CogALex-V Shared Task: HsH-Supervised – supervised similarity learning using entry wise product of context vectors

The CogALex-V Shared Task provides two datasets that consists of pairs of words along with a classification of their semantic relation. The dataset for the first task distinguishes only between related and unrelated, while the second data set distinguishes several types of semantic relations. A number of recent papers propose to construct a feature vector that represents a pair of words by applying a pairwise simple operation to all elements of the feature vector. Subsequently, the pairs can be classified by training any classification algorithm on these vectors. In the present paper we apply this method to the provided datasets. We see that the results are not better than from the given simple baseline. We conclude that the results of the investigated method are strongly depended on the type of data to which it is applied

Automatic Identification of Synonym Relations in the Dutch Parliament’s Thesaurus

For indexing archived documents the Dutch Parliament uses a specialized thesaurus. For good results for full text retrieval and automatic classification it turns out to be important to add more synonyms to the existing thesaurus terms. In the present work we investigate the possibilities to find synonyms for terms of the parliaments thesaurus automatically. We propose to use distributional similarity (DS). In an experiment with pairs of synonyms and non-synonyms we train and test a classifier using distributional similarity and string similarity. Using ten-fold cross validation we were able to classify 75% of the pairs of a set of 6000 word pairs correctly

Learning thesaurus relations from distributional features

In distributional semantics words are represented by aggregated context features. The similarity of words can be computed by comparing their feature vectors. Thus, we can predict whether two words are synonymous or similar with respect to some other semantic relation. We will show on six different datasets of pairs of similar and non-similar words that a supervised learning algorithm on feature vectors representing pairs of words outperforms cosine similarity between vectors representing single words. We compared different methods to construct a feature vector representing a pair of words. We show that simple methods like pairwise addition or multiplication give better results than a recently proposed method that combines different types of features. The semantic relation we consider is relatedness of terms in thesauri for intellectual document classification. Thus our findings can directly be applied for the maintenance and extension of such thesauri. To the best of our knowledge this relation was not considered before in the field of distributional semantics

Learning thesaurus relations from distributional features

In distributional semantics words are represented by aggregated context features. The similarity of words can be computed by comparing their feature vectors. Thus, we can predict whether two words are synonymous or similar with respect to some other semantic relation. We will show on six different datasets of pairs of similar and non-similar words that a supervised learning algorithm on feature vectors representing pairs of words outperforms cosine similarity between vectors representing single words. We compared different methods to construct a feature vector representing a pair of words. We show that simple methods like pairwise addition or multiplication give better results than a recently proposed method that combines different types of features. The semantic relation we consider is relatedness of terms in thesauri for intellectual document classification. Thus our findings can directly be applied for the maintenance and extension of such thesauri. To the best of our knowledge this relation was not considered before in the field of distributional semantics

Integrating distributional and lexical information for semantic classification of words using MRMF

Semantic classification of words using distributional features is usually based on the semantic similarity of words. We show on two different datasets that a trained classifier using the distributional features directly gives better results. We use Support Vector Machines (SVM) and Multirelational Matrix Factorization (MRMF) to train classifiers. Both give similar results. However, MRMF, that was not used for semantic classification with distributional features before, can easily be extended with more matrices containing more information from different sources on the same problem. We demonstrate the effectiveness of the novel approach by including information from WordNet. Thus we show, that MRMF provides an interesting approach for building semantic classifiers that (1) gives better results than unsupervised approaches based on vector similarity, (2) gives similar results as other supervised methods and (3) can naturally be extended with other sources of information in order to improve the results

Are memantine, methylphenidate and donepezil effective in sparing cognitive functioning after brain irradiation?

One strategy to reduce neurocognitive deterioration in patients after brain irradiation is the use of neuroprotective medication. To generate up-to date knowledge regarding neuroprotective agents we performed a systematic review on the clinical effectiveness of three agents that were reported to have neuroprotective characteristics: memantine, methylphenidate and donepezil. The use of memantine after brain irradiation showed a delay in cognitive deterioration, although at 24 weeks this did not reach significance (P = 0.059). Lack of significance is likely to be the result of the limited statistical power of 35% and memantine did show significant differences in secondary outcomes. The study on methylphenidate was not conclusive. Donepezil revealed significant differences in a few cognitive tests however no difference in global cognition was found. In addition, larger effects were observed in individuals with greater cognitive dysfunction prior to treatment

