It tends to result in a high recall, and its weakness of low precision is perfectly fixed by the ILP model.

Our ILP model and its variants all outperform Mintz++ in precision in both datasets, indicating that our approach helps filter out incorrect predictions from the output of MaxEnt model.

Compared to ILP-2cand and original ILP , ILP-lcand leads to slightly lower precision but much lower recall, showing that selecting more candidates may help us collect more potentially correct predictions.

We use integer linear programming ( ILP ) as the solver and evaluate our framework on English and Chinese datasets.

de Lacalle and Lapata (2013) encode general domain knowledge as FOL rules in a topic model while our instantiated constraints are directly operated in an ILP model.

In this paper, we propose to solve the problem by using an ILP tool, IBM ILOG Cplexl.

By adopting ILP , we can combine the local information including MaXEnt confidence scores and the implicit relation backgrounds that are embedded into global consistencies of the entity tuples together.

Joint methods have also been proposed that invoke integer linear programming ( ILP ) formulations to simultaneously consider multiple structural inference problems—both over n-grams and input dependencies (Martins and Smith, 2009) or n-grams and all possible dependencies (Thadani and McKeown, 2013).

However, it is well-established that the utility of ILP for optimal inference in structured problems is often outweighed by the worst-case performance of ILP solvers on large problems without unique integral solutions.

In this work, we develop approximate inference strategies to the joint approach of Thadani and McKeown (2013) which trade the optimality guarantees of exact ILP for faster inference by separately solving the n-gram and dependency subproblems and using Lagrange multipliers to enforce consistency between their solutions.

The primary advantage of this technique is the ability to leverage the underlying structure of the problems in inference rather than relying on a generic ILP formulation while still often producing exact solutions.

Even if ILP-based approaches perform reasonably at the scale of single-sentence compression problems, the exponential worst-case complexity of general-purpose ILPs will inevitably pose challenges when scaling up to (a) handle larger inputs, (b) use higher-order structural fragments, or (c) incorporate additional models.

In order to produce a solution to this subproblem, we use an LP relaxation of the relevant portion of the ILP from Thadani and McKeown (2013) by omitting integer constraints over the token and dependency variables in x and 2 respectively.

(2008) we use the log-probability of the local classifiers as a score in an integer linear program ( ILP ) to assign roles subject to hard constraints described in §5.4 and §5.5.

We use an off-the-shelf ILP solver for inference.

ILP constraints For FrameNet, we used three ILP constraints during argument identification (§4).

We also compare the proposed methods with an Integer Linear Programming ( ILP ) based method for timeline construction (Do et al., 2012).

Moreover, it also outperforms the integer linear programming ( ILP ) method for timeline construction proposed in (Do et al., 2012).

We observe that in case of MSA, the optimal solution using ILP is still intractable as the number of constraints increases exponentially with the number of sequences.