conrad: Track conferences and meetups on your terminal!

Topical keyphrase extraction is used to summarize large collections of text
documents. However, traditional methods cannot properly reflect the intrinsic
semantics and relationships of keyphrases because they rely on a simple
term-frequency-based process. Consequently, these methods are not effective in
obtaining significant contextual knowledge. To resolve this, we propose a
topical keyphrase extraction method based on a hierarchical semantic network
and multiple centrality network measures that together reflect the hierarchical
semantics of keyphrases. We conduct experiments on real data to examine the
practicality of the proposed method and to compare its performance with that of
existing topical keyphrase extraction methods. The results confirm that the
proposed method outperforms state-of-the-art topical keyphrase extraction
methods in terms of the representativeness of the selected keyphrases for each topic. The proposed method can effectively reflect intrinsic keyphrase semantics and interrelationships.

Keyword extraction (also known as keyword detection or keyword analysis) is a text analysis technique that consists of automatically extracting the most important words and expressions in a text.

It helps summarize the content of a text and recognize the main topics which are being discussed.

The Pulitzer prizewinner shares his advice for pleasing readers, editors and yourself.

At the heart of decentralized systems today is a demoralizing irony. Vast resources---intellect, equipment, and energy---go into avoiding centralized control and creating "trustless" systems like Bitcoin. But hapless users then defeat the whole purpose of these systems by handing over their private keys to centralized entities like Coinbase.

Would it be nice if there were a truly decentralized system that could do the impossible? I.e.,

• Make key management easier for ordinary users.
• Manage secret keys for transparent objects without secret state, like smart contracts.
• Operate seamlessly when nodes come and go.

N-grams have been a common tool for information retrieval and machine learning applications for decades. In nearly all previous works, only a few values of $n$ are tested, with $n > 6$ being exceedingly rare. Larger values of $n$ are not tested due to computational burden or the fear of overfitting.

In this work, we present a method to find the top-$k$ most frequent $n$-grams that is 60$\times$ faster for small $n$, and can tackle large $n\geq1024$. Despite the unprecedented size of $n$ considered, we show how these features still have predictive ability for malware classification tasks. More important, large $n$-grams provide benefits in producing features that are interpretable by malware analysis, and can be used to create general purpose signatures compatible with industry standard tools like Yara. Furthermore, the counts of common $n$-grams in a file may be added as features to publicly available human-engineered features that rival efficacy of professionally-developed features when used to train gradient-boosted decision tree models on the EMBER dataset.

Supplementary Materials for the paper Tshitoyan et al. "Unsupervised word embeddings capture latent knowledge from materials science literature", Nature (2019).

The outputs from scientific research are many and varied, including: research articles reporting new knowledge, data, reagents, and software; intellectual property; and highly trained young scientists. Funding agencies, institutions that employ scientists, and scientists themselves, all have a desire, and need, to assess the quality and impact of scientific outputs. It is thus imperative that scientific output is measured accurately and evaluated wisely.

Natural language processing algorithms applied to three million materials science abstracts uncover relationships between words, material compositions and properties, and predict potential new thermoelectric materials.

The overwhelming majority of scientific knowledge is published as text, which is difficult to analyse by either traditional statistical analysis or modern machine learning methods. By contrast, the main source of machine-interpretable data for the materials research community has come from structured property databases, which encompass only a small fraction of the knowledge present in the research literature. Beyond property values, publications contain valuable knowledge regarding the connections and relationships between data items as interpreted by the authors. To improve the identification and use of this knowledge, several studies have focused on the retrieval of information from scientific literature using supervised natural language processing, which requires large hand-labelled datasets for training. Here we show that materials science knowledge present in the published literature can be efficiently encoded as information-dense word embeddings (vector representations of words) without human labelling or supervision. Without any explicit insertion of chemical knowledge, these embeddings capture complex materials science concepts such as the underlying structure of the periodic table and structure–property relationships in materials. Furthermore, we demonstrate that an unsupervised method can recommend materials for functional applications several years before their discovery. This suggests that latent knowledge regarding future discoveries is to a large extent embedded in past publications. Our findings highlight the possibility of extracting knowledge and relationships from the massive body of scientific literature in a collective manner, and point towards a generalized approach to the mining of scientific literature.