dataesr/scientific-paragraphs-categorization huggingface dataset:
Each word is represented by at most three instances in WiC, and repeated sentences are excluded. It is important to note that meanings represented in the WiC dataset are coarser-grained than WordNet senses. This was ensured by excluding WordNet synsets describing highly similar meanings (sister senses, and senses belonging to the same supersense). The human-level performance upper-bound on this binary task, as measured on two 100-sentence samples, is 80. 5%. Inter-annotator agreement is also high, at 79%. The dataset comes with an official train/dev/test split containing 7,618, 702 and 1,366 sentence pairs, respectively. 4.
Direct Usage Similarity Prediction In the unsupervised Usim prediction setting, we apply different types of pre-trained word and sentence embeddings as follows: we compute an embedding for every sentence in the Usim dataset, and calculate the pairwise cosine similarity between the sentences available for a target word. Then, for every embedding type, we measure the correlation between sentence similarities and gold usage similarity judgments in the Usim dataset, using Spearman's ρ correlation coefficient. We experiment with the following embedding types. GloVe embeddings are uncontextualized word representations which merge all senses of a word in one vector  (Pennington et al. , 2014). We use 300-dimensional GloVe embeddings pre-trained on Common Crawl (840B tokens). 5 The representation of a sentence is obtained by averaging the GloVe embeddings of the words in the sentence.
SIF (Smooth Inverse Frequency) embeddings are sentence representations built by applying dimensionality reduction to a weighted average of uncontextualized embeddings of words in a sentence  (Arora et al. , 2017). We use SIF in combination with GloVe vectors. Context2vec embeddings  (Melamud et al. , 2016). The context2vec model learns embeddings for words and their sentential contexts simultaneously. The resulting representations reflect: a) the similarity between potential fillers of a sentence with a blank slot, and b) the similarity of contexts that can be filled with the same word. We use a context2vec model pre-trained on the UkWac corpus  (Baroni et al. , 2009)   6  to compute embeddings for sentences with a blank at the target word's position. ELMo (Embeddings from Language Models) representations are contextualized word embeddings derived from the internal states of an LSTM bidirectional language model (biLM)  (Peters et al.
10 e test the above models with representations built from the whole sentence, and using a smaller context window (cw) around the target word. Sentences in the WiC dataset are quite short (7. 9 ± 3. 9 words), but the length of sentences in the Usim and CoInCo datasets varies a lot (27. 4 ± 13. 2 and 18. 8 ± 10. 2, respectively). We want to check whether information surrounding the target word in the sentence is more relevant, and sufficient for Usim estimation. We focus on the words in a context window of ± 2, 3, 4 or 5 words at each side of a target word. Then, we collect their word embeddings to be averaged (for GloVe, ELMo and BERT), or derive an embedding from this specific window instead of the whole sentence (for USE). We approximate Usim by measuring the cosine similarity of the resulting context representations.
Automatic LexSub We generate rankings of candidate substitutes for words in context using the context2vec method  (Melamud et al. , 2016). The original method selects and ranks substitutes from the whole vocabulary. To facilitate comparison and evaluation, we use the following pools of candidates: (a) all substitutes that were proposed for a word in the LexSub and CoInCo annotations (we call this substitute pool AUTO-LSCNC); (b) the paraphrases of the word in the Paraphrase Database (PPDB) XXL package  (Ganitkevitch et al. , 2013; Pavlick et al. , 2015)  (AUTO-PPDB). 11  In the WiC experiments, where no substitute annotations are available, we only use PPDB paraphrases (AUTO-PPDB). We obtain a context2vec embedding for a sentence by replacing the target word with a blank. AUTO-LSCNC substitutes are high-quality since they were extracted from the manual LexSub and CoInCo annotations.
