domain articulatory measurements | cs.CL | Previous work has shown that it is possible to improve speech recognition by
learning acoustic features from paired acoustic-articulatory data, for example
by using canonical correlation analysis (CCA) or its deep extensions. One
limitation of this prior work is that the learned feature models are difficult
to port to ... | computer science |
17,847 | Polyglot Semantic Parsing in APIs | cs.CL | Traditional approaches to semantic parsing (SP) work by training individual
models for each available parallel dataset of text-meaning pairs. In this
paper, we explore the idea of polyglot semantic translation, or learning
semantic parsing models that are trained on multiple datasets and natural
languages. In particula... | computer science |
17,848 | Controlling Decoding for More Abstractive Summaries with Copy-Based
Networks | cs.CL | Attention-based neural abstractive summarization systems equipped with copy
mechanisms have shown promising results. Despite this success, it has been
noticed that such a system generates a summary by mostly, if not entirely,
copying over phrases, sentences, and sometimes multiple consecutive sentences
from an input pa... | computer science |
17,849 | Learning to Generate Wikipedia Summaries for Underserved Languages from
Wikidata | cs.CL | While Wikipedia exists in 287 languages, its content is unevenly distributed
among them. In this work, we investigate the generation of open domain
Wikipedia summaries in underserved languages using structured data from
Wikidata. To this end, we propose a neural network architecture equipped with
copy actions that lear... | computer science |
17,850 | Dynamic Natural Language Processing with Recurrence Quantification
Analysis | cs.CL | Writing and reading are dynamic processes. As an author composes a text, a
sequence of words is produced. This sequence is one that, the author hopes,
causes a revisitation of certain thoughts and ideas in others. These processes
of composition and revisitation by readers are ordered in time. This means that
text itsel... | computer science |
17,851 | Why not be Versatile? Applications of the SGNMT Decoder for Machine
Translation | cs.CL | SGNMT is a decoding platform for machine translation which allows paring
various modern neural models of translation with different kinds of constraints
and symbolic models. In this paper, we describe three use cases in which SGNMT
is currently playing an active role: (1) teaching as SGNMT is being used for
course work... | computer science |
17,852 | eSCAPE: a Large-scale Synthetic Corpus for Automatic Post-Editing | cs.CL | Training models for the automatic correction of machine-translated text
usually relies on data consisting of (source, MT, human post- edit) triplets
providing, for each source sentence, examples of translation errors with the
corresponding corrections made by a human post-editor. Ideally, a large amount
of data of this... | computer science |
17,853 | Expressivity in TTS from Semantics and Pragmatics | cs.CL | In this paper we present ongoing work to produce an expressive TTS reader
that can be used both in text and dialogue applications. The system called
SPARSAR has been used to read (English) poetry so far but it can now be applied
to any text. The text is fully analyzed both at phonetic and phonological
level, and at syn... | computer science |
17,854 | UnibucKernel: A kernel-based learning method for complex word
identification | cs.CL | In this paper, we present a kernel-based learning approach for the 2018
Complex Word Identification (CWI) Shared Task. Our approach is based on
combining multiple low-level features, such as character n-grams, with
high-level semantic features that are either automatically learned using word
embeddings or extracted fro... | computer science |
17,855 | AllenNLP: A Deep Semantic Natural Language Processing Platform | cs.CL | This paper describes AllenNLP, a platform for research on deep learning
methods in natural language understanding. AllenNLP is designed to support
researchers who want to build novel language understanding models quickly and
easily. It is built on top of PyTorch, allowing for dynamic computation graphs,
and provides (1... | computer science |
17,856 | InfyNLP at SMM4H Task 2: Stacked Ensemble of Shallow Convolutional
Neural Networks for Identifying Personal Medication Intake from Twitter | cs.CL | This paper describes Infosys's participation in the "2nd Social Media Mining
for Health Applications Shared Task at AMIA, 2017, Task 2". Mining social media
messages for health and drug related information has received significant
interest in pharmacovigilance research. This task targets at developing
automated classif... | computer science |
17,857 | $ρ$-hot Lexicon Embedding-based Two-level LSTM for Sentiment Analysis | cs.CL | Sentiment analysis is a key component in various text mining applications.
