[PDF] Top 20 Global Textual Relation Embedding for Relational Understanding

... Only textual relations with the num- ber of tokens (including both lexical tokens and dependency relations) less than or equal to 10 are ...non-symmetric textual relations are kept, because symmetric ones ... See full document

... rate decreased linearly in each epoch from the initial rate to the minimum rate. We used the unchanged parameter min count (β) that represents the minimum frequency for times that a token must appear to be included in ... See full document

... language understanding at the level of both syntax and semantics (Nakashole et ...entirety. Relation strings are still bound to surface text, lacking actual semantic con- ...of relational phrases ... See full document

... of global orderings and belonging (Aas 2007, ...to relational concerns ...to relational concerns here is not a new phenomenon, though it is one that is now more easily ...that relational ... See full document

... that embedding each word into a low-dimensional con- tinuous vector could achieve better performance, be- cause the global context information for each word can be better leveraged in this ... See full document

... the relational inner product associ- ation ...the relation vector ~ ...the relational inner product is simply h~ w,~ ...for embedding models that implicitly do matrix factorization, it has an ... See full document

... another, relational dialogism undermines the assumption that religion is incompatible with modernization and ...politics. Relational dialogism also challenges assumptions about what ‘modernization’ and ... See full document

... In this analysis, we evaluate the performance of three off-the-shelf multimodal systems: decinit uses a learned transformation of a global 2048D visual feature vector is used to initialise the de- coder hidden ... See full document

... network- embedding problem, and present two novel strategies to improve over traditional atten- tion mechanisms: (i) a content-aware sparse attention module based on optimal transport, and (ii) a high-level ... See full document

... explored textual relation embedding under the supervised setting (Xu et ...embed textual relations with distant supervision (Mintz et ...(KB) relation is likely to ex- press the ... See full document

... We proposed a new method to learn content-aware node embeddings, which extends NODE 2 VEC by considering the textual descriptors of the nodes. The proposed approach leverages the strengths of both structure- and ... See full document

... Match durations. Following the fresh Chinese exporters over time, we observe which firms that started an exporting activity in the US market in year t still export the same product j to the US after three years (in t ... See full document

... The core algorithm is complemented by a set of heuristics designed to handle specific cases. For instance, a list of uninformative syntactic heads is provided so that if a head belongs to this list, then the algorithm ... See full document

... at last time step. For both algorithms, the train- ing data is randomly sampled to store in the mem- ory, following (Lopez-Paz and Ranzato, 2017). On lifelong relation detection, the EMR outperforms GEM on both of ... See full document

... the relational model or RDBMS, taking the middleware approach and focusing more on similarity searching on metric spaces and not on the possible outcomes of the integration with the concepts of tuples and ... See full document

... The first reversible watermarking scheme was proposed in [7]. This schema guarantees a clear and exact tampered- or-not authentication without causing any permanent distortion to the database. A lossless and exact ... See full document

... word embedding pre-trained on Google news corpus, which is widely used in NLP com- munity (Mikolov et ...the embedding corpus, it is initialized to random values very close to ...event embedding, we ... See full document

... Multi-relation embedding is a popular approach to knowl- edge base completion that learns embedding representations of entities and relations to compute the plausibility of miss- ing ...of ... See full document

... To address these issues, we aim to propose a Context-Aware Network Embedding (CANE) framework for modeling relationships between vertices precisely. More specifically, we present CANE on information networks, ... See full document

... The task clustering approach for hierarchical multitask learning is evalu- ated on the drug resistance dataset, which provides a deeper structure of.. tasks as information on both drug c[r] ... See full document