Following this procedure, context2vec produces a ranking of candidate substitutes for each target word instance in the Usim, CoInCo and WiC datasets, according to their fit in context. Every candidate is assigned a score, with substitutes that are a good fit in a specific context being higher-ranked than others. For every new target word instance, context2vec ranks all candidate substitutes available for the target in each pool. Consequently, the automatic annotations produced for different instances of the target include the same set of substitutes, but in different order. This does not allow for the use of measures based on substitute overlap, which were shown to be useful for Usim prediction in  McCarthy et al. (2016). In order to use this type of measures, we propose ways to filter the automatically generated rankings, and keep for each instance only substitutes that are a good fit in context.
context2vec scores vary a lot across instances, it is thus not straightforward to choose a threshold. We instead refer to the scores assigned to adjacent pairs of substitutes in the ranking produced for each instance, R = [s 1 , s 2 ,. , s n ]. We view the pair with the biggest difference in scores as the cut-off point, considering it reflects a degradation in substitute fit. We retain only substitutes up to this point. • Highest-ranked X substitutes. We also test two simple baselines, which consist in keeping the 5 and 10 highest-ranked substitutes for each instance. We test the efficiency of each filter on the portion of the LexSub dataset  (McCarthy and Navigli, 2007)  that was not annotated for Usim. We compare the substitutes retained for each instance after filtering to its gold LexSub susbtitutes using the F1-score, and the proportion of false positives out of all positives.
Supervised Usim Prediction We train linear regression models to predict Usim scores for word instances in different contexts using as features the cosine similarity of the different representations in Section 4. 1, and the substitutebased features in 4. 2. For training, we use the Usim dataset on its own (cf. Section 3. 1), and combined with the additional training examples extracted from CoInCo (cf. Section 3. 2). To be able to evaluate the performance of our models separately for each of the 56 target words in the Usim dataset, we train a separate model for each word in a leave-one-out setting. Each time, we use 2,196 pairs for training, 225 for development and 45 for testing. 12 Each model is evaluated on the sentences corresponding to the left out target word. We report results of these experiments in Section 5.
The performance of the model with context2vec substitutes from the two substitute pools is compared to that of the model with gold substitute annotations. We replicate the experiments by adding CoInCo data to the Usim training data. To test the contribution of each feature, we perform an ablation study on the 225 Usim sentence pairs of the development set, which cover the full spectrum of Usim scores (from 1 to 5). We report results of the feature ablation in Appendix C. We also build a model for the binary Usim task on the WiC dataset (Pilehvar and Camacho-Collados, 2019), using the official train/dev/test split. We train a logistic regression classifier on the training set, and use the development set to select the best among several feature combinations. We report results of the best performing models on the WiC test set in Section 5.
Graded Usim To evaluate the performance of our supervised models, we measure the correlation of the predictions with human similarity judgments on the Usim dataset using Spearman's ρ. Results reported in Table  3  are the average of the correlations obtained for each target word with gold and automatic substitutes (from the two substitute pools), and for each type of features, substitutebased and embedding-based (cosine similarities from BERT and context2vec). We also report results with the additional CoInCo training data. Unsurprisingly, the best results are obtained by the methods that use the gold substitutes. This is consistent with previous analyses by  Erk et al. (2009)  who found overlap in manually-proposed substitutes to correlate with Usim judgments. The lower performance of features that rely on automatically selected substitutes (AUTO-LSCNC and AUTO-PPDB) demonstrates the impact of substitute quality on the contribution of this type of features.
Binary Usim We evaluate the predictions of our binary classifiers by measuring accuracy on the test portion of the WiC dataset. Results for the best configurations for each training set are reported in Table  4. Experiments on the development set showed that target word BERT representations and USE sentence embeddings are the best-suited for WiC. Therefore, 'embedding-based features' here refers to these two representations. Results on the development set can be found in Appendix D. All configurations obtain higher accuracy than the previous best reported result on this dataset (59. 4) (Pilehvar and Camacho-Collados, 2019), obtained using DeConf vectors, which are multi-prototype embeddings based on WordNet knowledge  (Pilehvar and Collier, 2016). Similar to the graded Usim experiments, adding substitute-based features to embedding features slightly improves the accuracy of the model.
A Filtering experiments Tables 5 and 6 contain results obtained using the different substitute filters described in Section 4. 2. We measure the quality of the substitutes retained in the automatic ranking produced by context2vec after filtering against gold substitute annotations in LexSub data. Here, we only use the portion of LexSub data that does not contain Usim judgments. We measure filtered substitute quality against the gold standard using the F1-score, and the proportion of false positives (FP) over all positives (TP+FP). Table  5  shows results for annotations assigned by context2vec using the the Lex-Sub/CoInCo pool of substitutes (AUTO-LSCNC).
