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89
Exploiting syntactic and shallow semantic kernels for question answer classification
- In Proc. of ACL-07
, 2007
"... We study the impact of syntactic and shallow semantic information in automatic classification of questions and answers and answer re-ranking. We define (a) new tree structures based on shallow semantics encoded in Predicate Argument Structures (PASs) and (b) new kernel functions to exploit the repre ..."
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Cited by 76 (30 self)
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We study the impact of syntactic and shallow semantic information in automatic classification of questions and answers and answer re-ranking. We define (a) new tree structures based on shallow semantics encoded in Predicate Argument Structures (PASs) and (b) new kernel functions to exploit the representational power of such structures with Support Vector Machines. Our experiments suggest that syntactic information helps tasks such as question/answer classification and that shallow semantics gives remarkable contribution when a reliable set of PASs can be extracted, e.g. from answers. 1
Kernel methods, syntax and semantics for relational text categorization
- In: CIKM
, 2008
"... Previous work on Natural Language Processing for Information Retrieval has shown the inadequateness of semantic and syntac-tic structures for both document retrieval and categorization. The main reason is the high reliability and effectiveness of language models, which are sufficient to accurately s ..."
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Cited by 42 (15 self)
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Previous work on Natural Language Processing for Information Retrieval has shown the inadequateness of semantic and syntac-tic structures for both document retrieval and categorization. The main reason is the high reliability and effectiveness of language models, which are sufficient to accurately solve such retrieval tasks. However, when the latter involve the computation of relational se-mantics between text fragments simple statistical models may re-sult ineffective. In this paper, we show that syntactic and semantic structures can be used to greatly improve complex categorization tasks such as determining if an answer correctly responds to a ques-tion. Given the high complexity of representing semantic/syntactic structures in learning algorithms, we applied kernel methods along with Support Vector Machines to better exploit the needed rela-tional information. Our experiments on answer classification on Web and TREC data show that our models greatly improve on bag-of-words.
Tree Edit Models for Recognizing Textual Entailments, Paraphrases, and Answers to Questions
"... We describe tree edit models for representing sequences of tree transformations involving complex reordering phenomena and demonstrate that they offer a simple, intuitive, and effective method for modeling pairs of semantically related sentences. To efficiently extract sequences of edits, we employ ..."
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Cited by 40 (1 self)
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We describe tree edit models for representing sequences of tree transformations involving complex reordering phenomena and demonstrate that they offer a simple, intuitive, and effective method for modeling pairs of semantically related sentences. To efficiently extract sequences of edits, we employ a tree kernel as a heuristic in a greedy search routine. We describe a logistic regression model that uses 33 syntactic features of edit sequences to classify the sentence pairs. The approach leads to competitive performance in recognizing textual entailment, paraphrase identification, and answer selection for question answering. 1
Semantic sentiment analysis of twitter. In:
- Proc. 11th Int. Semantic Web Conf. (ISWC).
, 2012
"... Abstract. Sentiment analysis over Twitter offer organisations a fast and effective way to monitor the publics' feelings towards their brand, business, directors, etc. A wide range of features and methods for training sentiment classifiers for Twitter datasets have been researched in recent yea ..."
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Cited by 37 (7 self)
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Abstract. Sentiment analysis over Twitter offer organisations a fast and effective way to monitor the publics' feelings towards their brand, business, directors, etc. A wide range of features and methods for training sentiment classifiers for Twitter datasets have been researched in recent years with varying results. In this paper, we introduce a novel approach of adding semantics as additional features into the training set for sentiment analysis. For each extracted entity (e.g. iPhone) from tweets, we add its semantic concept (e.g. "Apple product") as an additional feature, and measure the correlation of the representative concept with negative/positive sentiment. We apply this approach to predict sentiment for three different Twitter datasets. Our results show an average increase of F harmonic accuracy score for identifying both negative and positive sentiment of around 6.5% and 4.8% over the baselines of unigrams and part-of-speech features respectively. We also compare against an approach based on sentiment-bearing topic analysis, and find that semantic features produce better Recall and F score when classifying negative sentiment, and better Precision with lower Recall and F score in positive sentiment classification.
A syntactic tree matching approach to finding similar questions in community-based qa services
- In ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2009
, 2009
"... While traditional question answering (QA) systems tailored to the TREC QA task work relatively well for simple questions, they do not suffice to answer real world questions. The community-based QA systems offer this service well, as they contain large archives of such questions where manually crafte ..."
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Cited by 33 (4 self)
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While traditional question answering (QA) systems tailored to the TREC QA task work relatively well for simple questions, they do not suffice to answer real world questions. The community-based QA systems offer this service well, as they contain large archives of such questions where manually crafted answers are directly available. However, finding similar questions in the QA archive is not trivial. In this paper, we propose a new retrieval framework based on syntactic tree structure to tackle the similar question matching problem. We build a ground-truth set from Yahoo! Answers, and experimental results show that our method outperforms traditional bag-of-word or tree kernel based methods by 8.3 % in mean average precision. It further achieves up to 50% improvement by incorporating semantic features as well as matching of potential answers. Our model does not rely on training, and it is demonstrated to be robust against grammatical errors as well.