Numerous sentiment classification techniques, including conventional and deep
learning-based methods, have been proposed in the literature. In most existing
methods, a high-quality training set is assumed to be given. Nevertheless,
constructing ... | computer science |
17,858 | Olive Oil is Made of Olives, Baby Oil is Made for Babies: Interpreting
Noun Compounds using Paraphrases in a Neural Model | cs.CL | Automatic interpretation of the relation between the constituents of a noun
compound, e.g. olive oil (source) and baby oil (purpose) is an important task
for many NLP applications. Recent approaches are typically based on either
noun-compound representations or paraphrases. While the former has initially
shown promisin... | computer science |
17,859 | Quality expectations of machine translation | cs.CL | Machine Translation (MT) is being deployed for a range of use-cases by
millions of people on a daily basis. There should, therefore, be no doubt as to
the utility of MT. However, not everyone is convinced that MT can be useful,
especially as a productivity enhancer for human translators. In this chapter, I
address this... | computer science |
17,860 | A Feature-Based Model for Nested Named-Entity Recognition at VLSP-2018
NER Evaluation Campaign | cs.CL | In this report, we describe our participant named-entity recognition system
at VLSP 2018 evaluation campaign. We formalized the task as a sequence labeling
problem using BIO encoding scheme. We applied a feature-based model which
combines word, word-shape features, Brown-cluster-based features, and
word-embedding-based... | computer science |
17,861 | Context is Everything: Finding Meaning Statistically in Semantic Spaces | cs.CL | This paper introduces a simple and explicit measure of word importance in a
global context, including very small contexts (10+ sentences). After generating
a word-vector space containing both 2-gram clauses and single tokens, it became
clear that more contextually significant words disproportionately define clause
mean... | computer science |
17,862 | Minimum Description Length Induction, Bayesianism, and Kolmogorov
Complexity | cs.LG | The relationship between the Bayesian approach and the minimum description
length approach is established. We sharpen and clarify the general modeling
principles MDL and MML, abstracted as the ideal MDL principle and defined from
Bayes's rule by means of Kolmogorov complexity. The basic condition under which
the ideal ... | computer science |
17,863 | Algorithmic Theories of Everything | cs.AI | The probability distribution P from which the history of our universe is
sampled represents a theory of everything or TOE. We assume P is formally
describable. Since most (uncountably many) distributions are not, this imposes
a strong inductive bias. We show that P(x) is small for any universe x lacking
a short descrip... | computer science |
17,864 | Prediction and Modularity in Dynamical Systems | nlin.AO | Identifying and understanding modular organizations is centrally important in
the study of complex systems. Several approaches to this problem have been
advanced, many framed in information-theoretic terms. Our treatment starts from
the complementary point of view of statistical modeling and prediction of
dynamical sys... | computer science |
17,865 | An Augmented Lagrangian Approach for Sparse Principal Component Analysis | math.OC | Principal component analysis (PCA) is a widely used technique for data
analysis and dimension reduction with numerous applications in science and
engineering. However, the standard PCA suffers from the fact that the principal
components (PCs) are usually linear combinations of all the original variables,
and it is thus... | computer science |
17,866 | Proceedings of the second "international Traveling Workshop on
Interactions between Sparse models and Technology" (iTWIST'14) | cs.NA | The implicit objective of the biennial "international - Traveling Workshop on
Interactions between Sparse models and Technology" (iTWIST) is to foster
collaboration between international scientific teams by disseminating ideas
through both specific oral/poster presentations and free discussions. For its
second edition,... | computer science |
17,867 | Dynamic Backtracking | cs.AI | Because of their occasional need to return to shallow points in a search
tree, existing backtracking methods can sometimes erase meaningful progress
toward solving a search problem. In this paper, we present a method by which
backtrack points can be moved deeper in the search space, thereby avoiding this
difficulty. Th... | computer science |
17,868 | A Market-Oriented Programming Environment and its Application to
Distributed Multicommodity Flow Problems | cs.AI | Market price systems constitute a well-understood class of mechanisms that
under certain conditions provide effective decentralization of decision making
with minimal communication overhead. In a market-oriented programming approach
to distributed problem solving, we derive the activities and resource
allocations for a... | computer science |
17,869 | An Empirical Analysis of Search in GSAT | cs.AI | We describe an extensive study of search in GSAT, an approximation procedure
for propositional satisfiability. GSAT performs greedy hill-climbing on the
number of satisfied clauses in a truth assignment. Our experiments provide a
