634 research outputs found
Similarity-Based Models of Word Cooccurrence Probabilities
In many applications of natural language processing (NLP) it is necessary to
determine the likelihood of a given word combination. For example, a speech
recognizer may need to determine which of the two word combinations ``eat a
peach'' and ``eat a beach'' is more likely. Statistical NLP methods determine
the likelihood of a word combination from its frequency in a training corpus.
However, the nature of language is such that many word combinations are
infrequent and do not occur in any given corpus. In this work we propose a
method for estimating the probability of such previously unseen word
combinations using available information on ``most similar'' words.
We describe probabilistic word association models based on distributional
word similarity, and apply them to two tasks, language modeling and pseudo-word
disambiguation. In the language modeling task, a similarity-based model is used
to improve probability estimates for unseen bigrams in a back-off language
model. The similarity-based method yields a 20% perplexity improvement in the
prediction of unseen bigrams and statistically significant reductions in
speech-recognition error.
We also compare four similarity-based estimation methods against back-off and
maximum-likelihood estimation methods on a pseudo-word sense disambiguation
task in which we controlled for both unigram and bigram frequency to avoid
giving too much weight to easy-to-disambiguate high-frequency configurations.
The similarity-based methods perform up to 40% better on this particular task.Comment: 26 pages, 5 figure
Source-side context-informed hypothesis alignment for combining outputs from machine translation systems
This paper presents a new hypothesis alignment method for combining outputs of multiple machine translation (MT) systems. Traditional hypothesis alignment algorithms such
as TER, HMM and IHMM do not directly utilise the context information of the source side but rather address the alignment issues via the output data itself. In this paper, a source-side context-informed (SSCI) hypothesis alignment method is proposed to carry out the word alignment and word reordering issues. First of all, the source–target word alignment links are produced as the hidden variables by exporting source phrase spans during the translation decoding process. Secondly, a mapping strategy and normalisation model are employed to acquire the 1-
to-1 alignment links and build the confusion network (CN). The source-side context-based method outperforms the state-of-the-art TERbased alignment model in our experiments
on the WMT09 English-to-French and NIST Chinese-to-English data sets respectively. Experimental results demonstrate that our proposed approach scores consistently among the
best results across different data and language pair conditions
JUNIPR: a Framework for Unsupervised Machine Learning in Particle Physics
In applications of machine learning to particle physics, a persistent
challenge is how to go beyond discrimination to learn about the underlying
physics. To this end, a powerful tool would be a framework for unsupervised
learning, where the machine learns the intricate high-dimensional contours of
the data upon which it is trained, without reference to pre-established labels.
In order to approach such a complex task, an unsupervised network must be
structured intelligently, based on a qualitative understanding of the data. In
this paper, we scaffold the neural network's architecture around a
leading-order model of the physics underlying the data. In addition to making
unsupervised learning tractable, this design actually alleviates existing
tensions between performance and interpretability. We call the framework
JUNIPR: "Jets from UNsupervised Interpretable PRobabilistic models". In this
approach, the set of particle momenta composing a jet are clustered into a
binary tree that the neural network examines sequentially. Training is
unsupervised and unrestricted: the network could decide that the data bears
little correspondence to the chosen tree structure. However, when there is a
correspondence, the network's output along the tree has a direct physical
interpretation. JUNIPR models can perform discrimination tasks, through the
statistically optimal likelihood-ratio test, and they permit visualizations of
discrimination power at each branching in a jet's tree. Additionally, JUNIPR
models provide a probability distribution from which events can be drawn,
providing a data-driven Monte Carlo generator. As a third application, JUNIPR
models can reweight events from one (e.g. simulated) data set to agree with
distributions from another (e.g. experimental) data set.Comment: 37 pages, 24 figure
Resonant Anomaly Detection with Multiple Reference Datasets
An important class of techniques for resonant anomaly detection in high
energy physics builds models that can distinguish between reference and target
datasets, where only the latter has appreciable signal. Such techniques,
including Classification Without Labels (CWoLa) and Simulation Assisted
Likelihood-free Anomaly Detection (SALAD) rely on a single reference dataset.
They cannot take advantage of commonly-available multiple datasets and thus
cannot fully exploit available information. In this work, we propose
generalizations of CWoLa and SALAD for settings where multiple reference
datasets are available, building on weak supervision techniques. We demonstrate
improved performance in a number of settings with realistic and synthetic data.
As an added benefit, our generalizations enable us to provide finite-sample
guarantees, improving on existing asymptotic analyses
A three-pass system combination framework by combining multiple hypothesis alignment methods
So far, many effective hypothesis alignment metrics have been proposed and applied to the system combination, such as TER, HMM, ITER and IHMM. In addition, the Minimum Bayes-risk (MBR) decoding and the confusion network (CN) have become the state-of-the art techniques in system combination. In this paper, we present a three-pass system combination strategy that can combine hypothesis alignment results derived from different alignment metrics to generate a better translation. Firstly the different alignment metrics are carried out to align the backbone and hypotheses, and the individual CN is built corresponding to each alignment results; then we construct a super network by merging the multiple metric-based CN and generate a consensus output. Finally a modified consensus network MBR (ConMBR) approach is employed to search a best translation. Our proposed strategy out performs the best single CN as well as the best single system in our experiments on NIST Chinese-to-English test set
What drives product-service integration? An abductive study of decision-makers’ motives and value strategies
Many firms struggle to successfully translate corporate strategy into value-added solutions for customers by integrating products and services. A particular hurdle is the intrinsic motivation of the people in charge. This study contributes to the microfoundations of servitization literature by exploring what motives and strategies drive decision-makers to pursue product-service integration (PSI). Given the fragmented state of the literature, we follow an abductive approach. First, applying a behavioral strategy lens, we identify the theoretical building blocks to construct a conceptual framework. Next, we collect data of 178 small, Belgian firms to perform an exploratory quantitative analysis. Finally, we develop theory based on the results. Specifically, we find that the need for achievement and affiliation are both directly and positively associated with PSI. Also, achievement-driven people are likely to pursue PSI, originating from a product leadership position. Finally, the power motive is positively associated with operational excellence, but not with PSI
Referent tracking for corporate memories
For corporate memory and enterprise ontology systems to be maximally useful,
they must be freed from certain barriers placed around them by traditional
knowledge management paradigms. This means, above all, that they must mirror
more faithfully those portions of reality which are salient to the workings of the
enterprise, including the changes that occur with the passage of time. The purpose
of this chapter is to demonstrate how theories based on philosophical realism can
contribute to this objective. We discuss how realism-based ontologies (capturing
what is generic) combined with referent tracking (capturing what is specific) can
play a key role in building the robust and useful corporate memories of the future
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