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Unsupervised segmentation
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Machine Reading Comprehension (MRC) is a great NLP task that requires concentration on making the machine read, scan documents, and extract meaning from the text, just like a human reader one of the MRC system challenges is not only having to understand the context to extract the answer but also being aware of the trust-worthy of the given question is possible or not.
9p
viberkshire
09-08-2023
5
3
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Segmenting electron microscopy (EM) images of cellular and subcellular processes in the nervous system is a key step in many bioimaging pipelines involving classification and labeling of ultrastructures. However, fully automated techniques to segment images are often susceptible to noise and heterogeneity in EM images (e.g. different histological preparations, different organisms, different brain regions, etc.).
9p
viwyoming2711
16-12-2020
12
0
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Unsupervised segmentation of multi-spectral images plays an important role in annotating infrared microscopic images and is an essential step in label-free spectral histopathology. In this context, diverse clustering approaches have been utilized and evaluated in order to achieve segmentations of Fourier Transform Infrared (FT-IR) microscopic images that agree with histopathological characterization.
11p
viwyoming2711
16-12-2020
8
0
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Quality control is a necessary step of any Next Generation Sequencing analysis. Although customary, this step still requires manual interventions to empirically choose tuning parameters according to various quality statistics.
8p
vikentucky2711
24-11-2020
13
2
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Microarray analysis represents a powerful way to test scientific hypotheses on the functionality of cells. The measurements consider the whole genome, and the large number of generated data requires sophisticated analysis.
12p
vioklahoma2711
19-11-2020
16
3
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Image segmentation and quantification are essential steps in quantitative cellular analysis. In this work, we present a fast, customizable, and unsupervised cell segmentation method that is based solely on Fiji (is just ImageJ)®, one of the most commonly used open-source software packages for microscopy analysis.
9p
vicoachella2711
27-10-2020
7
0
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Cluster analysis is an unsupervised technique of grouping related objects without considering their label or class. The objects belonging to the same cluster are relatively more homogeneous in comparison with other clusters. The application of cluster analysis is in areas like gene expression analysis, galaxy formation, natural language processing and image segmentation etc.
12p
dieutringuyen
07-06-2017
34
2
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We approximate Arabic’s rich morphology by a model that a word consists of a sequence of morphemes in the pattern prefix*-stem-suffix* (* denotes zero or more occurrences of a morpheme). Our method is seeded by a small manually segmented Arabic corpus and uses it to bootstrap an unsupervised algorithm to build the Arabic word segmenter from a large unsegmented Arabic corpus. The algorithm uses a trigram language model to determine the most probable morpheme sequence for a given input.
8p
bunbo_1
17-04-2013
46
1
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We present a language-independent and unsupervised algorithm for the segmentation of words into morphs. The algorithm is based on a new generative probabilistic model, which makes use of relevant prior information on the length and frequency distributions of morphs in a language. Our algorithm is shown to outperform two competing algorithms, when evaluated on data from a language with agglutinative morphology (Finnish), and to perform well also on English data.
8p
bunbo_1
17-04-2013
45
1
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The applicability of many current information extraction techniques is severely limited by the need for supervised training data. We demonstrate that for certain field structured extraction tasks, such as classified advertisements and bibliographic citations, small amounts of prior knowledge can be used to learn effective models in a primarily unsupervised fashion. Although hidden Markov models (HMMs) provide a suitable generative model for field structured text, general unsupervised HMM learning fails to learn useful structure in either of our domains.
8p
bunbo_1
17-04-2013
48
4
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We describe a novel approach to statistical machine translation that combines syntactic information in the source language with recent advances in phrasal translation. This method requires a source-language dependency parser, target language word segmentation and an unsupervised word alignment component. We align a parallel corpus, project the source dependency parse onto the target sentence, extract dependency treelet translation pairs, and train a tree-based ordering model.
9p
bunbo_1
17-04-2013
40
2
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Morphological segmentation has been shown to be beneficial to a range of NLP tasks such as machine translation, speech recognition, speech synthesis and information retrieval. Recently, a number of approaches to unsupervised morphological segmentation have been proposed. This paper describes an algorithm that draws from previous approaches and combines them into a simple model for morphological segmentation that outperforms other approaches on English and German, and also yields good results on agglutinative languages such as Finnish and Turkish. ...
8p
hongvang_1
16-04-2013
34
2
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We address the task of unsupervised topic segmentation of speech data operating over raw acoustic information. In contrast to existing algorithms for topic segmentation of speech, our approach does not require input transcripts. Our method predicts topic changes by analyzing the distribution of reoccurring acoustic patterns in the speech signal corresponding to a single speaker. The algorithm robustly handles noise inherent in acoustic matching by intelligently aggregating information about the similarity profile from multiple local comparisons. ...
8p
hongvang_1
16-04-2013
48
1
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Topic segmentation and identification are often tackled as separate problems whereas they are both part of topic analysis. In this article, we study how topic identification can help to improve a topic segmenter based on word reiteration. We first present an unsupervised method for discovering the topics of a text. Then, we detail how these topics are used by segmentation for finding topical similarities between text segments. Finally, we show through the results of an evaluation done both for French and English the interest of the method we propose. ...
8p
hongvang_1
16-04-2013
52
2
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Figure 1: Intuitive illustration of a variety of successive tokens and a word boundary mentation by formalizing the uncertainty of successive tokens via the branching entropy (which we mathematically de ne in the next section). Our intention in this paper is above all to study the fundamental and scienti c statistical property underlying language data, so that it can be applied to language engineering. The above assumption (A) dates back to the fundamental work done by Harris (Harris, 1955), where he says that when the number of di erent tokens coming after every pre x of a word marks...
8p
hongvang_1
16-04-2013
40
1
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Developing better methods for segmenting continuous text into words is important for improving the processing of Asian languages, and may shed light on how humans learn to segment speech. We propose two new Bayesian word segmentation methods that assume unigram and bigram models of word dependencies respectively. The bigram model greatly outperforms the unigram model (and previous probabilistic models), demonstrating the importance of such dependencies for word segmentation. We also show that previous probabilistic models rely crucially on suboptimal search procedures. ...
8p
hongvang_1
16-04-2013
55
2
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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
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We consider the task of unsupervised lecture segmentation. We formalize segmentation as a graph-partitioning task that optimizes the normalized cut criterion. Our approach moves beyond localized comparisons and takes into account longrange cohesion dependencies. Our results demonstrate that global analysis improves the segmentation accuracy and is robust in the presence of speech recognition errors.
8p
hongvang_1
16-04-2013
51
1
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Query segmentation is essential to query processing. It aims to tokenize query words into several semantic segments and help the search engine to improve the precision of retrieval. In this paper, we present a novel unsupervised learning approach to query segmentation based on principal eigenspace similarity of queryword-frequency matrix derived from web statistics.
4p
hongphan_1
15-04-2013
43
3
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For centuries, the deep connection between languages has brought about major discoveries about human communication. In this paper we investigate how this powerful source of information can be exploited for unsupervised language learning. In particular, we study the task of morphological segmentation of multiple languages. We present a nonparametric Bayesian model that jointly induces morpheme segmentations of each language under consideration and at the same time identifies cross-lingual morpheme patterns, or abstract morphemes. ...
9p
hongphan_1
15-04-2013
49
1
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