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2,772
A unified architecture for natural language processing: Deep neural networks with multitask learning
, 2008
"... We describe a single convolutional neural network architecture that, given a sentence, outputs a host of language processing predictions: part-of-speech tags, chunks, named entity tags, semantic roles, semantically similar words and the likelihood that the sentence makes sense (grammatically and sem ..."
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Cited by 340 (13 self)
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We describe a single convolutional neural network architecture that, given a sentence, outputs a host of language processing predictions: part-of-speech tags, chunks, named entity tags, semantic roles, semantically similar words and the likelihood that the sentence makes sense (grammatically
Data Clustering: 50 Years Beyond K-Means
, 2008
"... Organizing data into sensible groupings is one of the most fundamental modes of understanding and learning. As an example, a common scheme of scientific classification puts organisms into taxonomic ranks: domain, kingdom, phylum, class, etc.). Cluster analysis is the formal study of algorithms and m ..."
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Cited by 294 (7 self)
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and methods for grouping, or clustering, objects according to measured or perceived intrinsic characteristics or similarity. Cluster analysis does not use category labels that tag objects with prior identifiers, i.e., class labels. The absence of category information distinguishes data clustering
Tag Similarity in Folksonomies
"... ABSTRACT. Folksonomies- collections of user-contributed tags, proved to be efficient in reducing the inherent semantic gap. However, user tags are noisy; thus, they need to be processed before they can be used by further applications. In this paper, we propose an approach for bootstrapping semantics ..."
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semantics from folksonomy tags. Our goal is to automatically identify semantically related tags. The approach is based on creating probability distribution for each tag based on co-occurrence statistics. Subsequently, the similarity between two tags is determined by the distance between their corresponding
Tags
"... educational change, disruptive movements Though the history of massive open online courses is very short, scholars can gain insights by looking at similar movements in the past. This paper examines several historical moments in education to develop an understanding of MOOCs and their future. Specifi ..."
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educational change, disruptive movements Though the history of massive open online courses is very short, scholars can gain insights by looking at similar movements in the past. This paper examines several historical moments in education to develop an understanding of MOOCs and their future
From treebank to propbank
- In Language Resources and Evaluation
, 2002
"... This paper describes our approach to the development of a Proposition Bank, which involves the addition of semantic information to the Penn English Treebank. Our primary goal is the labeling of syntactic nodes with specific argument labels that preserve the similarity of roles such as the window in ..."
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Cited by 265 (14 self)
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of argument structure across both polysemous and synonymous verbs are also discussed and we present our actual guidelines for these types of phenomena, along with numerous examples of tagged sentences and verb frames. Metaframes are introduced as a technique for handling similar frames among near− synonymous
Evaluating Similarity Measures for Emergent Semantics of Social Tagging
"... Social bookmarking systems and their emergent information structures, known as folksonomies, are increasingly important data sources for Semantic Web applications. A key question for harvesting semantics from these systems is how to extend and adapt traditional notions of similarity to folksonomies, ..."
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Cited by 71 (8 self)
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. Our framework deals generally and symmetrically with users, tags, and resources. For evaluation purposes we focus on similarity among tags and resources, considering different ways to aggregate annotations across users. After comparing how tag similarity measures predict user-created tag relations, we
MBT: A Memory-Based Part of Speech Tagger-Generator
- PROC. OF FOURTH WORKSHOP ON VERY LARGE CORPORA
, 1996
"... We introduce a memory-based approach to part of speech tagging. Memory-based learning is a form of supervised learning based on similarity-based reasoning. The part of speech tag of a word in a particular context is extrapolated from the most similar cases held in memory. Supervised learning approac ..."
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Cited by 236 (56 self)
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We introduce a memory-based approach to part of speech tagging. Memory-based learning is a form of supervised learning based on similarity-based reasoning. The part of speech tag of a word in a particular context is extrapolated from the most similar cases held in memory. Supervised learning
Tags4Tags: Using Tagging to Consolidate Tags
"... Abstract. Tagging has become increasingly popular and useful across various social networks and applications. It allows users to classify and organize resources for improving the retrieval performance over those tagged resources. Within social networks, tags can also facilitate the interaction betwe ..."
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Cited by 4 (2 self)
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between members of the community, e.g. because similar tags may represent similar interests. Although obviously useful for straightforward retrieval tasks, the current meta-data model underlying typical tagging systems does not fully exploit the potential of the social process of finding, establishing
Detecting similar applications with collaborative tagging
- In ICSM
, 2012
"... Abstract—Detecting similar applications are useful for var-ious purposes ranging from program comprehension, rapid prototyping, plagiarism detection, and many more. McMillan et al. have proposed a solution to detect similar applications based on common Java API usage patterns. Recently, collaborativ ..."
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Cited by 9 (2 self)
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performed a user study involving several participants and the results show that collaborative tagging is a promising source of information useful for detecting similar software applications. I.
A Maximum Entropy Approach to Identifying Sentence Boundaries
- In Proceedings of the Fifth Conference on Applied Natural Language Processing
, 1997
"... We present a trainable model for identifying sentence boundaries in raw text. Given a corpus annotated with sentence boundaries, our model learns to classify each occurrence of., ?, and ! as either a valid or invalid sentence boundary. The training procedure requires no hand-crafted rules, lex ..."
Abstract
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Cited by 209 (3 self)
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, lexica, part-of-speech tags, or domain-specific information. The model can therefore be trained easily on any genre of English, and should be trainable on any other Romanalphabet language. Performance is comparable to or better than the performance of similar systems, but we emphasize
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