Cluster-Based News Representative Generation with Automatic Incremental Clustering

Nowadays, a large volume of news circulates around the Internet in one day, amounting to more than two thousand news. However, some of these news have the same topic and content, trapping readers among different sources of news that say similar things. This research proposes a new approach to provide a representative news automatically through the Automatic Incremental Clustering method. This method began with the Data Acquisition process, Keyword Extraction, and Metadata Aggregation to produce a news metadata matrix. The news metadata matrix consisted of types of word in the column and news section of each line. Furthermore, the news on the matrix were grouped by the Automatic Incremental Clustering method based on the number of word similarities that arised, calculated using the Euclidean Distance approach, and was done automatically and real-time. Each cluster (topic) determined one representing news as a Representative News based on the location of the news closest to the midpoint/centroid on the cluster. This study used 101 news as experimental data and produced 87 news clusters with 85.14% precision ratio

A Monte Carlo Language Model Pipeline for Zero-Shot Sociopolitical Event Extraction

We consider dyadic zero-shot event extraction (EE) to identify actions between pairs of actors. The \emph{zero-shot} setting allows social scientists or other non-computational researchers to extract any customized, user-specified set of events without training, resulting in a \emph{dyadic} event database, allowing insight into sociopolitical relational dynamics among actors and the higher level organizations or countries they represent. Unfortunately, we find that current zero-shot EE methods perform poorly for the task, with issues including word sense ambiguity, modality mismatch, and efficiency. Straightforward application of large language model prompting typically performs even worse. We address these challenges with a new fine-grained, multi-stage generative question-answer method, using a Monte Carlo approach to exploit and overcome the randomness of generative outputs. It performs 90\% fewer queries than a previous approach, with strong performance on the widely-used Automatic Content Extraction dataset. Finally, we extend our method to extract affiliations of actor arguments and demonstrate our method and findings on a dyadic international relations case study

Detecting New Word Meanings: A Comparison of Word Embedding Models in Spanish

Semantic neologisms (SN) are defined as words that acquire a new word meaning while maintaining their form. Given the nature of this kind of neologisms, the task of identifying these new word meanings is currently performed manually by specialists at observatories of neology. To detect SN in a semi-automatic way, we developed a system that implements a combination of the following strategies: topic modeling, keyword extraction, and word sense disambiguation. The role of topic modeling is to detect the themes that are treated in the input text. Themes within a text give clues about the particular meaning of the words that are used, for example: viral has one meaning in the context of computer science (CS) and another when talking about health. To extract keywords, we used TextRank with POS tag filtering. With this method, we can obtain relevant words that are already part of the Spanish lexicon. We use a deep learning model to determine if a given keyword could have a new meaning. Embeddings that are different from all the known meanings (or topics) indicate that a word might be a valid SN candidate. In this study, we examine the following word embedding models: Word2Vec, Sense2Vec, and FastText. The models were trained with equivalent parameters using Wikipedia in Spanish as corpora. Then we used a list of words and their concordances (obtained from our database of neologisms) to show the different embeddings that each model yields. Finally, we present a comparison of these outcomes with the concordances of each word to show how we can determine if a word could be a valid candidate for SN.Comment: 16 pages, 3 figure

Improving the translation environment for professional translators

When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side. This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project

Extraction of Keyphrases from Text: Evaluation of Four Algorithms

This report presents an empirical evaluation of four algorithms for automatically extracting keywords and keyphrases from documents. The four algorithms are compared using five different collections of documents. For each document, we have a target set of keyphrases, which were generated by hand. The target keyphrases were generated for human readers; they were not tailored for any of the four keyphrase extraction algorithms. Each of the algorithms was evaluated by the degree to which the algorithm’s keyphrases matched the manually generated keyphrases. The four algorithms were (1) the AutoSummarize feature in Microsoft’s Word 97, (2) an algorithm based on Eric Brill’s part-of-speech tagger, (3) the Summarize feature in Verity’s Search 97, and (4) NRC’s Extractor algorithm. For all five document collections, NRC’s Extractor yields the best match with the manually generated keyphrases