A Statistical Approach with Syntactic and Semantic Features for Chinese Textual Entailment

[[abstract]]Recognizing Textual Entailment (RTE) is a PASCAL/TAC task in which two text fragments are processed by system to determine whether the meaning of hypothesis is entailed from another text or not. In this paper, we proposed a textual entailment system using a statistical approach that integrates syntactic and semantic techniques for Recognizing Inference in Text (RITE) using the NTCIR-9 RITE task and make a comparison between semantic and syntactic features based on their differences. We thoroughly evaluate our approach using subtasks of the NTCIR-9 RITE. As a result, our system achieved 73.28% accuracy on the Chinese Binary-Class (BC) subtask with NTCIR-9 RITE. Thorough experiments with the text fragments provided by the NTCIR-9 RITE task show that the proposed approach can significantly improve system accuracy.[[sponsorship]]IEEE[[incitationindex]]EI[[cooperationtype]]國外[[conferencetype]]國際[[conferencedate]]20120808~20120810[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Vegas, Nevada, US

IMTKU Textual Entailment System for Recognizing Inference in Text at NTCIR-11

[[abstract]]In this paper, we describe the IMTKU (Information Management at TamKang University) textual entailment system for recognizing inference in text at NTCIR-11 RITE-VAL (Recognizing Inference in Text). We proposed a textual entailment system using statistics approach that integrate semantic features and machine learning techniques for recognizing inference in text at NTCIR-11 RITEVAL task. We submitted 3 official runs for BC, MC subtask. In NTCIR-11 RITE-VAL task, IMTKU team achieved 0.2911 in the CT-MC subtask, 0.5275 in the CT-BC subtask; 0.2917 in the CSMC subtask, 0.5325 in the CS-BC subtask.[[incitationindex]]EI[[conferencetype]]國際[[conferencedate]]20141209~20141212[[booktype]]紙本[[iscallforpapers]]Y[[conferencelocation]]Tokyo, Japa

Chinese Textual Entailment with Wordnet Semantic and Dependency Syntactic Analysis

Chun Tu and Min-Yuh Day (2013), "Chinese Textual Entailment with Wordnet Semantic and Dependency Syntactic Analysis", 2013 IEEE International Workshop on Empirical Methods for Recognizing Inference in Text (IEEE EM-RITE 2013), August 14, 2013, in Proceedings of the IEEE International Conference on Information Reuse and Integration (IEEE IRI 2013), San Francisco, California, USA, August 14-16, 2013, pp. 69-74.[[abstract]]Recognizing Inference in TExt (RITE) is a task for automatically detecting entailment, paraphrase, and contradiction in texts which addressing major text understanding in information access research areas. In this paper, we proposed a Chinese textual entailment system using Wordnet semantic and dependency syntactic approaches in Recognizing Inference in Text (RITE) using the NTCIR-10 RITE-2 subtask datasets. Wordnet is used to recognize entailment at lexical level. Dependency syntactic approach is a tree edit distance algorithm applied on the dependency trees of both the text and the hypothesis. We thoroughly evaluate our approach using NTCIR-10 RITE-2 subtask datasets. As a result, our system achieved 73.28% on Traditional Chinese Binary-Class (BC) subtask and 74.57% on Simplified Chinese Binary-Class subtask with NTCIR-10 RITE-2 development datasets. Thorough experiments with the text fragments provided by the NTCIR-10 RITE-2 subtask showed that the proposed approach can improve system's overall accuracy.[[sponsorship]]IEEE[[incitationindex]]EI[[conferencetype]]國際[[conferencedate]]20130814~20130816[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]San Francisco, US

IMTKU Textual Entailment System for Recognizing Inference in Text at NTCIR-10 RITE-2