Graded Usim results: Spearman's ρ correlation results between supervised model predictions and graded annotations on the Usim test set. The first column reports results obtained using gold substitute annotations for each target word instance. The last two columns give results with automatic substitutes selected among all substitutes proposed for the word in the LexSub and CoInCo datasets (AUTO-LSCNC), or paraphrases in the PPDB XXL package (AUTO-PPDB). The Embedding-based configuration uses cosine similarities from BERT and context2vec.
Accuracy of different features and combinations on the WiC development set. On this dataset, the two best types of embeddings, that were chosen for the Embedding-based and Combined configurations, were BERT (target word, average of the last 4 layers) and USE. Both Only-substitutes and Combined use features of automatic substitutes from the PPDB pool, and back off to the Embedding-based model when there were no paraphrases available for the target word in the PPDB.
090 mm in gauge). Surface tensions at the air-sample surface were determined for distilled water, three water+SDS mixtures (at surfactant concentrations of 0. 25, 0. 5 and 1. 25 wt. %), bulk-RT21HC and the four designed nano-emulsions using a 15-gauge needle (external diameter of 1. 835 mm). Surface tension measurements were carried out in the range between 275 and 303 K, both increasing and decreasing temperature with steps of 2-5 K. The contact angles of water and nano-emulsion droplets formed using a 15-gauge needle and deposited on a stainless steel substrate were analyzed at 278 and 298 K using Young-Laplace equation. This substrate was previously utilized in the framework of a round robin test  [36]  in which nine independent European research centers of the Cost Action NanoUptake program  [37]  took part.
A thoroughly characterization of substrate roughness is presented in  Hernaiz et al. [36]. Recommendations such as substrate cleaning proposed in  [36]  were followed in this study. For the three physical properties, values were determined within a few seconds after drops were formed (IFT and SFT) or deposited in the stainless substrate (CA). Air-water interfacial tension was determined at room temperature and the result showed a deviation lower than 2% with surface tension data reported for water in  [38]. Experimental uncertainty in measurements performed with this device was estimated to be 1% (SFT)  [39] , while a maximum relative deviation of 0. 52% was obtained in  [36]  for this DSA-30 device measuring sessile drop gauges of 30 • , 60 • and 120 • in CA.
Therefore, the contig size threshold for scaffolding was set to 466 bp, 10 bp greater than the mean (294) plus two times the standard deviation (81) of the PE300 fragment size as estimated by mapping. The scaffolded genome assembly is referred to as ABAL 1. 0. Moreover, an analysis of the spectra copy number (KAT;  Mapleson et al. 2017)  with default k = 27 was done to compare the PE300 library to the assembly. The KAT program is often used to compare the proportion of k-mers present in the reads that end up in the final assemblies. It shows how much the genome architecture agrees with the final assembly. Chloroplast genome assembly and annotation: All of the 100 bp reads from the MP1500 library (the library with the tightest size distribution and highest complexity) were mapped to the closest complete reference chloroplast sequence available in NCBI, i.
alba contigs. Finally, Gapfiller  (Boetzer and Pirovano 2012)  was used to close gaps in the chloroplast genome. The DNA diff module-from MUMMER 3. 22 package  (Kurtz et al. 2004 )-was run to compare the intermediate Spases assembly with the A. koreana (NC_026892. 1) and A. sibirica (NC_035067. 1) complete chloroplast sequences. Finally, the annotation of the chloroplast was carried out with DOGMA  (Wyman et al. 2004 ).
Gene completeness: The final nuclear assembly was evaluated for gene completeness using CEGMA v2. 5  (Parra et al. 2007) , which searches for 248 ultra-conserved core eukaryotic genes (CEGs), and BUSCO v3. 0. 2  (Simão et al. 2015) , using 956 single-copy orthologs from plants (BUSCO v1 plantae database). To obtain a more comprehensive estimate of genes present in the genome assembly, the STAR software package (Domin and Gingeras 2015) was used to map the genome assembly with the silver fir RNAseq produced by  Roschanski et al. (2013)  (GenBank accession numbers JV134525-JV157085) as well as 12 transcriptomes originating from Mont Ventoux (France) and the Black Forest (District Oberharmersbach, Germany), as reported in  Roschanski et al. (2013)  and available in the Dryad Digital Repository  (Roschanski et al.
