The lexical semantic relationships between word pairs are key features for many NLP tasks. Most approaches for automatically classifying related word pairs are hindered by data sparsity because of their need to observe two words co-occurring in order to detect the lexical relation holding between them. Even when mining very large corpora, not every related word pair co-occurs. Using novel representations based on graphs and word embeddings, we present two systems that are able to predict relations between words, even when these are never found in the same sentence in a given corpus. In two experiments, we demonstrate superior performance of both approaches over the state of the art, achieving significant gains in recall.
In this paper we propose a small set of lexical conceptual relations which allow to encode adjectives in computational relational lexica in a principled and integrated way. Our main motivation comes from the fact that adjectives and certain classes of verbs, related in a way or another with adjectives, do not have a satisfactory representation in this kind of lexica. This is due to a great extent to the heterogeneity of their semantic and syntactic properties. We sustain that such properties are mostly derived from the relations holding between adjectives and other POS. Accordingly, our proposal is mainly concerned with the specification of appropriate cross-POS relations to encode adjectives in lexica of the type considered here.
Arabinogalactan proteins (AGPs) are hydroxyproline-rich glycoproteins containing a high proportion of carbohydrates, widely distributed in the plant kingdom and ubiquitously present in land plants. AGPs have long been suggested to play important roles in plant reproduction and there is already evidence that specific glycoproteins are essential for male and female gametophyte development, pollen tube growth and guidance, and successful fertilization. However, the functions of many of these proteins have yet to be uncovered, mainly due to the difficulty to study individual AGPs. In this work, we generated molecular tools to analyze the expression patterns of a subgroup of individual AGPs in different Arabidopsis tissues, focusing on reproductive processes. This study focused on six AGPs: four classical AGPs (AGP7, AGP25, AGP26, AGP27), one AG peptide (AGP24) and one chimeric AGP (AGP31). These AGPs were first selected based on their predicted expression patterns along the reproductive tissues from available RNA-seq data. Promoter analysis using β-glucuronidase fusions and qPCR in different Arabidopsis tissues allowed to confirm these predictions. AGP7 was mainly expressed in female reproductive tissues, more precisely in the style, funiculus, and integuments near the micropyle region. AGP25 was found to be expressed in the style, septum and ovules with higher expression in the chalaza and funiculus tissues. AGP26 was present in the ovules and pistil valves. AGP27 was expressed in the transmitting tissue, septum and funiculus during seed development. AGP24 was expressed in pollen grains, in mature embryo sacs, with highest expression at the chalazal pole and in the micropyle. AGP31 was expressed in the mature embryo sac with highest expression at the chalaza and, occasionally, in the micropyle. For all these AGPs a co-expression analysis was performed providing new hints on its possible functions. This work confirmed the detection in Arabidopsis male and female tissues of six AGPs never studied before regarding the reproductive process. These results provide novel evidence on the possible involvement of specific AGPs in plant reproduction, as strong candidates to participate in pollen-pistil interactions in an active way, which is significant for this field of study.
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