Min-Yuh Day, Chun Tu, Shih-Jhen Huang, Hou-Cheng Vong, Shih-Wei Wu (2013), "IMTKU Textual Entailment System for Recognizing Inference in Text at NTCIR-10 RITE-2," in Proceedings of the 10th NTCIR Conference on Evaluation of Information Access Technologies(NTCIR-10), Tokyo, Japan, June 18-21, 2013, pp. 462-468.[[abstract]]In this paper, we describe the IMTKU (Information Management at TamKang University) textual entailment system for recognizing inference in text at NTCIR-10 RITE-2 (Recognizing Inference in Text). We proposed a textual entailment system using a hybrid approach that integrate semantic features and machine learning techniques for recognizing inference in text at NTCIR-10 RITE-2 task. We submitted 3 official runs for BC, MC and RITE4QA subtask. In NTCIR-10 RITE-2 task, IMTKU team achieved 0.509 in the CT-MC subtask, 0.663 in the CT-BC subtask; 0.402 in the CS-MC subtask, 0.627 in the CS-BC subtask; In MRR index, 0.257 in the CT-RITE4QA subtask, 0.338 in the CS-RITE4QA subtask.[[sponsorship]]National Institute of Informatics (NII), Tokyo, Japan[[conferencetype]]國際[[conferencedate]]20130618~20130621[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Tokyo, Japa

A Novel and Robust Approach for Pro-Drop Language Translation

A significant challenge for machine translation (MT) is the phenomena of dropped pronouns (DPs), where certain classes of pronouns are frequently dropped in the source language but should be retained in the target language. In response to this common problem, we propose a semi-supervised approach with a universal framework to recall missing pronouns in translation. Firstly, we build training data for DP generation in which the DPs are automatically labelled according to the alignment information from a parallel corpus. Secondly, we build a deep learning-based DP generator for input sentences in decoding when no corresponding references exist. More specifically, the generation has two phases: (1) DP position detection, which is modeled as a sequential labelling task with recurrent neural networks; and (2) DP prediction, which employs a multilayer perceptron with rich features. Finally, we integrate the above outputs into our statistical MT (SMT) system to recall missing pronouns by both extracting rules from the DP-labelled training data and translating the DP-generated input sentences. To validate the robustness of our approach, we investigate our approach on both Chinese–English and Japanese–English corpora extracted from movie subtitles. Compared with an SMT baseline system, experimental results show that our approach achieves a significant improvement of++1.58 BLEU points in translation performance with 66% F-score for DP generation accuracy for Chinese–English, and nearly++1 BLEU point with 58% F-score for Japanese–English. We believe that this work could help both MT researchers and industries to boost the performance of MT systems between pro-drop and non-pro-drop languages

