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English is shown to be trans-context-free on the basis of coordinations of the respectively type that involve strictly syntactic cross-serial agreement . | {
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The agreement in question involves number in nouns and reflexive pronouns and is syntactic rather than semantic in nature because grammatical number in English , like grammatical gender in languages such as French , is partly arbitrary . | {
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The formal proof , which makes crucial use of the Interchange Lemma of Ogden et al. , is so constructed as to be valid even if English is presumed to contain grammatical sentences in which respectively operates across a pair of coordinate phrases one of whose members has fewer conjuncts than the other ; it thus goes th... | {
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In this paper , a novel method to learn the intrinsic object structure for robust visual tracking is proposed . | {
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The basic assumption is that the parameterized object state lies on a low dimensional manifold and can be learned from training data . | {
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Based on this assumption , firstly we derived the dimensionality reduction and density estimation algorithm for unsupervised learning of object intrinsic representation , the obtained non-rigid part of object state reduces even to 2 dimensions . | {
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Secondly the dynamical model is derived and trained based on this intrinsic representation . | {
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Thirdly the learned intrinsic object structure is integrated into a particle-filter style tracker . | {
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We will show that this intrinsic object representation has some interesting properties and based on which the newly derived dynamical model makes particle-filter style tracker more robust and reliable . | {
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Experiments show that the learned tracker performs much better than existing trackers on the tracking of complex non-rigid motions such as fish twisting with self-occlusion and large inter-frame lip motion . | {
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The proposed method also has the potential to solve other type of tracking problems . | {
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In this paper , we present a digital signal processor -LRB- DSP -RRB- implementation of real-time statistical voice conversion -LRB- VC -RRB- for silent speech enhancement and electrolaryngeal speech enhancement . | {
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As a silent speech interface , we focus on non-audible murmur -LRB- NAM -RRB- , which can be used in situations where audible speech is not acceptable . | {
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Electrolaryngeal speech is one of the typical types of alaryngeal speech produced by an alternative speaking method for laryngectomees . | {
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However , the sound quality of NAM and electrolaryngeal speech suffers from lack of naturalness . | {
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VC has proven to be one of the promising approaches to address this problem , and it has been successfully implemented on devices with sufficient computational resources . | {
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An implementation on devices that are highly portable but have limited computational resources would greatly contribute to its practical use . | {
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In this paper we further implement real-time VC on a DSP . | {
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To implement the two speech enhancement systems based on real-time VC , one from NAM to a whispered voice and the other from electrolaryngeal speech to a natural voice , we propose several methods for reducing computational cost while preserving conversion accuracy . | {
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We conduct experimental evaluations and show that real-time VC is capable of running on a DSP with little degradation . | {
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We propose a method that automatically generates paraphrase sets from seed sentences to be used as reference sets in objective machine translation evaluation measures like BLEU and NIST . | {
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We measured the quality of the paraphrases produced in an experiment , i.e. , -LRB- i -RRB- their grammaticality : at least 99 % correct sentences ; -LRB- ii -RRB- their equivalence in meaning : at least 96 % correct paraphrases either by meaning equivalence or entailment ; and , -LRB- iii -RRB- the amount of internal ... | {
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The paraphrase sets produced by this method thus seem adequate as reference sets to be used for MT evaluation . | {
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Graph unification remains the most expensive part of unification-based grammar parsing . | {
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We focus on one speed-up element in the design of unification algorithms : avoidance of copying of unmodified subgraphs . | {
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We propose a method of attaining such a design through a method of structure-sharing which avoids log -LRB- d -RRB- overheads often associated with structure-sharing of graphs without any use of costly dependency pointers . | {
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The proposed scheme eliminates redundant copying while maintaining the quasi-destructive scheme 's ability to avoid over copying and early copying combined with its ability to handle cyclic structures without algorithmic additions . | {
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We describe a novel technique and implemented system for constructing a subcategorization dictionary from textual corpora . | {
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Each dictionary entry encodes the relative frequency of occurrence of a comprehensive set of subcategorization classes for English . | {
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An initial experiment , on a sample of 14 verbs which exhibit multiple complementation patterns , demonstrates that the technique achieves accuracy comparable to previous approaches , which are all limited to a highly restricted set of subcategorization classes . | {
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We also demonstrate that a subcategorization dictionary built with the system improves the accuracy of a parser by an appreciable amount | {
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The introduction of prior knowledge has greatly enhanced numerous purely low-level driven image processing algorithms . | {
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In this work , we focus on the problem of non-rigid image registration . | {
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A number of powerful registration criteria have been developed in the last decade , most prominently the criterion of maximum mutual information . | {
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Although this criterion provides for good registration results in many applications , it remains a purely low-level criterion . | {
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As a consequence , registration results will deteriorate once this low-level information is corrupted , due to noise , partial occlusions or missing image structure . | {
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In this paper , we will develop a Bayesian framework that allows to impose statistically learned prior knowledge about the joint intensity distribution into image registration methods . | {
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The prior is given by a kernel density estimate on the space of joint intensity distributions computed from a representative set of pre-registered image pairs . | {