more complete picture of GSAT's search than previous accounts. We describe in
detail the t... | computer science |
17,870 | The Difficulties of Learning Logic Programs with Cut | cs.AI | As real logic programmers normally use cut (!), an effective learning
procedure for logic programs should be able to deal with it. Because the cut
predicate has only a procedural meaning, clauses containing cut cannot be
learned using an extensional evaluation method, as is done in most learning
systems. On the other h... | computer science |
17,871 | Software Agents: Completing Patterns and Constructing User Interfaces | cs.AI | To support the goal of allowing users to record and retrieve information,
this paper describes an interactive note-taking system for pen-based computers
with two distinctive features. First, it actively predicts what the user is
going to write. Second, it automatically constructs a custom, button-box user
interface on ... | computer science |
17,872 | Decidable Reasoning in Terminological Knowledge Representation Systems | cs.AI | Terminological knowledge representation systems (TKRSs) are tools for
designing and using knowledge bases that make use of terminological languages
(or concept languages). We analyze from a theoretical point of view a TKRS
whose capabilities go beyond the ones of presently available TKRSs. The new
features studied, oft... | computer science |
17,873 | Teleo-Reactive Programs for Agent Control | cs.AI | A formalism is presented for computing and organizing actions for autonomous
agents in dynamic environments. We introduce the notion of teleo-reactive (T-R)
programs whose execution entails the construction of circuitry for the
continuous computation of the parameters and conditions on which agent action
is based. In a... | computer science |
17,874 | Learning the Past Tense of English Verbs: The Symbolic Pattern
Associator vs. Connectionist Models | cs.AI | Learning the past tense of English verbs - a seemingly minor aspect of
language acquisition - has generated heated debates since 1986, and has become
a landmark task for testing the adequacy of cognitive modeling. Several
artificial neural networks (ANNs) have been implemented, and a challenge for
better symbolic model... | computer science |
17,875 | Substructure Discovery Using Minimum Description Length and Background
Knowledge | cs.AI | The ability to identify interesting and repetitive substructures is an
essential component to discovering knowledge in structural data. We describe a
new version of our SUBDUE substructure discovery system based on the minimum
description length principle. The SUBDUE system discovers substructures that
compress the ori... | computer science |
17,876 | Bias-Driven Revision of Logical Domain Theories | cs.AI | The theory revision problem is the problem of how best to go about revising a
deficient domain theory using information contained in examples that expose
inaccuracies. In this paper we present our approach to the theory revision
problem for propositional domain theories. The approach described here, called
PTR, uses pr... | computer science |
17,877 | Exploring the Decision Forest: An Empirical Investigation of Occam's
Razor in Decision Tree Induction | cs.AI | We report on a series of experiments in which all decision trees consistent
with the training data are constructed. These experiments were run to gain an
understanding of the properties of the set of consistent decision trees and the
factors that affect the accuracy of individual trees. In particular, we
investigated t... | computer science |
17,878 | A Semantics and Complete Algorithm for Subsumption in the CLASSIC
Description Logic | cs.AI | This paper analyzes the correctness of the subsumption algorithm used in
CLASSIC, a description logic-based knowledge representation system that is
being used in practical applications. In order to deal efficiently with
individuals in CLASSIC descriptions, the developers have had to use an
algorithm that is incomplete ... | computer science |
17,879 | Applying GSAT to Non-Clausal Formulas | cs.AI | In this paper we describe how to modify GSAT so that it can be applied to
non-clausal formulas. The idea is to use a particular ``score'' function which
gives the number of clauses of the CNF conversion of a formula which are false
under a given truth assignment. Its value is computed in linear time, without
constructi... | computer science |
17,880 | Random Worlds and Maximum Entropy | cs.AI | Given a knowledge base KB containing first-order and statistical facts, we
consider a principled method, called the random-worlds method, for computing a
degree of belief that some formula Phi holds given KB. If we are reasoning
about a world or system consisting of N individuals, then we can consider all
possible worl... | computer science |
17,881 | Pattern Matching and Discourse Processing in Information Extraction from
Japanese Text | cs.AI | Information extraction is the task of automatically picking up information of
interest from an unconstrained text. Information of interest is usually
extracted in two steps. First, sentence level processing locates relevant
pieces of information scattered throughout the text; second, discourse
processing merges corefer... | computer science |
17,882 | A System for Induction of Oblique Decision Trees | cs.AI | This article describes a new system for induction of oblique decision trees.
