BACKGROUND The practice of evidence-based medicine (EBM) requires clinicians to integrate their expertise with the latest scientific research. But this is becoming increasingly difficult with the growing numbers of published articles. There is a clear need for better tools to improve clinician's ability to search the primary literature. Randomized clinical trials (RCTs) are the most reliable so...

This paper addresses the problem of speech act recognition in written asynchronous conversations (e.g., fora, emails). We propose a class of conditional structured models defined over arbitrary graph structures to capture the conversational dependencies between sentences. Our models use sentence representations encoded by a long short term memory (LSTM) recurrent neural model. Empirical evaluat...

We present some experiments on text segmentation for German texts aimed at developing a method of segmenting historical texts. Since such texts have no (consistent) punctuation, we use a machine learning approach to label tokens with their relative positions in text segments using Conditional Random Fields. We compare the performance of this approach on the task of segmenting of text into sente...

We model the relationship between sentences and their punctuation labels using conditional random fields. Some feature functions are hand-designed and others are generated by templates. We train the same model by stochastic gradient ascent, Collins Perceptron and contrastive divergence respectively and compare their performance. On the provided dataset, we achieve word-level accuracy of 94.56%....

This paper presents a clause boundary identification system for Malayalam sentences using the machine learning approach CRF (Conditional Random Field).Malayalam Language is considered as a 'Left branching language' where verbs are seen at the end of the sentence. Clause boundary identification plays a vital role in many NLP applications and for Malayalam language, the clause boundary identifica...

This paper presents the study that we have carried out to investigate supervised opinion summarization in Modern Standard Arabic. We use a corpus of news articles. We use conditional random fields (CRF) as machine learning technique. We investigate some features to identify those that allow achieving the best results. Our contribution is to use opinion specific features to summarize Arabic news...

Conditional logic and connexive logic are two theories whose goal is the correct formalization of the way conditionals (i.e. sentences of form if A, C) are used in natural language. However, both approaches are never combined in the literature. I present here the first formal system which allows modeling the ideas behind these two approaches in a unique framework. Furthermore, the resulting sys...

We examined the comprehension of different types of conditionals. We measured the reading time of sentences primed by different types of conditionals (Experiments 1 and 2). We found that the participants read not-p and not-q faster when it was primed by the conditional form p if q and they were slower to read p and q when it was primed by the conditional form p only if q. This effect disappeare...