Recent work on Semantic Role Labeling (SRL) has shown that to achieve high accuracy a joint inference on the whole predicate argument structure should be applied. In this paper, we used syntactic subtrees that span potential argument structures of the target predicate in tree kernel functions. This allows Support Vector Machines to discern between correct and incorrect predicate structures and to re-rank them based on the joint probability of their arguments. Experiments on the PropBank data show that both classification and re-ranking based on tree kernels can improve SRL systems.

Developing a full coreference system able to run all the way from raw text to semantic interpretation is a considerable engineering effort. Accordingly, there is very limited availability of off-the shelf tools for researchers whose interests are not primarily in coreference or others who want to concentrate on a specific aspect of the problem. We present BART, a highly modular toolkit for developing coreference applications. In the Johns Hopkins workshop on using lexical and encyclopedic knowledge for entity disambiguation, the toolkit was used to extend a reimplementation of the Soon et al. (2001) proposal with a variety of additional syntactic and knowledge-based features, and experiment with alternative resolution processes, preprocessing tools, and classifiers. 1.

This paper presents a comparative evaluation of several state-of-the-art English parsers based on different frameworks. Our approach is to measure the impact of each parser when it is used as a component of an information extraction system that performs protein-protein interaction (PPI) identification in biomedical papers. We evaluate eight parsers (based on dependency parsing, phrase structure parsing, or deep parsing) using five different parse representations. We run a PPI system with several combinations of parser and parse representation, and examine their impact on PPI identification accuracy. Our experiments show that the levels of accuracy obtained with these different parsers are similar, but that accuracy improvements vary when the parsers are retrained with domain-specific data.

There is a widely held belief in the natural language and computational linguistics communities that Semantic Role Labeling (SRL) is a significant step toward improving important applications, e.g. question answering and information extraction. In this paper, we present an SRL system for Modern Standard Arabic that exploits many aspects of the rich morphological features of the language. The experiments on the pilot Arabic Propbank data show that our system based on Support Vector Machines and Kernel Methods yields a global SRL F1 score of 82.17%, which improves the current state-of-the-art in Arabic SRL. 1

Recent work on the design of automatic systems for semantic role labeling has shown that feature engineering is a complex task from a modeling and implementation point of view. Tree kernels alleviate such complexity as kernel functions generate features automatically and require less software development for data extraction. In this paper, we study several tree kernel approaches for both boundary detection and argument classification. The comparative experiments on Support Vector Machines with such kernels on the CoNLL 2005 dataset show that very simple tree manipulations trigger automatic feature engineering that highly improves accuracy and efficiency in both phases. Moreover, the use of different classifiers for internal and pre-terminal nodes maintains the same accuracy and highly improves efficiency. 1

The combination of Support Vector Machines with very high dimensional kernels, such as string or tree kernels, suffers from two major drawbacks: first, the implicit representation of feature spaces does not allow us to understand which features actually triggered the generalization; second, the resulting computational burden may in some cases render unfeasible to use large data sets for training. We propose an approach based on feature space reverse engineering to tackle both problems. Our experiments with Tree Kernels on a Semantic Role Labeling data set show that the proposed approach can drastically reduce the computational footprint while yielding almost unaffected accuracy. 1

Abstract—Modern computing systems are instrumented to generate huge amounts of system logs and these data can be utilized for understanding and complex system behaviors. One main fundamental challenge in automated log analysis is the generation of system events from raw textual logs. Recent works apply clustering techniques to translate the raw log messages into system events using only the word/term information. In this paper, we first illustrate the drawbacks of existing techniques for event generation from system logs. We then propose LogTree, a novel and algorithm-independent framework for events generation from raw system log messages. LogTree utilizes the format and structural information of the raw logs in the clustering process to generate system events with better accuracy. In addition, an indexing data structure, Message Segment Table, is proposed in LogTree to significantly improve the efficiency of events generation. Extensive experiments on real system logs demonstrate the effectiveness and efficiency of LogTree. Keywords-event generation; mining system log data; LogTree; log message clustering. I.

In this paper, we describe an approach based on off-the-shelf parsers and semantic resources for the Recognizing Textual Entailment (RTE) challenge that can be generally applied to any domain. Syntax is exploited by means of tree kernels whereas lexical semantics is derived from heterogeneous resources, e.g. WordNet or distributional semantics through Wikipedia. The joint syntactic/semantic model is realized by means of tree kernels, which can exploit lexical relatedness to match syntactically similar structures, i.e. whose lexical compounds are related. The comparative experiments across different RTE challenges and traditional systems show that our approach consistently and meaningfully achieves high accuracy, without requiring any adaptation or tuning. 1

This system uses a background knowledge base to identify semantic relations between base noun phrases in English text, as evaluated in SemEval 2007, Task 4. Training data for each relation is converted to statements in the Scone Knowledge Representation Language. At testing time a new Scone statement is created for the sentence under scrutiny, and presence or absence of a relation is calculated by comparing the total semantic distance between the new statement and all positive examples to the total distance between the new statement and all negative examples. 1

Question Answering (QA) is the task of searching a large text collection for specific answers to questions posed in natural language. Many QA systems rely heavily on Natural Language Processing (NLP) technology, such as syntactic and semantic parsing and named entity recognition, for question analysis and for answer generation. To access the text collection, QA systems often use off-the-shelf bag-of-words Information Retrieval (IR) solutions, which rank results by matching a set of keyterms extracted from the question. There is a fundamental disconnect between the capabilities of the bag-of-words retrieval model and the retrieval needs of the QA system. Bag-of-words IR retrieves documents matching a query, but the QA system really needs documents that contain answers. Through question analysis, the QA system has compiled a sophisticated information need representation for what constitutes an answer to the question. This representation is composed of a set of linguistic and semantic constraints