Approaches to implement and evaluate aggregated search

La recherche d'information agrégée peut être vue comme un troisième paradigme de recherche d'information après la recherche d'information ordonnée (ranked retrieval) et la recherche d'information booléenne (boolean retrieval). Les deux paradigmes les plus explorés jusqu'à aujourd'hui retournent un ensemble ou une liste ordonnée de résultats. C'est à l'usager de parcourir ces ensembles/listes et d'en extraire l'information nécessaire qui peut se retrouver dans plusieurs documents. De manière alternative, la recherche d'information agrégée ne s'intéresse pas seulement à l'identification des granules (nuggets) d'information pertinents, mais aussi à l'assemblage d'une réponse agrégée contenant plusieurs éléments. Dans nos travaux, nous analysons les travaux liés à la recherche d'information agrégée selon un schéma général qui comprend 3 parties: dispatching de la requête, recherche de granules d'information et agrégation du résultat. Les approches existantes sont groupées autours de plusieurs perspectives générales telle que la recherche relationnelle, la recherche fédérée, la génération automatique de texte, etc. Ensuite, nous nous sommes focalisés sur deux pistes de recherche selon nous les plus prometteuses: (i) la recherche agrégée relationnelle et (ii) la recherche agrégée inter-verticale. * La recherche agrégée relationnelle s'intéresse aux relations entre les granules d'information pertinents qui servent à assembler la réponse agrégée. En particulier, nous nous sommes intéressés à trois types de requêtes notamment: requête attribut (ex. président de la France, PIB de l'Italie, maire de Glasgow, ...), requête instance (ex. France, Italie, Glasgow, Nokia e72, ...) et requête classe (pays, ville française, portable Nokia, ...). Pour ces requêtes qu'on appelle requêtes relationnelles nous avons proposés trois approches pour permettre la recherche de relations et l'assemblage des résultats. Nous avons d'abord mis l'accent sur la recherche d'attributs qui peut aider à répondre aux trois types de requêtes. Nous proposons une approche à large échelle capable de répondre à des nombreuses requêtes indépendamment de la classe d'appartenance. Cette approche permet l'extraction des attributs à partir des tables HTML en tenant compte de la qualité des tables et de la pertinence des attributs. Les différentes évaluations de performances effectuées prouvent son efficacité qui dépasse les méthodes de l'état de l'art. Deuxièmement, nous avons traité l'agrégation des résultats composés d'instances et d'attributs. Ce problème est intéressant pour répondre à des requêtes de type classe avec une table contenant des instances (lignes) et des attributs (colonnes). Pour garantir la qualité du résultat, nous proposons des pondérations sur les instances et les attributs promouvant ainsi les plus représentatifs. Le troisième problème traité concerne les instances de la même classe (ex. France, Italie, Allemagne, ...). Nous proposons une approche capable d'identifier massivement ces instances en exploitant les listes HTML. Toutes les approches proposées fonctionnent à l'échelle Web et sont importantes et complémentaires pour la recherche agrégée relationnelle. Enfin, nous proposons 4 prototypes d'application de recherche agrégée relationnelle. Ces derniers peuvent répondre des types de requêtes différents avec des résultats relationnels. Plus précisément, ils recherchent et assemblent des attributs, des instances, mais aussi des passages et des images dans des résultats agrégés. Un exemple est la requête ``Nokia e72" dont la réponse sera composée d'attributs (ex. prix, poids, autonomie batterie, ...), de passages (ex. description, reviews, ...) et d'images. Les résultats sont encourageants et illustrent l'utilité de la recherche agrégée relationnelle. * La recherche agrégée inter-verticale s'appuie sur plusieurs moteurs de recherche dits verticaux tel que la recherche d'image, recherche vidéo, recherche Web traditionnelle, etc. Son but principal est d'assembler des résultats provenant de toutes ces sources dans une même interface pour répondre aux besoins des utilisateurs. Les moteurs de recherche majeurs et la communauté scientifique nous offrent déjà une série d'approches. Notre contribution consiste en une étude sur l'évaluation et les avantages de ce paradigme. Plus précisément, nous comparons 4 types d'études qui simulent des situations de recherche sur un total de 100 requêtes et 9 sources différentes. Avec cette étude, nous avons identifiés clairement des avantages de la recherche agrégée inter-verticale et nous avons pu déduire de nombreux enjeux sur son évaluation. En particulier, l'évaluation traditionnelle utilisée en RI, certes la moins rapide, reste la plus réaliste. Pour conclure, nous avons proposé des différents approches et études sur deux pistes prometteuses de recherche dans le cadre de la recherche d'information agrégée. D'une côté, nous avons traité trois problèmes importants de la recherche agrégée relationnelle qui ont porté à la construction de 4 prototypes d'application avec des résultats encourageants. De l'autre côté, nous avons mis en place 4 études sur l'intérêt et l'évaluation de la recherche agrégée inter-verticale qui ont permis d'identifier les enjeux d'évaluation et les avantages du paradigme. Comme suite à long terme de ce travail, nous pouvons envisager une recherche d'information qui intègre plus de granules relationnels et plus de multimédia.Aggregated search or aggregated retrieval can be seen as a third paradigm for information retrieval