Lamia Tounsi

DCU: Aspect-based Polarity Classification for SemEval Task 4

We describe the work carried out by DCU on the Aspect Based Sentiment Analysis task at SemEval 2014. Our team submitted one constrained run for the restaurant domain and one for the laptop domain for sub-task B (aspect term polarity prediction), ranking highest out of 36 systems on the restaurant test set and joint highest out of 32 systems on the laptop test set.

A Lexical Database for Modern Standard Arabic Interoperable with a Finite State Morphological Transducer

Current Arabic lexicons, whether computational or otherwise, make no distinction between entries from Modern Standard Arabic (MSA) and Classical Arabic (CA), and tend to include obsolete words that are not attested in current usage. We address this problem by building a large-scale, corpus-based lexical database that is representative of MSA. We use an MSA corpus of 1,089,111,204 words, a pre-annotation tool, machine learning techniques, and knowledge-based templatic matching to automatically acquire and filter lexical knowledge about morpho-syntactic attributes and inflection paradigms. Our lexical database is scalable, interoperable and suitable for constructing a morphological analyser, regardless of the design approach and programming language used. The database is formatted according to the international ISO standard in lexical resource representation, the Lexical Markup Framework (LMF). This lexical database is used in developing an open-source finite-state morphological processing toolkit. We build a web application, AraComLex (Arabic Computer Lexicon), for managing and curating the lexical database.

ZamAn and raqm: extracting temporal and numerical expressions in arabic

In this paper we investigate automatic identification of Arabic temporal and numerical expressions. The objectives of this paper are 1) to describe ZamAn , a machine learning method we have developed to label Arabic temporals, processing the functional dashtag -TMP used in the Arabic treebank to mark a temporal modifier which represents a reference to a point in time or a span of time, and 2) to present Raqm , a machine learning method applied to identify different forms of numerical expressions in order to normalise them into digits. We present a series of experiments evaluating how well ZamAn (resp. Raqm ) copes with the enriched Arabic data achieving state-of-the-art results of F1-measure of 88.5% (resp. 96%) for bracketing and 73.1% (resp. 94.4%) for detection.

Cross-lingual Transfer Parsing for Low-Resourced Languages: An Irish Case Study

We present a study of cross-lingual direct transfer parsing for the Irish language. Firstly we discuss mapping of the annotation scheme of the Irish Dependency Treebank to a universal dependency scheme. We explain our dependency label mapping choices and the structural changes required in the Irish Dependency Treebank. We then experiment with the universally annotated treebanks of ten languages from four language family groups to assess which languages are the most useful for cross-lingual parsing of Irish by using these treebanks to train delexicalised parsing models which are then applied to sentences from the Irish Dependency Treebank. The best results are achieved when using Indonesian, a language from the Austronesian language family.

Risk Information Extraction and Aggregation

By exploiting advances in natural language processing, we believe that information contained in unstructured texts can be leveraged to facilitate risk modeling and decision support in healthcare. In this paper, we present our initial investigations into dependence relation extraction and aggregation into a Bayesian Belief Network structure. Our results are based on a corpus composed of MEDLINE® abstracts dealing with breast cancer risk factors.

Basic search of sub automata application to electronic dictionaries

This paper we present a new method to detect and compute a set of sub structures of an automaton. This method is applied through a search algorithm for sub automata recognition and used in natural language processing (NLP) applications such as dictionaries. This algorithm is based on the notion of height and cardinality of states; it visits the states of a minimal deterministic finite state automaton in a depth first order where each state is inspected once.