Annotation Protein-coding gene annotation: Repeats were identified, annotated and masked in the silver fir genome assembly following three sequential steps. First, RepeatMasker v4. 0. 6 (http://www. repeatmasker. org) was run using the Pinaceae-specific repeat library included in the RepeatMasker release. Then, repeats annotated in P. taeda and P. menziesii were used in a second run of RepeatMasker. Finally, Abies alba-specific repeats were detected with RepeatModeler and masked with RepeatMasker. An annotation of the genes present in the assembly was further obtained by combining transcript alignments, protein alignments and ab initio gene predictions as follows. The RNAseq reads mentioned above (JV134525-JV157085 in  Roschanski et al. 2013; 2015; 2016)  were aligned to the genome using STAR v2.
5. 4a  (Dobin et al. 2013)  with default options, and then transcript models were generated from Stringtie  (Pertea et al. 2015)  also with default options. The resulting models were given to PASA v2. 2. 0  (Haas et al. 2008)   the Transdecoder predictions against the Swiss-Prot database (The UniProt Consortium 2018). Sequences with a complete Open Reading Frame (ORF), a BLAST hit against Swiss-Prot (E-value , 1e-9), and not hitting any repeat were considered as potential candidates to train gene predictors. Of this list, the 500 sequences whose length differed the least from the length of their BLAST target were selected as the best candidate genes and used to train the parameters for three gene predictors: GeneID v1. 4  (Parra et al. 2000) , Augustus v3. 2. 3  (Stanke et al. 2006)  and Glimmer  (Majoros et al.
To remove the potential presence of some bacterial genes in the genome annotation, a protein-based bacterial decontamination procedure was performed on the assembly and annotation. This process utilizes a BLASTp search of the annotated proteins against the bacterial non-redundant protein database from NCBI to detect genes likely to belong to bacteria. All the scaffolds containing more than 50% of bacterial genes and without conifer-specific repeats and RNAseq mappings were removed from the assembly, resulting in the final assembly ABAL 1. 1. Finally, to check for the presence of the chloroplast genome in the nuclear genome assembly, the chloroplast assembly was mapped to ABAL 1. 1 using Minimap2  (Li 2018)  with the parameter-asm10. Sixty-six unique mappings longer than 1 Kb were found in the assembly (the longest being 18.
49 Kb) but they did not meet the threshold of at least 70% matches. Therefore, these regions were considered as nuclear sequence homologous to chloroplast and were kept in the ABAL 1. 1 assembly. The proteins resulting from the structural annotation process described above were functionally annotated using the Blast2GO v4. 1 pipeline  (Conesa et al. 2005 ) with default parameters. The annotated proteins were first scanned for InterProScan patterns and profiles. Next, a BLASTp search against the NCBI RefSeq database (Uniprot and Swissprot databases) was performed, inheriting the functional annotations of the top-20 BLAST hits with an e-value , 1e-06. Finally, Blast2GO produced a consensus annotation. In addition, the software CateGOrize (Zhi-Liang et al. 2008) was run to assign all genes to the main Gene Ontology (GO) categories.
The software provides the count and percentage of the GO term assigned in each category. Two classification lists (slim2 and myclass2) were used in the analysis. The slim2 list is a subset of gene ontology terms (https://www. animalgenome. org/tools/catego/. goslim/GO_slim2). Myclass2 classification list is based on slim2 with 50 additional GO term categories (Table  S3 ). The percentages across the two classification lists were visualized using the geom_col function of the "ggplot" package in R CRAN. Comparison With other conifers: The summary statistics on the annotated genes were computed using a custom Python script (Supplementary Material 2). The same script was applied to calculate the length of exons, introns and genes in other conifer assemblies, such as P. abies v1. 0, P. glauca v3.