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This nonparametric prior accurately models previously learned intensity relations between various image modalities and slice locations . | {
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Experimental results demonstrate that the resulting registration process is more robust to missing low-level information as it favors intensity correspondences statistically consistent with the learned intensity distributions . | {
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There is an increased interest in the efficient creation of city models , be it virtual or as-built . | {
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We present a method for synthesizing complex , photo-realistic facade images , from a single example . | {
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After parsing the example image into its semantic components , a tiling for it is generated . | {
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Novel tilings can then be created , yielding facade textures with different dimensions or with occluded parts inpainted . | {
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A genetic algorithm guides the novel facades as well as inpainted parts to be consistent with the example , both in terms of their overall structure and their detailed textures . | {
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Promising results for multiple standard datasets -- in particular for the different building styles they contain -- demonstrate the potential of the method . | {
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We introduce a new interactive corpus exploration tool called InfoMagnets . | {
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InfoMagnets aims at making exploratory corpus analysis accessible to researchers who are not experts in text mining . | {
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As evidence of its usefulness and usability , it has been used successfully in a research context to uncover relationships between language and behavioral patterns in two distinct domains : tutorial dialogue -LRB- Kumar et al. , submitted -RRB- and on-line communities -LRB- Arguello et al. , 2006 -RRB- . | {
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As an educational tool , it has been used as part of a unit on protocol analysis in an Educational Research Methods course . | {
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Sources of training data suitable for language modeling of conversational speech are limited . | {
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In this paper , we show how training data can be supplemented with text from the web filtered to match the style and/or topic of the target recognition task , but also that it is possible to get bigger performance gains from the data by using class-dependent interpolation of N-grams . | {
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We present a method for detecting 3D objects using multi-modalities . | {
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While it is generic , we demonstrate it on the combination of an image and a dense depth map which give complementary object information . | {
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It works in real-time , under heavy clutter , does not require a time consuming training stage , and can handle untextured objects . | {
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It is based on an efficient representation of templates that capture the different modalities , and we show in many experiments on commodity hardware that our approach significantly outperforms state-of-the-art methods on single modalities . | {
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The compact description of a video sequence through a single image map and a dominant motion has applications in several domains , including video browsing and retrieval , compression , mosaicing , and visual summarization . | {
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Building such a representation requires the capability to register all the frames with respect to the dominant object in the scene , a task which has been , in the past , addressed through temporally localized motion estimates . | {
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In this paper , we show how the lack of temporal consistency associated with such estimates can undermine the validity of the dominant motion assumption , leading to oscillation between different scene interpretations and poor registration . | {
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"text": "lack of temporal consistency",
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To avoid this oscillation , we augment the motion model with a generic temporal constraint which increases the robustness against competing interpretations , leading to more meaningful content summarization . | {
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In cross-domain learning , there is a more challenging problem that the domain divergence involves more than one dominant factors , e.g. , different viewpoints , various resolutions and changing illuminations . | {
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Fortunately , an intermediate domain could often be found to build a bridge across them to facilitate the learning problem . | {
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In this paper , we propose a Coupled Marginalized Denoising Auto-encoders framework to address the cross-domain problem . | {
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"text": "Coupled Marginalized Denoising Auto-encoders framework",
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Specifically , we design two marginalized denoising auto-encoders , one for the target and the other for source as well as the intermediate one . | {
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To better couple the two denoising auto-encoders learning , we incorporate a feature mapping , which tends to transfer knowledge between the intermediate domain and the target one . | {
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Furthermore , the maximum margin criterion , e.g. , intra-class com-pactness and inter-class penalty , on the output layer is imposed to seek more discriminative features across different domains . | {
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End of preview. Expand in Data Studio
SciERC
SciERC (end-to-end relation extraction), rebuilt for MNEB from the original release with character-level offsets.
Source: http://nlp.cs.washington.edu/sciIE/data/sciERC_processed.tar.gz
| config | split | records |
|---|---|---|
| default | train | 1861 |
| default | validation | 275 |
| default | test | 551 |
Labels
entities: Task, Method, Metric, Material, OtherScientificTerm, Generic
relations: used for, feature of, hyponym of, part of, compare, conjunction, evaluate for
Format
Each record has input (the text), output (gold annotations grouped by label) and schema (the label set with descriptions). Offsets are character offsets into input, half-open [start, end).
"output": {
"entities": {"<label>": [{"text", "start", "end"}]},
"relations": {"<label>": [{"head": {"text", "start", "end"},
"tail": {"text", "start", "end"}}]},
"events": {"<label>": [{"trigger": {"text", "start", "end"},
"arguments": [{"role", "text", "start", "end"}]}]}
}
Each schema item has a description and a description_source: official when adapted from the dataset's paper or guideline, mneb when written for MNEB. Relation datasets also give setting (end_to_end or gold_entity) and negative_label; relation items give symmetric. Event items list the roles valid for that event type.
Within a record, identical annotations are kept once and outer whitespace is trimmed from spans; source annotations are otherwise kept as annotated.
Notes
- All sentences are kept, including those without relations.
compareandconjunctionare symmetric (schema.relations[*].symmetric).- Entity and relation descriptions are adapted from the annotation guideline.
Build
Built by scripts/datasets/sci_erc.py in the MNEB repository.
Citation
Luan et al. (2018), Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction. EMNLP.
License: not stated by the source; see the original release.
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