[PDF] Top 20 Inference with Distributional Semantic Models
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Inference with Distributional Semantic Models
... Detecting membership direction As described in Section 3.2 above, NBP also contains single-word h →c pairs (parrot→pet ). We extracted the subset of those that all judges considered to be in the category membership ... See full document
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An Artificial Language Evaluation of Distributional Semantic Models
... paradigmatic task with or without the w+c option (compare the solid lines). In fact, the performance in the paradigmatic task was slightly enhanced too. Putting this together with what we saw above regarding SGNS ... See full document
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Literal and Metaphorical Senses in Compositional Distributional Semantic Models
... our models on the metaphor classification tasks demonstrates that there is in- formation about metaphoricity of a phrase inher- ent in the composition of the meanings of its ... See full document
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General estimation and evaluation of compositional distributional semantic models
... We extracted distributional semantic vectors us- ing as source corpus the concatenation of ukWaC, Wikipedia (2009 dump) and BNC, 2.8 billion to- kens in total. 7 We use a bag-of-words approach and we count ... See full document
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A corpus based evaluation method for Distributional Semantic Models
... In Figure 3 (left), we show the results of the behavior-based Median Rank measure, obtained from the three corpora across a number of seman- tic dimensions. The best results are obtained with a few hundred dimensions. It ... See full document
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Distributional Semantic Concept Models for Entity Relation Discovery
... respective semantic similarity (SemSim) and their normalized inverse document frequency ...the semantic similarity measurement, we list the top co-occurring PMI*IDF measurements for Afghanistan and Paki- ... See full document
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Vectors or Graphs? On Differences of Representations for Distributional Semantic Models
... Rooted in Structural Linguistics (de Saussure, 1966; Harris, 1951), Distributional Semantic Models (DSMs, see e.g. (Baroni and Lenci, 2010)) characterize the meaning of lexical units by the contexts ... See full document
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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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BrainBench: A Brain Image Test Suite for Distributional Semantic Models
... Brain images are quite noisy, so we used the methodology from Mitchell et al. (2008) to select the most stable brain image features for each of the 18 participants. The stability metric assigns a high score to features ... See full document
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Lexical Variability and Compositionality: Investigating Idiomaticity with Distributional Semantic Models
... in Distributional Semantics Models (DSMs) (Baldwin et ...a distributional se- mantic ...its semantic non- compositionality and idiomatic ... See full document
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The Effects of Data Size and Frequency Range on Distributional Semantic Models
... on distributional seman- tic ...representative models for several test settings over data of varying sizes, and over test items of various ...network-based models underperform when the data is small, ... See full document
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A critique of word similarity as a method for evaluating distributional semantic models
... The notion of similarity is challenging to define precisely. Existing word similarity data sets typ- ically contain a broad range of semantic rela- tions such as synonymy, antonymy, hypernymy, co-hypernymy, ... See full document
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A SICK cure for the evaluation of compositional distributional semantic models
... The semantic annotation efforts mentioned so far have been done by experts. Interestingly, crowdsourcing services have proved to be useful for textual entailment annotation. Snow et al. (2008) show high agreement ... See full document
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Squibs: When the Whole Is Not Greater Than the Combination of Its Parts: A “Decompositional” Look at Compositional Distributional Semantics
... compositional distributional semantic models (CDSMs) estimate degrees of seman- tic similarity (or, more generally, relatedness) between two phrases: A good CDSM might tell us that green bird is ... See full document
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Redefining part of speech classes with distributional semantic models
... ways depends on the human annotators them- selves, but also on the quality of linguistic con- ventions behind the division into different word classes. That is why there have been several at- tempts to refine the ... See full document
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Towards Syntax aware Compositional Distributional Semantic Models
... Compositional distributional semantics models (CDSMs) are functions mapping text fragments to vectors (or higher-order ...map distributional vectors of words to distributional vectors for the ... See full document
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EVALution 1 0: an Evolving Semantic Dataset for Training and Evaluation of Distributional Semantic Models
... Given its large size, in terms both of lexical items and coded relations, WordNet is potentially extremely relevant to evaluate DSMs. However, since it has been built by lexicographers without checking against human ... See full document
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Evaluation of distributional semantic models: a holistic approach
... If we compare the maximum MAP obtained on each of the datasets (by either BOW or W2V), we see that DSMs capture syntactic derivatives even more accurately than near-synonyms if the models are tuned for this ... See full document
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Crossmodal Network Based Distributional Semantic Models
... Distributional semantic models (DSMs) (Baroni and Lenci, 2010) constitute a widely-used paradigm for ex- tracting, representing and learning semantics from lin- guistic ...the distributional ... See full document
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Low Dimensional Manifold Distributional Semantic Models
... lexical semantic space proper is high-dimensional, it is organized in such a way that interesting semantic relations can be exported from manifolds of much lower dimensionality embedded in this high ... See full document
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