This system, OC1, combines deterministic hill-climbing with two forms of
randomization to find a good oblique split (in the form of a hyperplane) at
each node of a decision tree. Oblique decision tree methods are tuned
especially for domains i... | computer science |
17,883 | On Planning while Learning | cs.AI | This paper introduces a framework for Planning while Learning where an agent
is given a goal to achieve in an environment whose behavior is only partially
known to the agent. We discuss the tractability of various plan-design
processes. We show that for a large natural class of Planning while Learning
systems, a plan c... | computer science |
17,884 | Wrap-Up: a Trainable Discourse Module for Information Extraction | cs.AI | The vast amounts of on-line text now available have led to renewed interest
in information extraction (IE) systems that analyze unrestricted text,
producing a structured representation of selected information from the text.
This paper presents a novel approach that uses machine learning to acquire
knowledge for some of... | computer science |
17,885 | Operations for Learning with Graphical Models | cs.AI | This paper is a multidisciplinary review of empirical, statistical learning
from a graphical model perspective. Well-known examples of graphical models
include Bayesian networks, directed graphs representing a Markov chain, and
undirected networks representing a Markov field. These graphical models are
extended to mode... | computer science |
17,886 | Total-Order and Partial-Order Planning: A Comparative Analysis | cs.AI | For many years, the intuitions underlying partial-order planning were largely
taken for granted. Only in the past few years has there been renewed interest
in the fundamental principles underlying this paradigm. In this paper, we
present a rigorous comparative analysis of partial-order and total-order
planning by focus... | computer science |
17,887 | Solving Multiclass Learning Problems via Error-Correcting Output Codes | cs.AI | Multiclass learning problems involve finding a definition for an unknown
function f(x) whose range is a discrete set containing k > 2 values (i.e., k
``classes''). The definition is acquired by studying collections of training
examples of the form [x_i, f (x_i)]. Existing approaches to multiclass learning
problems in... | computer science |
17,888 | A Domain-Independent Algorithm for Plan Adaptation | cs.AI | The paradigms of transformational planning, case-based planning, and plan
debugging all involve a process known as plan adaptation - modifying or
repairing an old plan so it solves a new problem. In this paper we provide a
domain-independent algorithm for plan adaptation, demonstrate that it is sound,
complete, and sys... | computer science |
17,889 | Truncating Temporal Differences: On the Efficient Implementation of
TD(lambda) for Reinforcement Learning | cs.AI | Temporal difference (TD) methods constitute a class of methods for learning
predictions in multi-step prediction problems, parameterized by a recency
factor lambda. Currently the most important application of these methods is to
temporal credit assignment in reinforcement learning. Well known reinforcement
learning alg... | computer science |
17,890 | Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic
Decision Tree Induction Algorithm | cs.AI | This paper introduces ICET, a new algorithm for cost-sensitive
classification. ICET uses a genetic algorithm to evolve a population of biases
for a decision tree induction algorithm. The fitness function of the genetic
algorithm is the average cost of classification when using the decision tree,
including both the cost... | computer science |
17,891 | Rerepresenting and Restructuring Domain Theories: A Constructive
Induction Approach | cs.AI | Theory revision integrates inductive learning and background knowledge by
combining training examples with a coarse domain theory to produce a more
accurate theory. There are two challenges that theory revision and other
theory-guided systems face. First, a representation language appropriate for
the initial theory may... | computer science |
