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The use of lake sedimentary DNA to track the long-term changes in both terrestrial and aquatic biota is a rapidly advancing field in paleoecological research. Although largely applied nowadays, knowledge gaps remain in this field and there is therefore still research to be conducted to ensure the reliability of the sedimentary DNA signal. Building on the most recent literature and seven original case studies, we synthesize the state-of-the-art analytical procedures for effective sampling, extraction, amplification, quantification and/or generation of DNA inventories from sedimentary ancient DNA (sedaDNA) via high-throughput sequencing technologies. We provide recommendations based on current knowledge and best practises. published

SemEval-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation (MCL-WiC) Task Description Multilingual and Cross-lingual Word-in-Context Disambiguation (MCL-WiC) is the first SemEval task for Word-in-Context disambiguation which tackles the challenge of capturing the polysemous nature of words without relying on a fixed sense inventory in a multilingual and cross-lingual setting. MCL-WiC provides a single high-quality framework for the performance evaluation of a wide range of approaches aimed at evaluating the capability of a system to deeply understand word meaning. Compared to other datasets, MCL-WiC brings the following novelties: it addresses multilinguality and cross-linguality, it provides coverage of all parts of speech, and it covers a high number of domains and genres. Participating systems will be asked to perform a binary classification task in which they indicate whether the target word is used in the same meaning (tagged as T for true) or in a different meaning (F for false) in the same language (multilingual sub-task) or across different languages (cross-lingual sub-task). Below you can find two examples of sentence pairs, the first one from the multilingual part and the second one from the cross-lingual part: la souris mange le fromage -- le chat court après la souris click the right mouse button -- le chat court après la souris In the first sentence pair, the target word souris will be tagged with T (True) since it is used in the same meaning in both sentences. Instead, in the second sentence pair, the target word mouse and its corresponding translation into French are used in two distinct meanings, therefore, in this case, the expected output will be F (False). MCL-WiC covers the following languages: Arabic, Chinese, English, French and Russian. Files included SemEval-2021_MCL-WiC_trial.zip: trial data SemEval-2021_MCL-WiC_all-datasets.zip: training, development and test data SemEval-2021_MCL-WiC_test-gold-data.zip: gold answers Key links Github data repository: https://github.com/SapienzaNLP/mcl-wic Codalab website: https://competitions.codalab.org/competitions/27054 Link to the paper: https://aclanthology.org/2021.semeval-1.3.pdf Reference Martelli, F., Kalach, N., Tola, G and Navigli, R. SemEval-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation (MCL-WiC). Proc. of the 15th Workshop on Semantic Evaluation, 2021. BibTex: @inproceedings{martelli-etal-2021-mclwic, title = "{S}em{E}val-2021 {T}ask 2: {M}ultilingual and {C}ross-lingual {W}ord-in-{C}ontext {D}isambiguation ({MCL}-{W}i{C})", author= "Martelli, Federico and Kalach, Najla and Tola, Gabriele and Navigli, Roberto", booktitle="Proceedings of the Fifteenth Workshop on Semantic Evaluation (SemEval-2021)", year={2021} }

Recent studies treat Word Sense Disambiguation (WSD) as a single-label classification problem in which one is asked to choose only the best-fitting sense for a target word, given its context. However, gold data labelled by expert annotators suggest that maximizing the probability of a single sense may not be the most suitable training objective for WSD, especially if the sense inventory of choice is fine-grained. In this paper, we approach WSD as a multi-label classification problem in which multiple senses can be assigned to each target word. Not only does our simple method bear a closer resemblance to how human annotators disambiguate text, but it can also be seamlessly extended to exploit structured knowledge from semantic networks to achieve state-of-the-art results in English all-words WSD.

In this paper we present SPRING Online Services, a Web interface and RESTful APIs for our state-of-the-art AMR parsing and generation system, SPRING (Symmetric PaRsIng aNd Generation). The Web interface has been developed to be easily used by the Natural Language Processing community, as well as by the general public. It provides, among other things, a highly interactive visualization platform and a feedback mechanism to obtain user suggestions for further improvements of the system’s output. Moreover, our RESTful APIs enable easy integration of SPRING in downstream applications where AMR structures are needed. Finally, we make SPRING Online Services freely available at http://nlp.uniroma1.it/spring and, in addition, we release extra model checkpoints to be used with the original SPRING Python code.1

With the advent of contextualized embeddings, attention towards neural ranking approaches for Information Retrieval increased considerably. However, two aspects have remained largely neglected: i) queries usually consist of few keywords only, which increases ambiguity and makes their contextualization harder, and ii) performing neural ranking on non-English documents is still cumbersome due to shortage of labeled datasets. In this paper we present SIR (Sense-enhanced Information Retrieval) to mitigate both problems by leveraging word sense information. At the core of our approach lies a novel multilingual query expansion mechanism based on Word Sense Disambiguation that provides sense definitions as additional semantic information for the query. Importantly, we use senses as a bridge across languages, thus allowing our model to perform considerably better than its supervised and unsupervised alternatives across French, German, Italian and Spanish languages on several CLEF benchmarks, while being trained on English Robust04 data only. We release SIR at https://github.com/SapienzaNLP/sir.