Recommendation systems are essential for music platforms to drive exploration and discovery for users. Little work has been done in exploring cross-language music recommendation systems, which represent another avenue for music exploration. In this paper, we collected and created a database of over 200,000 artists, which includes subsets of artists that sing in 8 different languages other than English. Our goal was to recommend artists in those 8 other language subsets for a given English-speaking artist. Using Spotify’s API-related artists feature, we implemented two approaches: a matrix factorization model using alternating least squares and a breadth-first search system. Both systems perform significantly better than a random baseline based on accuracy of the base artist’s genre with the breadth-first search model outperforming the matrix factorization technique. We conclude with suggestions for improving the performance and reach of cross-language music recommendation systems.

The goal of text simplification (TS) is to transform difficult text into a version that is easier to understand and more broadly accessible to a wide variety of readers.In some domains, such as healthcare, fully automated approaches cannot be used since information must be accurately preserved.Instead, semi-automated approaches can be used that assist a human writer in simplifying text faster and at a higher quality.In this paper, we examine the application of autocomplete to text simplification in the medical domain.We introduce a new parallel medical data set consisting of aligned English Wikipedia with Simple English Wikipedia sentences and examine the application of pretrained neural language models (PNLMs) on this dataset.We compare four PNLMs (BERT, RoBERTa, XLNet, and GPT-2), and show how the additional context of the sentence to be simplified can be incorporated to achieve better results (6.17% absolute improvement over the best individual model).We also introduce an ensemble model that combines the four PNLMs and outperforms the best individual model by 2.1%, resulting in an overall word prediction accuracy of 64.52%.

Our goal is data-driven discovery of features for text simplification. In this paper, we investigate three types of lexical chains: exact, synonymous, and semantic. A lexical chain links semantically related words in a document. We examine their potential with a document-level corpus statistics study (914 texts) to estimate their overall capacity to differentiate between easy and difficult text and a classification task (11 000 sentences) to determine usefulness of features at sentence-level for simplification. For the corpus statistics study we tested five document-level features for each chain type: total number of chains, average chain length, average chain span, number of crossing chains, and the number of chains longer than half the document length. We found significant differences between easy and difficult text for average chain length and the average number of cross chains. For the sentence classification study, we compared the lexical chain features to standard bag-of-words features on a range of classifiers: logistic regression, naïve Bayes, decision trees, linear and RBF kernel SVM, and random forest. The lexical chain features performed significantly better than the bag-of-words baseline across all classifiers with the best classifier achieving an accuracy of ∼90% (compared to 78% for bag-of-words). Overall, we find several lexical chain features provide specific information useful for identifying difficult sentences of text, beyond what is available from standard lexical features.

Text simplification often relies on dated, unproven readability formulas. As an alternative and motivated by the success of term familiarity, we test a complementary measure: grammar familiarity. Grammar familiarity is measured as the frequency of the 3rd level sentence parse tree and is useful for evaluating individual sentences. We created a database of 140K unique 3rd level parse structures by parsing and binning all 5.4M sentences in English Wikipedia. We then calculated the grammar frequencies across the corpus and created 11 frequency bins. We evaluate the measure with a user study and corpus analysis. For the user study, we selected 20 sentences randomly from each bin, controlling for sentence length and term frequency, and recruited 30 readers per sentence (N = 6,600) on Amazon Mechanical Turk. We measured actual difficulty (comprehension) using a Cloze test, perceived difficulty using a 5‐point Likert scale, and time taken. Sentences with more frequent grammatical structures, even with very different surface presentations, were easier to understand, perceived as easier, and took less time to read. Outcomes from readability formulas correlated with perceived but not with actual difficulty. Our corpus analysis shows how the metric can be used to understand grammar regularity in a broad range of corpora.

The Symposium on Educational Advances in Artificial Intelligence (EAAI-13) seeks to advance the AAAI goal of improving the teaching and training of AI practitioners. The symposium provides a venue for both researchers and educators to discuss pedagogical issues and share resources related to teaching AI and using AI in education across a variety of curricular levels (K-12 through postgraduate training).

Multinomial distributions are often used to model text documents. However, they do not capture well the phenomenon that words in a document tend to appear in bursts: if a word appears once, it is more likely to appear again. In this paper, we propose the Dirichlet compound multinomial model (DCM) as an alternative to the multinomial. The DCM model has one additional degree of freedom, which allows it to capture burstiness. We show experimentally that the DCM is substantially better than the multinomial at modeling text data, measured by perplexity. We also show using three standard document collections that the DCM leads to better classification than the multinomial model. DCM performance is comparable to that obtained with multiple heuristic changes to the multinomial model. 1.

We describe a procedure which finds a hierarchical clustering by hill- climbing. The cost function we use is a hierarchical extension of the k-means cost; our local moves are tree restructurings and node reorder- ings. We show these can be accomplished efficiently, by exploiting spe- cial properties of squared Euclidean distances and by using techniques from scheduling algorithms.