17,892 | Using Pivot Consistency to Decompose and Solve Functional CSPs | cs.AI | Many studies have been carried out in order to increase the search efficiency
of constraint satisfaction problems; among them, some make use of structural
properties of the constraint network; others take into account semantic
properties of the constraints, generally assuming that all the constraints
possess the given ... | computer science |
17,893 | Adaptive Load Balancing: A Study in Multi-Agent Learning | cs.AI | We study the process of multi-agent reinforcement learning in the context of
load balancing in a distributed system, without use of either central
coordination or explicit communication. We first define a precise framework in
which to study adaptive load balancing, important features of which are its
stochastic nature ... | computer science |
17,894 | Provably Bounded-Optimal Agents | cs.AI | Since its inception, artificial intelligence has relied upon a theoretical
foundation centered around perfect rationality as the desired property of
intelligent systems. We argue, as others have done, that this foundation is
inadequate because it imposes fundamentally unsatisfiable requirements. As a
result, there has ... | computer science |
17,895 | Pac-Learning Recursive Logic Programs: Efficient Algorithms | cs.AI | We present algorithms that learn certain classes of function-free recursive
logic programs in polynomial time from equivalence queries. In particular, we
show that a single k-ary recursive constant-depth determinate clause is
learnable. Two-clause programs consisting of one learnable recursive clause and
one constant-d... | computer science |
17,896 | Pac-learning Recursive Logic Programs: Negative Results | cs.AI | In a companion paper it was shown that the class of constant-depth
determinate k-ary recursive clauses is efficiently learnable. In this paper we
present negative results showing that any natural generalization of this class
is hard to learn in Valiant's model of pac-learnability. In particular, we show
that the follow... | computer science |
17,897 | FLECS: Planning with a Flexible Commitment Strategy | cs.AI | There has been evidence that least-commitment planners can efficiently handle
planning problems that involve difficult goal interactions. This evidence has
led to the common belief that delayed-commitment is the "best" possible
planning strategy. However, we recently found evidence that eager-commitment
planners can ha... | computer science |
17,898 | Induction of First-Order Decision Lists: Results on Learning the Past
Tense of English Verbs | cs.AI | This paper presents a method for inducing logic programs from examples that
learns a new class of concepts called first-order decision lists, defined as
ordered lists of clauses each ending in a cut. The method, called FOIDL, is
based on FOIL (Quinlan, 1990) but employs intensional background knowledge and
avoids the n... | computer science |
17,899 | Building and Refining Abstract Planning Cases by Change of
Representation Language | cs.AI | ion is one of the most promising approaches to improve the performance of
problem solvers. In several domains abstraction by dropping sentences of a
domain description -- as used in most hierarchical planners -- has proven
useful. In this paper we present examples which illustrate significant
drawbacks of abstraction b... | computer science |
17,900 | Using Qualitative Hypotheses to Identify Inaccurate Data | cs.AI | Identifying inaccurate data has long been regarded as a significant and
difficult problem in AI. In this paper, we present a new method for identifying
inaccurate data on the basis of qualitative correlations among related data.
First, we introduce the definitions of related data and qualitative
correlations among rela... | computer science |
17,901 | An Integrated Framework for Learning and Reasoning | cs.AI | Learning and reasoning are both aspects of what is considered to be
intelligence. Their studies within AI have been separated historically,
learning being the topic of machine learning and neural networks, and reasoning
falling under classical (or symbolic) AI. However, learning and reasoning are
in many ways interdepe... | computer science |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.