Unsupervised inference

Part 2 of ebook "The elements of statistical learning: Data mining, inference, and prediction (Second edition)" provides readers with contents including: Chapter 10 - Boosting and additive trees; Chapter 11 - Neural networks; Chapter 12 - Support vector machines and flexible discriminants; Chapter 13 - Prototype methods and nearest-neighbors; Chapter 14 - Unsupervised learning; Chapter 15 - Random forests; Chapter 16 - Ensemble learning; Chapter 17 - Undirected graphical models; Chapter 18 - High-dimensional problems p ≫ N;...

409p daonhiennhien 03-07-2024 4 1   Download

Optimal integration of transcriptomics data and associated spatial information is essential towards fully exploiting spatial transcriptomics to dissect tissue heterogeneity and map out intercellular communications.

15p vicwell 29-02-2024 5 2   Download

Predication of gene regularity network (GRN) from expression data is a challenging task. There are many methods that have been developed to address this challenge ranging from supervised to unsupervised methods. Most promising methods are based on support vector machine (SVM).

7p vikentucky2711 26-11-2020 12 1   Download

Event extraction from the biomedical literature is one of the most actively researched areas in biomedical text mining and natural language processing. However, most approaches have focused on events within single sentence boundaries, and have thus paid much less attention to events spanning multiple sentences.

22p vicolorado2711 22-10-2020 9 0   Download

In this paper, we present ppiGReMLIN, a graph based strategy to infer interaction patterns in a set of protein-protein complexes. Our method combines an unsupervised learning strategy with frequent subgraph mining in order to detect conserved structural arrangements (patterns) based on the physicochemical properties of atoms on protein interfaces.

25p vicolorado2711 22-10-2020 14 0   Download

Most organisms cannot be cultivated, as they live in unique ecological conditions that cannot be mimicked in the lab. Understanding the functionality of those organisms’ genes and their interactions by performing large-scale measurements of transcription levels, protein-protein interactions or metabolism, is extremely difficult and, in some cases, impossible.

17p vicolorado2711 22-10-2020 10 0   Download

GOD (General Ontology Discovery) is an unsupervised system to extract semantic relations among domain specific entities and concepts from texts. Operationally, it acts as a search engine returning a set of true predicates regarding the query instead of the usual ranked list of relevant documents. Our approach relies on two basic assumptions: (i) paradigmatic relations can be established only among terms in the same Semantic Domain an (ii) they can be inferred from texts by analyzing the Subject-Verb-Object patterns where two domain specific terms co-occur. ...

4p bunthai_1 06-05-2013 51 1   Download

We present a method for unsupervised topic modelling which adapts methods used in document classification (Blei et al., 2003; Griffiths and Steyvers, 2004) to unsegmented multi-party discourse transcripts. We show how Bayesian inference in this generative model can be used to simultaneously address the problems of topic segmentation and topic identification: automatically segmenting multi-party meetings into topically coherent segments with performance which compares well with previous unsupervised segmentation-only methods (Galley et al.

8p hongvang_1 16-04-2013 46 1   Download

Variational EM has become a popular technique in probabilistic NLP with hidden variables. Commonly, for computational tractability, we make strong independence assumptions, such as the meanfield assumption, in approximating posterior distributions over hidden variables. We show how a looser restriction on the approximate posterior, requiring it to be a mixture, can help inject prior knowledge to exploit soft constraints during the variational E-step. We show that empirically, injecting prior knowledge improves performance on an unsupervised Chinese grammar induction task. ...

4p hongphan_1 15-04-2013 37 1   Download

Hand-coded scripts were used in the 1970-80s as knowledge backbones that enabled inference and other NLP tasks requiring deep semantic knowledge. We propose unsupervised induction of similar schemata called narrative event chains from raw newswire text. A narrative event chain is a partially ordered set of events related by a common protagonist. We describe a three step process to learning narrative event chains. The first uses unsupervised distributional methods to learn narrative relations between events sharing coreferring arguments.

9p hongphan_1 15-04-2013 53 1   Download

This paper presents a new unsupervised algorithm (WordEnds) for inferring word boundaries from transcribed adult conversations. Phone ngrams before and after observed pauses are used to bootstrap a simple discriminative model of boundary marking. This fast algorithm delivers high performance even on morphologically complex words in English and Arabic, and promising results on accurate phonetic transcriptions with extensive pronunciation variation.

9p hongphan_1 15-04-2013 38 2   Download

In this paper, we propose a new Bayesian model for fully unsupervised word segmentation and an efficient blocked Gibbs sampler combined with dynamic programming for inference. Our model is a nested hierarchical Pitman-Yor language model, where Pitman-Yor spelling model is embedded in the word model. We confirmed that it significantly outperforms previous reported results in both phonetic transcripts and standard datasets for Chinese and Japanese word segmentation.

9p hongphan_1 14-04-2013 44 2   Download

We present a preliminary study on unsupervised preposition sense disambiguation (PSD), comparing different models and training techniques (EM, MAP-EM with L0 norm, Bayesian inference using Gibbs sampling). To our knowledge, this is the first attempt at unsupervised preposition sense disambiguation.

6p hongdo_1 12-04-2013 45 4   Download

Learning by Reading (LbR) aims at enabling machines to acquire knowledge from and reason about textual input. This requires knowledge about the domain structure (such as entities, classes, and actions) in order to do inference. We present a method to infer this implicit knowledge from unlabeled text. Unlike previous approaches, we use automatically extracted classes with a probability distribution over entities to allow for context-sensitive labeling.

10p hongdo_1 12-04-2013 50 2   Download

This paper presents an unsupervised method for deriving inference axioms by composing semantic relations. The method is independent of any particular relation inventory. It relies on describing semantic relations using primitives and manipulating these primitives according to an algebra. The method was tested using a set of eight semantic relations yielding 78 inference axioms which were evaluated over PropBank.

10p hongdo_1 12-04-2013 44 2   Download

We initiate a study comparing effectiveness of the transformed spaces learned by recently proposed supervised, and semisupervised metric learning algorithms to those generated by previously proposed unsupervised dimensionality reduction methods (e.g., PCA). Through a variety of experiments on different realworld datasets, we find IDML-IT, a semisupervised metric learning algorithm to be the most effective.

5p hongdo_1 12-04-2013 68 3   Download

Researchers in textual entailment have begun to consider inferences involving downward-entailing operators, an interesting and important class of lexical items that change the way inferences are made. Recent work proposed a method for learning English downward-entailing operators that requires access to a high-quality collection of negative polarity items (NPIs).

6p hongdo_1 12-04-2013 42 2   Download