Data availability The silver fir genome assembly ABAL 1. 1 is available in the TreeGenes Database under https://treegenesdb. org/FTP/Genomes/Abal/. The following data are listed in the supplementary tables: the estimation of DNA concentration (Table  S1 ), the multi-locus microsatellite genotypes of the megagametophyte and needle tissue used for sequencing (Table  S2A ), the genotype of AA_WSL_01 for the SNP loci (Table  S2B ), the Gene ontology (GO) term categories used to count the GO terms in A. alba (Table  S3 ), the A. alba genome annotation statistics (Table  S4 ), the intron and exon statistics for A. alba and Pseudotsuga menziesii (Table  S5 ), and the count and percentage of the GO terms (Table  S6 ). The following supplementary figures are included in the supplementary file: Abies alba distribution map (Figure  S1 ), the location of the sampled tree AA_WSL01 along with the location of the other 19 Swiss A.
abies populations (Figure  S2 ), plot for the comparison between Abies chloroplast (Figure  S3 ), boxplots of the gene distribution lengths in A. alba (Figure  S4 ) and in other conifers (Figure  S5 ), distribution of the most abundant GO terms. The Python script for the genome summary statistics is presented in Supplementary Material 2. Supplemental material available at FigShare: https://doi. org/10. 25387/g3. 7706717.
Genome completeness was estimated with three methods based on the presence of conserved genes. CEGMA estimated 81. 5% completeness using 248 conserved eukaryotic genes. Using larger gene sets, BUSCO estimated a completeness of 49%, whereas mapping to the P. taeda transcriptome resulted in a completeness estimate of 69%. The contiguity of the silver fir assembly was also compared to those of other available conifer genome assemblies (Tree Gene Database; https://treegenesdb. org/). The scaffold N50 (scfN50) of the silver fir assembly was 14. 05 Kb, almost double that of the 5. 21 Kb scfN50 of the latest P. abies assembly (Paab1. 0b) and the 6. 44 Kb of the L. sibirica assembly (Table  3 ). However, it is still far below those of P. lambertiana (2,509. 9 Kb), P. glauca (110. 56 Kb), P. taeda (2,108.
3 Kb) and P. menziesii (372. 39 Kb; Table  3 ). The assembly completeness is estimated to be moderately high with 81. 5% of the Core Eukaryotic Genes as estimated by CEGMA, 65% of 956 plant orthologs as estimated by BUSCO and at least 69% P. taeda transcripts mapping to the assembly. As each of these methods are also affected by assembly fragmentation, the most likely explanation for less than ideal "completeness" is that the assembly is too fragmented for a good fraction of genes to be detected properly by the programs rather than the genes being truly missing from the assembly. While this first draft of the silver fir genome is highly fragmented, as were earlier conifer genome assemblies, likely due to the presumed density of repetitive sequences typical for plant genomes  (Bennetzen and Wang 2014) , it represents a very valuable reference resource to the community and can be used immediately to facilitate a broad spectrum of genetic and genomic studies in a demographic, evolutionary, and ecological context.
Annotation Protein-coding gene annotation: According to the repeat annotation performed, 78% (14. 25 Gb) of the genome assembly corresponds to repeats. In the non-repetitive fraction, 94,205 genes were annotated whose 98,227 transcripts encode 97,750 proteins (Table  4 ). However, the number of distinct genes is inflated as many partial genes have been annotated due to the large fragmentation of the assembly. Supporting this assessment, the median gene length was 558 bp, half of the genes were mono-exonic and approximately half of the annotated proteins (44,646) contained only partial ORFs (missing a start or stop codon). Of the 97,750 protein sequences, 39,420 (35. 8%) were assigned functional labels, while the rest (58,327 proteins) were analyzed with BLAST, but failed to return significant hits against the RefSeq database.
ACKNOWLEDGMENTS The authors thank  Berta Fusté  from the  CNAG-CRG, Centre for Genomic Regulation  for her help in managing this project. Technical support from the  WSL  nursery staff is highly appreciated. We would also thank  Aleksey Zimin ,  Daniela Puiu  and  Michael Schatz  for their comments and advice on genome and organelle assembly. This work was in part supported by grants of the  National Bioinformatics Institute (INB) ,  PRB2-ISCIII (PT13/0001/0044  to JG). Authors would like to thank " ELIXIR-ITA HPC@CINECA " for providing the computing resources to complete some bioinformatic tasks within this project. We dedicate this paper to the memory of our colleague Eric Bazin.