In this paper, we address query-based summarization of discussion threads. New users can profit from the information shared in the forum, Please check if the inserted city and country names in the affiliations are correct. if they can find back the previously posted information. However, discussion threads on a single topic can easily comprise dozens or hundreds of individual posts. Our aim is to summarize forum threads given real web search queries. We created a data set with search queries from a discussion forum’s search engine log and the discussion threads that were clicked by the user who entered the query. For 120 thread–query combinations, a reference summary was made by five different human raters. We compared two methods for automatic summarization of the threads: a query-independent method based on post features, and Maximum Marginal Relevance (MMR), a method that takes the query into account. We also compared four different word embeddings representations as alternative for standard word vectors in extractive summarization. We find (1) that the agreement between human summarizers does not improve when a query is provided that: (2) the query-independent post features as well as a centroid-based baseline outperform MMR by a large margin; (3) combining the post features with query similarity gives a small improvement over the use of post features alone; and (4) for the word embeddings, a match in domain appears to be more important than corpus size and dimensionality. However, the differences between the models were not reflected by differences in quality of the summaries created with help of these models. We conclude that query-based summarization with web queries is challenging because the queries are short, and a click on a result is not a direct indicator for the relevance of the result.

This paper proposes four novel term evaluation metrics to represent documents in the text categorization where class distribution is imbalanced. These metrics are achieved from the revision of the four common term evaluation metrics: chi-square, information gain, odds ratio, and relevance frequency. While the common metrics require a balanced class distribution, our proposed metrics evaluate the document terms under an imbalanced distribution. They calculate the degree of relatedness of terms with respect to minor and major classes by considering their imbalanced distribution. Using these metrics in the document representation makes a better distinction between the documents of the minor and major classes and improves the performance of machine learning algorithms. The proposed metrics are assessed over three popular benchmarks (two subsets of Reuters-21578 and WebKB) by using four classification algorithms: support vector machines, naive Bayes, decision trees, and centroid-based classifiers. Our empirical results indicate that the proposed metrics outperform the common metrics in the imbalanced text categorization.

In this article, we propose an innovative and robust approach to stylometric analysis without annotation and leveraging lexical and sub-lexical information. In particular, we propose to leverage the phonological information of tones and rimes in Mandarin Chinese automatically extracted from unannotated texts. The texts from different authors were represented by tones, tone motifs, and word length motifs as well as rimes and rime motifs. Support vector machines and random forests were used to establish the text classification model for authorship attribution. From the results of the experiments, we conclude that the combination of bigrams of rimes, word-final rimes, and segment-final rimes can discriminate the texts from different authors effectively when using random forests to establish the classification model. This robust approach can in principle be applied to other languages with established phonological inventory of onset and rimes.

Various studies show that statistical machine translation (SMT) systems suffer from fluency errors, especially in the form of grammatical errors and errors related to idiomatic word choices. In this study, we investigate the effectiveness of using monolingual information contained in the machine-translated text to estimate word-level quality of SMT output. We propose a recurrent neural network architecture which uses morpho-syntactic features and word embeddings as word representations within surface and syntactic n-grams. We test the proposed method on two language pairs and for two tasks, namely detecting fluency errors and predicting overall post-editing effort. Our results show that this method is effective for capturing all types of fluency errors at once. Moreover, on the task of predicting post-editing effort, while solely relying on monolingual information, it achieves on-par results with the state-of-the-art quality estimation systems which use both bilingual and monolingual information.

This paper presents the second release of arrau, a multigenre corpus of anaphoric information created over 10 years to provide data for the next generation of coreference/anaphora resolution systems combining different types of linguistic and world knowledge with advanced discourse modeling supporting rich linguistic annotations. The distinguishing features of arrau include the following: treating all NPs as markables, including non-referring NPs, and annotating their (non-) referentiality status; distinguishing between several categories of non-referentiality and annotating non-anaphoric mentions; thorough annotation of markable boundaries (minimal/maximal spans, discontinuous markables); annotating a variety of mention attributes, ranging from morphosyntactic parameters to semantic category; annotating the genericity status of mentions; annotating a wide range of anaphoric relations, including bridging relations and discourse deixis; and, finally, annotating anaphoric ambiguity. The current version of the dataset contains 350K tokens and is publicly available from LDC. In this paper, we discuss in detail all the distinguishing features of the corpus, so far only partially presented in a number of conference and workshop papers, and we also discuss the development between the first release of arrau in 2008 and this second one.

The end of the calendar year always seems like a good time to pause for breath and reflect on what’s been happening over the last 12 months, and that’s as true in the world of commercial NLP as it is in any other domain. In particular, 2019 has been a busy year for voice assistance, thanks to the focus placed on this area by all the major technology players. So, we take this opportunity to review a number of key themes that have defined recent developments in the commercialization of voice technology.