Combination Strategies for Semantic Role Labeling

This paper introduces and analyzes a battery of inference models for the problem of semantic role labeling: one based on constraint satisfaction, and several strategies that model the inference as a meta-learning problem using discriminative classifiers. These classifiers are developed with a rich set of novel features that encode proposition and sentence-level information. To our knowledge, this is the first work that: (a) performs a thorough analysis of learning-based inference models for semantic role labeling, and (b) compares several inference strategies in this context. We evaluate the proposed inference strategies in the framework of the CoNLL-2005 shared task using only automatically-generated syntactic information. The extensive experimental evaluation and analysis indicates that all the proposed inference strategies are successful -they all outperform the current best results reported in the CoNLL-2005 evaluation exercise- but each of the proposed approaches has its advantages and disadvantages. Several important traits of a state-of-the-art SRL combination strategy emerge from this analysis: (i) individual models should be combined at the granularity of candidate arguments rather than at the granularity of complete solutions; (ii) the best combination strategy uses an inference model based in learning; and (iii) the learning-based inference benefits from max-margin classifiers and global feedback

Abstractive news summarization based on event semantic link network

This paper studies the abstractive multi-document summarization for event-oriented news texts through event information extraction and abstract representation. Fine-grained event mentions and semantic relations between them are extracted to build a unified and connected event semantic link network, an abstract representation of source texts. A network reduction algorithm is proposed to summarize the most salient and coherent event information. New sentences with good linguistic quality are automatically generated and selected through sentences over-generation and greedy-selection processes. Experimental results on DUC2006 and DUC2007 datasets show that our system significantly outperforms the state-of-the-art extractive and abstractive baselines under both pyramid and ROUGE evaluation metrics

Abstractive Multi-Document Summarization via Phrase Selection and Merging

We propose an abstraction-based multi-document summarization framework that can construct new sentences by exploring more fine-grained syntactic units than sentences, namely, noun/verb phrases. Different from existing abstraction-based approaches, our method first constructs a pool of concepts and facts represented by phrases from the input documents. Then new sentences are generated by selecting and merging informative phrases to maximize the salience of phrases and meanwhile satisfy the sentence construction constraints. We employ integer linear optimization for conducting phrase selection and merging simultaneously in order to achieve the global optimal solution for a summary. Experimental results on the benchmark data set TAC 2011 show that our framework outperforms the state-of-the-art models under automated pyramid evaluation metric, and achieves reasonably well results on manual linguistic quality evaluation.Comment: 11 pages, 1 figure, accepted as a full paper at ACL 201

Parsing Argumentation Structures in Persuasive Essays

In this article, we present a novel approach for parsing argumentation structures. We identify argument components using sequence labeling at the token level and apply a new joint model for detecting argumentation structures. The proposed model globally optimizes argument component types and argumentative relations using integer linear programming. We show that our model considerably improves the performance of base classifiers and significantly outperforms challenging heuristic baselines. Moreover, we introduce a novel corpus of persuasive essays annotated with argumentation structures. We show that our annotation scheme and annotation guidelines successfully guide human annotators to substantial agreement. This corpus and the annotation guidelines are freely available for ensuring reproducibility and to encourage future research in computational argumentation.Comment: Under review in Computational Linguistics. First submission: 26 October 2015. Revised submission: 15 July 201

Aligning English Sentences with Abstract Meaning Representation Graphs using Inductive Logic Programming

abstract: In this thesis, I propose a new technique of Aligning English sentence words with its Semantic Representation using Inductive Logic Programming(ILP). My work focusses on Abstract Meaning Representation(AMR). AMR is a semantic formalism to English natural language. It encodes meaning of a sentence in a rooted graph. This representation has gained attention for its simplicity and expressive power. An AMR Aligner aligns words in a sentence to nodes(concepts) in its AMR graph. As AMR annotation has no explicit alignment with words in English sentence, automatic alignment becomes a requirement for training AMR parsers. The aligner in this work comprises of two components. First, rules are learnt using ILP that invoke AMR concepts from sentence-AMR graph pairs in the training data. Second, the learnt rules are then used to align English sentences with AMR graphs. The technique is evaluated on publicly available test dataset and the results are comparable with state-of-the-art aligner.Dissertation/ThesisMasters Thesis Computer Science 201