Post-COVID condition in patients with inflammatory rheumatic diseases:a prospective cohort study in the Netherlands

Background: Studies on long-term consequences of COVID-19, commonly referred to as post-COVID condition, in patients with inflammatory rheumatic diseases are scarce and inconclusive. Furthermore, classifying patients with inflammatory rheumatic diseases as having post-COVID condition is complicated because of overlapping symptoms. Therefore, we investigated the risk of post-COVID condition and time until recovery, and compared the prevalence of symptoms seen in post-COVID condition, between patients with inflammatory rheumatic diseases and healthy controls, with and without a history of COVID-19. Methods: In this substudy we used data from an ongoing prospective cohort study in the Netherlands. All adult patients with inflammatory rheumatic diseases from the Amsterdam Rheumatology and Immunology Center in Amsterdam, the Netherlands, were invited to participate in the study between April 26, 2020, and March 1, 2021. All patients were asked, but not obliged, to recruit their own control participant of the same sex, of comparable age (< 5 years), and without an inflammatory rheumatic disease. Demographic and clinical data, including data on the occurrence of SARS-CoV-2 infections, were collected via online questionnaires. On March 10, 2022, all study participants received a questionnaire on the occurrence, onset, severity, and duration of persistent symptoms during the first 2 years of the COVID-19 pandemic, independent of their history of SARS-CoV-2 infection. Additionally, we prospectively monitored a subset of participants who had a PCR or antigen confirmed SARS-CoV-2 infection in the 2-month period surrounding the questionnaire in order to assess COVID-19 sequelae. In line with WHO guidelines, post-COVID condition was defined as persistent symptoms that lasted at least 8 weeks, started after the onset and within 3 months of a PCR or antigen-confirmed SARS-CoV-2 infection, and could not be explained by an alternative diagnosis. Statistical analyses included descriptive statistics, logistic regression analyses, logistic-based causal mediation analyses, and Kaplan-Meier survival analyses for time until recovery from post-COVID condition. In exploratory analyses, E-values were calculated to investigate unmeasured confounding. Findings: A total of 1974 patients with inflammatory rheumatic disease (1268 [64%] women and 706 [36%] men; mean age 59 years [SD 13]) and 733 healthy controls (495 [68%] women and 238 [32%] men; mean age 59 years [12]) participated. 468 (24%) of 1974 patients with inflammatory rheumatic disease and 218 (30%) of 733 healthy controls had a recent SARS-CoV-2 omicron infection. Of those, 365 (78%) of 468 patients with inflammatory rheumatic disease and 172 (79%) of 218 healthy controls completed the prospective follow-up COVID-19 sequelae questionnaires. More patients than controls fulfilled post-COVID condition criteria: 77 (21%) of 365 versus 23 (13%) of 172 (odds ratio [OR] 1·73 [95% CI 1·04–2·87]; p=0·033). The OR was attenuated after adjusting for potential confounders (adjusted OR 1·53 [95% CI 0·90–2·59]; p=0·12). Among those without a history of COVID-19, patients with inflammatory diseases were more likely to report persistent symptoms consistent with post-COVID condition than were healthy controls (OR 2·52 [95% CI 1·92–3·32]; p<0·0001). This OR exceeded the calculated E-values of 1·74 and 1·96. Recovery time from post-COVID condition was similar for patients and controls (p=0·17). Fatigue and loss of fitness were the most frequently reported symptoms in both patients with inflammatory rheumatic disease and healthy controls with post-COVID condition. Interpretation: Post-COVID condition after SARS-CoV-2 omicron infections was higher in patients with inflammatory rheumatic disease than in healthy controls based on WHO classification guidelines. However, because more patients with inflammatory rheumatic disease than healthy controls without a history of COVID-19 reported symptoms that are commonly used to define a post-COVID condition during the first 2 years of the pandemic, it is likely that the observed difference in post-COVID condition between patients and controls might in part be explained by clinical manifestations in the context of underlying rheumatic diseases. This highlights the limitations of applying current criteria for post-COVID condition in patients with inflammatory rheumatic disease, and suggests it might be appropriate for physicians to keep a nuanced attitude when communicating the long-term consequences of COVID-19. Funding: ZonMw (the Netherlands organization for Health Research and Development) and Reade foundation