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Joint Distant and Direct Supervision for Relation Extraction
"... Supervised approaches to Relation Extraction (RE) are characterized by higher accuracy than unsupervised models. Unfortunately, their applicability is limited by the need of training data for each relation type. Automatic creation of such data using Distant Supervision (DS) provides a promising solu ..."
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Supervised approaches to Relation Extraction (RE) are characterized by higher accuracy than unsupervised models. Unfortunately, their applicability is limited by the need of training data for each relation type. Automatic creation of such data using Distant Supervision (DS) provides a promising solution to the problem. In this paper, we study DS for designing endto-end systems of sentence-level RE. In particular, we propose a joint model between Web data derived with DS and manually annotated data from ACE. The results show (i) an improvement on the previous state-of-the-art in ACE, which provides important evidence of the benefit of DS; and (ii) a rather good accuracy on extracting 52 types of relations from Web data, which suggests the applicability of DS for general RE. 1
Kernel-Based Machines for Abstract and Easy Modeling of Automatic Learning
"... Abstract. The modeling of system semantics (in several ICT domains) by means of pattern analysis or relational learning is a product of latest results in statistical learning theory. For example, the modeling of natural language semantics ex-pressed by text, images, speech in information search (e.g ..."
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Abstract. The modeling of system semantics (in several ICT domains) by means of pattern analysis or relational learning is a product of latest results in statistical learning theory. For example, the modeling of natural language semantics ex-pressed by text, images, speech in information search (e.g. Google, Yahoo,..) or DNA sequence labeling in Bioinformatics represent distinguished cases of suc-cessful use of statistical machine learning. The reason of this success is due to the ability to overcome the concrete limitations of logic/rule-based approaches to semantic modeling: although, from a knowledge engineer perspective, rules are natural methods to encode system semantics, noise, ambiguity and errors affect-ing dynamic systems, prevent such approached from being effective, e.g. they are not flexible enough. In contrast, statistical relational learning, applied to representations of system states, i.e. training examples, can produce semantic models of system behavior based on a large number attributes. As the values of the latter are automatically learned, they reflect the flexibility of statistical settings and the overall model
Name-aware Machine Translation
"... We propose a Name-aware Machine Translation (MT) approach which can tightly integrate name processing into MT model, by jointly annotating parallel corpora, extracting name-aware translation grammar and rules, adding name phrase table and name translation driven decoding. Additionally, we also propo ..."
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We propose a Name-aware Machine Translation (MT) approach which can tightly integrate name processing into MT model, by jointly annotating parallel corpora, extracting name-aware translation grammar and rules, adding name phrase table and name translation driven decoding. Additionally, we also propose a new MT metric to appropriately evaluate the translation quality of informative words, by assigning different weights to different words according to their importance values in a document. Experiments on Chinese-English translation demonstrated the effectiveness of our approach on enhancing the quality of overall translation, name translation and word alignment over a high-quality MT baseline1. 1
Relational Structures and Models for Coreference Resolution
"... Coreference resolution is the task of identifying the sets of mentions referring to the same entity. Although modern machine learning approaches to coreference resolution exploit a variety of semantic information, the literature on the effect of relational information on coreference is still very li ..."
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Coreference resolution is the task of identifying the sets of mentions referring to the same entity. Although modern machine learning approaches to coreference resolution exploit a variety of semantic information, the literature on the effect of relational information on coreference is still very limited. In this paper, we discuss and compare two methods for incorporating relational information into a coreference resolver. One approach is to use a filtering algorithm to rerank the output of coreference hypotheses. The filter is based on the relational structures between mentions and their corresponding relationships. The second approach is to use a joint model enriched with a set of relational features derived from semantic relations of each mention. Both methods have shown to improve the performance of a learning-based state-of-the-art coreference resolver.
Deep Neural Networks for Named Entity Recognition in Italian
"... English. In this paper, we intro-duce a Deep Neural Network (DNN) for engineering Named Entity Recognizers (NERs) in Italian. Our network uses a sliding window of word contexts to pre-dict tags. It relies on a simple word-level log-likelihood as a cost function and uses a new recurrent feedback mech ..."
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English. In this paper, we intro-duce a Deep Neural Network (DNN) for engineering Named Entity Recognizers (NERs) in Italian. Our network uses a sliding window of word contexts to pre-dict tags. It relies on a simple word-level log-likelihood as a cost function and uses a new recurrent feedback mechanism to ensure that the dependencies between the output tags are properly modeled. The evaluation on the Evalita 2009 benchmark shows that our DNN performs on par with the best NERs, outperforming the state of the art when gazetteer features are used. Italiano. In questo lavoro, si introduce una rete neurale deep (DNN) per pro-gettare estrattori automatici di entita ́ nom-inate (NER) per la lingua italiana. La rete utilizza una finestra scorrevole di contesti delle parole per predire le loro etichette con associata probabilitá, la quale e ̀ us-ata come funzione di costo. Inoltre si uti-lizza un nuovo meccanismo di retroazione ricorrente per modellare le dipendenze tra le etichette di uscita. La valutazione della DNN sul dataset di Evalita 2009 indica che e ̀ alla pari con i migliori NER e migliora lo stato dell’arte quando si aggiungono delle features derivate dai dizionari. 1
IOS Press Structural Reranking Models for Named Entity Recognition
"... Abstract. We present a method for incorporating global features in named entity recognizers using reranking techniques and the combination of two state-of-the-art NER learning algorithms: conditional random fields (CRFs) and support vector machines (SVMs). The reranker employs two kinds of features: ..."
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Abstract. We present a method for incorporating global features in named entity recognizers using reranking techniques and the combination of two state-of-the-art NER learning algorithms: conditional random fields (CRFs) and support vector machines (SVMs). The reranker employs two kinds of features: flat and structured features. The former are generated by a polynomial kernel encoding entity features whereas tree kernels are used to model dependencies amongst tagged candidate examples. The experiments on two standard corpora in two languages, i.e. the Italian EVALITA 2009 and the English CoNLL 2003 datasets, show a large improvement on CRFs in F-measure, i.e., from 80.34 % to 84.33 % and from 84.86 % to 87.99%, respectively. Our analysis reveals that (i) both kernels provide a comparable improvement over the CRFs baseline; and (ii) their combination improves CRFs much more than the sum of the individual contributions, suggesting an interesting synergy.
