We combine several graph alignment features with lexical semantic similarity measures using machine learning techniques and show that the student answers can be more accurately graded than if the semantic measures were used in isolation.

We define a total of 68 features to be used to train our machine learning system to compute node-node (more specifically, subgraph-subgraph) matches.

In this paper, we explore the possibility of improving upon existing bag-of-words (BOW) approaches to short answer grading by utilizing machine learning techniques.

First, to what extent can machine learning be leveraged to improve upon existing approaches to short answer grading.

A later implementation of the Oxford-UCLES system (Pulman and Sukkarieh, 2005) compares several machine learning techniques, including inductive logic programming, decision tree learning, and Bayesian learning, to the earlier pattern matching approach, with encouraging results.

Before applying any machine learning techniques, we first test the quality of the eight graph alignment features 2pc; (A1, A8) independently.

In particular, we use machine learning techniques to identify social power relationships between members of a social network, based purely on the content of their interpersonal communication.

Then, we apply machine learning to train classifiers with groups of these n-grams as features.

Our approach is corpus-driven like the Na'ive Bayes approach, but we interject statistically driven feature selection between the corpus and the machine learning classifiers.

We use a machine learning technique for this purpose.

We classify the citation sentences into the five categories mentioned above using a machine learning technique.

To determine whether a reference is part of the sentence or not, we again use a machine learning approach.

The state-of-the-art approaches for solving this problem, such as (Go et al., 20095; Barbosa and Feng, 2010), basically follow (Pang et al., 2002), who utilize machine learning based classifiers for the sentiment classification of texts.

According to the experimental results, machine learning based classifiers outperform the unsupervised approach, where the best performance is achieved by the SVM classifier with unigram presences as features.

(Go et al., 2009; Parikh and Movassate, 2009; Barbosa and Feng, 2010; Davidiv et al., 2010) all follow the machine learning based approach for sentiment classification of tweets.

Proposed solutions to NER fall into three categories: 1) The rule-based (Krupka and Hausman, 1998); 2) the machine learning based (Finkel and Manning, 2009; Singh et al., 2010) ; and 3) hybrid methods (J ansche and Abney, 2002).

Algorithm 1 outlines our method, where: trains and twink, denote two machine learning processes to get the CRF labeler and the KNN classifier, respectively; reprw converts a word in a tweet into a bag-of-words vector; the reprt function transforms a tweet into a feature matrix that is later fed into the CRF model; the knn function predicts the class of a word; the update function applies the predicted class by KNN to the inputted tweet; the C7“ f function conducts word level NE labeling;7' and 7 represent the minimum labeling confidence of KNN and CRF, respectively, which are experimentally set to 0.1 and 0.001; N (1,000 in our work) denotes the maximum number of new accumulated training data.

Machine learning based systems are commonly used and outperform the rule based systems.

We demonstrate how supervised discriminative machine learning techniques can be used to automate the assessment of ‘English as a Second or Other Language’ (ESOL) examination scripts.

Different techniques have been used, including cosine similarity of vectors representing text in various ways (Attali and Burstein, 2006), often combined with dimensionality reduction techniques such as Latent Semantic Analysis (LSA) (Landauer et al., 2003), generative machine learning models (Rudner and Liang, 2002), domain-specific feature extraction (Attali and Burstein, 2006), and/or modified syntactic parsers (Lonsdale and Strong-Krause, 2003).

We address automated assessment as a supervised discriminative machine learning problem and particularly as a rank preference problem (J oachims, 2002).