We investigate in this paper the adequate unit of analysis for Arabic Mention Detection. We experiment different segmentation schemes with various feature-sets. Results show that when limited resources are available, models built on morphologically segmented data outperform other models by up to 4F points. On the other hand, when more resources extracted from morphologically segmented data beco...

One of the key challenges in natural language processing (NLP) is to yield good performance across application domains and languages. In this work, we investigate the robustness of the mention detection systems, one of the fundamental tasks in information extraction, via recurrent neural networks (RNNs). The advantage of RNNs over the traditional approaches is their capacity to capture long ran...

We consider the problem of learning rules from natural language text sources. These sources, such as news articles, journal articles, and web texts, are created by a writer to communicate information to a reader, where the writer and reader share substantial domain knowledge. Consequently, the texts tend to be concise and mention the minimum information necessary for the reader to draw the corr...

Most wh-questions admit only exhaustive answers. For example, to properly answer (1), the addressee needs to specify all the attendants to the party, as in (1a). If the addressee can only provide a non-exhaustive answer, then he needs to indicate the incompleteness of his answer. For instance, he can mark his answer with a prosodic rise-fall-rise contour (in the following indicated by ‘.../’), ...

In this paper we briefly describe our supervised machine learning approach for disorder mention detection system that we submitted as part of our participation in the SemEval-2014 Shared task. The main goal of this task is to build a system that automatically identifies mentions of clinical conditions from the clinical texts. The main challenge lies due in the fact that the same mention of conc...

We present an experimental framework for Entity Mention Detection in which two different classifiers are combined to exploit Data Redundancy attained through the annotation of a large text corpus, as well as a number of Patterns extracted automatically from the same corpus. In order to recognize proper name, nominal, and pronominal mentions we not only exploit the information given by mentions ...

We participated Task 1 using an existing system MedTagger implemented in integrated cTAKES (icTAKES). The concept mention detection is based on Conditional Random Fields (CRF) and the concept mention normalization is based on a greedy dictionary lookup algorithm. A distinctive feature in MedTagger compared to other concept mention detection systems is the incorporation of dictionary lookup resu...

In this study, we investigated the problem of recommending usernames when people attempt to use the “@” sign to mention other people in twitter-like social media. With the extremely rapid development of social networking services, this problem has received considerable attention in recent years. Previous methods have studied the problem from different aspects. Because most of Twitter-like micro...

Gene mention normalization (GN) refers to the automated mapping of gene names to a unique identifier, such as an NCBI Entrez Gene ID. Such knowledge helps in indexing and retrieval, linkage to additional information (such as sequences), database curation, and data integration. We present here an ensemble system encompassing LINNAEUS for recognizing organism names and GNAT for recognition and no...