We develop a supervised ranking model to rerank candidates generated from an SMT-based grammatical error correction (GEC) system. A range of novel features with respect to GEC are investigated and implemented in our reranker. We train a rank preference SVM model and demonstrate that this outperforms both Minimum Bayes-Risk and Multi-Engine Machine Translation based re-ranking for the GEC task. ...

Question answering (QA) systems provide an intuitive way of requesting concise information from a given data source. An important stage of such a system is the passage ranking stage, which ranks the possible answers based on their relevance to the question. There has been a lot of previous work on passage ranking, employing lexical, semantic or syntactic methods, but to our knowledge there has ...

this paper proposes a method for ranking decision making units (dmus) using some of the multiple criteria decision making / multiple attribute decision making          (mcdm /madm) techniques, namely, interval analytic hierarchy process (iahp)          and the technique for order preference by similarity to an ideal solution (topsis).          since the efficiency score of unity is assigned to ...

Ranking functions are instrumental for the success of an information retrieval (search engine) system. However nearly all existing ranking functions are manually designed based on experience, observations and probabilistic theories. This paper tested a novel ranking function discovery technique proposed in [Fan 2003a, Fan2003b] – ARRANGER (Automatic geneRation of RANking functions by GEnetic pR...

Answer ranking is critical to a QA (Question Answering) system because it determines the final system performance. In this paper, we explore the behavior of shallow ranking features under different conditions. The features are easy to implement and are also suitable when complex NLP techniques or resources are not available for monolingual or cross-lingual tasks. We analyze six shallow ranking ...