Fast and Effective Kernels for Relational Learning from Texts
"... In this paper, we define a family of syntactic kernels for automatic relational learning from pairs of natural language sentences. We provide an efficient computation of such models by optimizing the dynamic programming algorithm of the kernel evaluation. Experiments with Support Vector Machines and ..."
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Cited by 24 (16 self)
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In this paper, we define a family of syntactic kernels for automatic relational learning from pairs of natural language sentences. We provide an efficient computation of such models by optimizing the dynamic programming algorithm of the kernel evaluation. Experiments with Support Vector Machines and the above kernels show the effectiveness and efficiency of our approach on two very important natural language tasks, Textual Entailment Recognition and Question Answering. 1.
UDDI project --- Universal Description, Discovery and Integration
- Advances in Information Retrieval - Proceedings of the 29th European Conference on Information Retrieval (ECIR 2007
"... Abstract. The exploitation of syntactic structures and semantic background knowledge has always been an appealing subject in the context of text retrieval and information management. The usefulness of this kind of information has been shown most prominently in highly specialized tasks, such as class ..."
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Cited by 19 (6 self)
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Abstract. The exploitation of syntactic structures and semantic background knowledge has always been an appealing subject in the context of text retrieval and information management. The usefulness of this kind of information has been shown most prominently in highly specialized tasks, such as classification in Question Answering (QA) scenarios. So far, however, additional syntactic or semantic information has been used only individually. In this paper, we propose a principled approach for jointly exploiting both types of information. We propose a new type of kernel, the Semantic Syntactic Tree Kernel (SSTK), which incorporates linguistic structures, e.g. syntactic dependencies, and semantic background knowledge, e.g. term similarity based on WordNet, to automatically learn question categories in QA. We show the power of this approach in a series of experiments with a well known Question Classification dataset. 1
Structural relationships for large-scale learning of answer reranking
- In SIGIR
, 2012
"... Supervised learning applied to answer re-ranking can highly improve on the overall accuracy of question answering (QA) systems. The key aspect is that the relationships and prop-erties of the question/answer pair composed of a question and the supporting passage of an answer candidate, can be effici ..."
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Cited by 18 (10 self)
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Supervised learning applied to answer re-ranking can highly improve on the overall accuracy of question answering (QA) systems. The key aspect is that the relationships and prop-erties of the question/answer pair composed of a question and the supporting passage of an answer candidate, can be efficiently compared with those captured by the learnt model. In this paper, we define novel supervised approaches that exploit structural relationships between a question and their candidate answer passages to learn a re-ranking model. We model structural representations of both questions and answers and their mutual relationships by just using an off-the-shelf shallow syntactic parser. We encode structures in Support Vector Machines (SVMs) by means of sequence and tree kernels, which can implicitly represent question and an-swer pairs in huge feature spaces. Such models together with the latest approach to fast kernel-based learning enabled the training of our rerankers on hundreds of thousands of instances, which previously rendered intractable for kernel-ized SVMs. The results on two different QA datasets, e.g., Answerbag and Jeopardy! data, show that our models de-liver large improvement on passage re-ranking tasks, reduc-ing the error in Recall of BM25 baseline by about 18%. One of the key findings of this work is that, despite its simplicity, shallow syntactic trees allow for learning complex relational structures, which exhibits a steep learning curve with the increase in the training size.
Kernels on Linguistic Structures for Answer Extraction
"... Natural Language Processing (NLP) for Information Retrieval has always been an interesting and challenging research area. Despite the high expectations, most of the results indicate that successfully using NLP is very complex. In this paper, we show how Support Vector Machines along with kernel func ..."
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Cited by 18 (9 self)
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Natural Language Processing (NLP) for Information Retrieval has always been an interesting and challenging research area. Despite the high expectations, most of the results indicate that successfully using NLP is very complex. In this paper, we show how Support Vector Machines along with kernel functions can effectively represent syntax and semantics. Our experiments on question/answer classification show that the above models highly improve on bag-of-words on a TREC dataset. 1
Coreference Systems based on Kernels Methods
"... Various types of structural information-e.g., about the type of constructions in which binding constraints apply, or about the structure of names- play a central role in coreference resolution, often in combination with lexical information (as in expletive detection). Kernel functions appear to be a ..."
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Cited by 15 (4 self)
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Various types of structural information-e.g., about the type of constructions in which binding constraints apply, or about the structure of names- play a central role in coreference resolution, often in combination with lexical information (as in expletive detection). Kernel functions appear to be a promising candidate to capture structure-sensitive similarities and complex feature combinations, but care is required to ensure they are exploited in the best possible fashion. In this paper we propose kernel functions for three subtasks of coreference resolution- binding constraint detection, expletive identification, and aliasing- together with an architecture to integrate them within the standard framework for coreference resolution. 1
