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Inferring the set name of semantically grouped entities is useful in many tasks related to natural language processing and information retrieval. Previous studies mainly draw names from knowledge bases to ensure high quality, but that limits the candidate scope. We propose an unsupervised framework, AutoName, that exploits large-scale text corpora to name a set of query entities. Specifically, it first extracts hypernym phrases as candidate names from query-related documents via probing a pre-trained language model. A hierarchical density-based clustering is then applied to form potential concepts for these candidate names. Finally, AutoName ranks candidates and picks the top one as the set name based on constituents of the phrase and the semantic similarity of their concepts. We also contribute a new benchmark dataset for this task, consisting of 130 entity sets with name labels. Experimental results show that AutoName generates coherent and meaningful set names and significantly outperforms all compared methods. Further analyses show that AutoName is able to offer explanations for extracted names using the sentences most relevant to the corresponding concept. 

Entity set expansion (ESE) refers to mining ``siblings'' of some user-provided seed entities from unstructured data. It has drawn increasing attention in the IR and NLP communities for its various applications. To the best of our knowledge, there has not been any work towards a supervised neural model for entity set expansion from unstructured data. We suspect that the main reason is the lack of massive annotated entity sets. In order to solve this problem, we propose and implement a toolkit called {DBpedia-Sets}, which automatically extracts entity sets from any plain text collection and can provide a large number of distant supervision data for neural model training. We propose a two-channel neural re-ranking model {NESE} that jointly learns exact and semantic matching of entity contexts. The former accepts entity-context co-occurrence information and the latter learns a non-linear transformer from generally pre-trained embeddings to ESE-task specific embeddings for entities. Experiments on real datasets of different scales from different domains show that {NESE} outperforms state-of-the-art approaches in terms of precision and MAP, where the improvements are statistically significant and are higher when the given corpus is larger. 

We have designed and synthesized a series of deep‐blue light‐emitting polyfluorenes, PF‐27SOs and PF‐36SOs, by introducing electron‐deficient 9,9‐dimethyl‐9H‐thioxanthene 10,10‐dioxide isomers (27SO and 36SO) into the poly(9,9‐dioctylfluorene) (PFO) backbone. Compared with PFO, the resulting polymers exhibit an equivalent thermal decomposition temperature (>415 °C), an enhanced glass transition temperature (>99 °C), a decreased lowest unoccupied molecular orbital energy level (E_LUMO) below −2.32 eV, a blue‐shifted photoluminescence spectra in solid film with a maximum emission at ~422 nm, and a shoulder peak at ~445 nm. The resulting polymers also show blue‐shifted and narrowed electroluminescence spectra with deep‐blue Commission Internationale de L'Eclairage (CIE) coordinates of (0.16, 0.07) for PF‐27SO20 and (0.16, 0.06) for PF‐36SO30, compared with (0.17, 0.13) for PFO. Moreover, simple device based on PF‐36SO30 achieves a superior device performance with a maximum external quantum efficiency (EQE_max= 3.62%) compared with PFO (EQE_max= 0.47%). The results show that nonconjugated 9,9‐dimethyl‐9H‐thioxanthene 10,10‐dioxide isomers can effectively perturb the conjugation length of polymers, significantly weaken the charge‐transfer effect in donor–acceptor systems, substantially improve electroluminescence device efficiency, and achieve deep‐blue light emission.