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A Generative Model of Vector Space Semantics
... ative model for vector space representa- tions of ...This model reformulates earlier tensor-based approaches to vector space semantics as a top-down process, and provides ... See full document
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“Not not bad” is not “bad”: A distributional account of negation
... the semantics of statements such as ‘A NOT B’ rather than merely ‘NOT B’, and does so by projecting the vector for A into the orthogonal subspace of the vector for ...for vector-based ... See full document
9
TransG : A Generative Model for Knowledge Graph Embedding
... relation semantics that a relation may have multiple meanings revealed by the entity pairs associated with the corresponding ...latent semantics: composition-related as (Table, HasPart, Leg) and ... See full document
10
What Do We Learn from Word Associations? Evaluating Machine Learning Algorithms for the Extraction of Contextual Word Meaning in Natural Language Processing
... Keywords: Machine Learning; Algorithms; Natural Language Processing, Deep Learning, Vector 29.. Space Models, Semantic Similarity, Distributional Semantics, Latent Semantic Analys[r] ... See full document
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Concept Extensions as the Basis for Vector Space Semantics: Combining Distributional and Ontological Information about Entities
... Distributional semantics is a potential solution to this problem. The long-touted advantages of distributional approaches are that they can be eas- ily trained from a large corpus, and they enable a graded notion ... See full document
10
Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality
... word vector space models, they are severely limited since they do not capture compositionality, the important quality of natural language that allows speakers to determine the meaning of a longer expression ... See full document
10
A Vector Model for Type Theoretical Semantics
... dows, 2008), linear maps (Baroni et al., 2014; Co- ecke et al., 2010), tensor decomposition (Van de Cruys et al., 2013), co-composition based on vec- tor space projections (Tsubaki et al., 2013), simul- taneous ... See full document
9
A Systematic Study of Semantic Vector Space Model Parameters
... (Landauer and Dumais, 1997). There is a risk that semantic similarity studies have been overfit- ting to their idiosyncracies, so in this study we evaluate on a variety of datasets: in addition to WordSim353 (W353) and ... See full document
10
Unsupervised Metaphor Paraphrasing using a Vector Space Model
... Metaphor has traditionally been viewed as an artistic device that lends vividness and distinction to its author’s style. This view was first challenged by Lakoff and Johnson (1980), who claimed that it is a productive ... See full document
10
Towards a Vector Space Model for FrameNet-like Resources
... to model FrameNet using minimal supervision, based on semantic ...distributional semantics is at a large extent valuable to model the notion of ...the model to more sophisticated and ... See full document
7
The Role of Syntax in Vector Space Models of Compositional Semantics
... of vector space representations of sentential semantics and the transparent interface between syntax and semantics provided by Combinatory Categorial Grammar to introduce Com- binatory ... See full document
11
A Vector Space for Distributional Semantics for Entailment
... the model of Vilnis and McCallum (2015), we also evalu- ated the unsupervised models on the hyponymy data from (Baroni et ...best model achieved 81% average precision on this dataset, non-significantly ... See full document
11
Vector space semantics with frequency driven motifs
... (2010) model that differentially mod- els content and function words for semantic com- position, and Goyal et ...SDSM model (2013) that incorporates syntactic roles to model seman- tic ...that ... See full document
10
A Structured Vector Space Model for Word Meaning in Context
... “catch”. Vector space models generally predict paraphrase ap- propriateness based on the similarity between ...feature space for efficient similarity computation. Consequently, vector ... See full document
10
Linguistic Model Based on the Generative Topological Information Space
... LINGUISTIC MODEL BASED ON THE GENERATIVE TOPOLOGICAL INFORMATION SPACE LINGUISTIC MODEL BASED ON THE GENERATIVE TOPOLOGICAL INFORMATION SPACE S e i i c h i Uchinami and Yoshikazu Tezuka F a c u l t y[.] ... See full document
8
Vector Space Model for Adaptation in Statistical Machine Translation
... The vector space used by VSM adaptation can be defined in various ways. In the experiments described below, we chose a definition that mea- sures the contribution (to counts of a given phrase pair, or to ... See full document
9
Improving Web Page Classification by Vector Space Model
... Among them, classification is a kind of data analysis form, which can be used to gather and describe important data setIn Web text mining, the text extraction and the characteristi[r] ... See full document
6
Monotonic Vector Space Model (Ⅰ): Concepts and Operations
... Z axis of three dimensional coordinate system (see Figure 1). Electric charge quantity of the point charge is also assumed to decrease with time increasing. So, it can be easily understood that there exists monotonic ... See full document
12
A Generative Model of Phonotactics
... probabilistic model of phono- tactics, the set of well-formed phoneme se- quences in a ...fully generative approach, model- ing a process where forms are built up out of subparts by ... See full document
14
Modeling Word Meaning: Distributional Semantics and the Corpus Quality Quantity Trade Off
... In this paper, we build simple SVD-reduced word-collocate models using four English corpora that differ considerably in quality, size and composition. We employ simple word co-occurrence based models rather than the more ... See full document
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