following the Boolean retrieval paradigm and the ranked retrieval paradigm. In the first two, we are returned respectively sets and ranked lists of search results. It is up to the time-poor user to scroll this set/list, scan within different documents and assemble his/her information need. Alternatively, aggregated search not only aims the identification of relevant information nuggets, but also the assembly of these nuggets into a coherent answer. In this work, we present at first an analysis of related work to aggregated search which is analyzed with a general framework composed of three steps: query dispatching, nugget retrieval and result aggregation. Existing work is listed aside different related domains such as relational search, federated search, question answering, natural language generation, etc. Within the possible research directions, we have then focused on two directions we believe promise the most namely: relational aggregated search and cross-vertical aggregated search. * Relational aggregated search targets relevant information, but also relations between relevant information nuggets which are to be used to assemble reasonably the final answer. In particular, there are three types of queries which would easily benefit from this paradigm: attribute queries (e.g. president of France, GDP of Italy, major of Glasgow, ...), instance queries (e.g. France, Italy, Glasgow, Nokia e72, ...) and class queries (countries, French cities, Nokia mobile phones, ...). We call these queries as relational queries and we tackle with three important problems concerning the information retrieval and aggregation for these types of queries. First, we propose an attribute retrieval approach after arguing that attribute retrieval is one of the crucial problems to be solved. Our approach relies on the HTML tables in the Web. It is capable to identify useful and relevant tables which are used to extract relevant attributes for whatever queries. The different experimental results show that our approach is effective, it can answer many queries with high coverage and it outperforms state of the art techniques. Second, we deal with result aggregation where we are given relevant instances and attributes for a given query. The problem is particularly interesting for class queries where the final answer will be a table with many instances and attributes. To guarantee the quality of the aggregated result, we propose the use of different weights on instances and attributes to promote the most representative and important ones. The third problem we deal with concerns instances of the same class (e.g. France, Germany, Italy ... are all instances of the same class). Here, we propose an approach that can massively extract instances of the same class from HTML lists in the Web. All proposed approaches are applicable at Web-scale and they can play an important role for relational aggregated search. Finally, we propose 4 different prototype applications for relational aggregated search. They can answer different types of queries with relevant and relational information. Precisely, we not only retrieve attributes and their values, but also passages and images which are assembled into a final focused answer. An example is the query ``Nokia e72" which will be answered with attributes (e.g. price, weight, battery life ...), passages (e.g. description, reviews ...) and images. Results are encouraging and they illustrate the utility of relational aggregated search. * The second research direction that we pursued concerns cross-vertical aggregated search, which consists of assembling results from different vertical search engines (e.g. image search, video search, traditional Web search, ...) into one single interface. Here, different approaches exist in both research and industry. Our contribution concerns mostly evaluation and the interest (advantages) of this paradigm. We propose 4 different studies which simulate different search situations. Each study is tested with 100 different queries and 9 vertical sources. Here, we could clearly identify new advantages of this paradigm and we could identify different issues with evaluation setups. In particular, we observe that traditional information retrieval evaluation is not the fastest but it remains the most realistic. To conclude, we propose different studies with respect to two promising research directions. On one hand, we deal with three important problems of relational aggregated search following with real prototype applications with encouraging results. On the other hand, we have investigated on the interest and evaluation of cross-vertical aggregated search. Here, we could clearly identify some of the advantages and evaluation issues. In a long term perspective, we foresee a possible combination of these two kinds of approaches to provide relational and cross-vertical information retrieval incorporating more focus, structure and multimedia in search results

Cross-Lingual Textual Entailment and Applications

Textual Entailment (TE) has been proposed as a generic framework for modeling language variability. The great potential of integrating (monolingual) TE recognition components into NLP architectures has been reported in several areas, such as question answering, information retrieval, information extraction and document summarization. Mainly due to the absence of cross-lingual TE (CLTE) recognition components, similar improvements have not yet been achieved in any corresponding cross-lingual application. In this thesis, we propose and investigate Cross-Lingual Textual Entailment (CLTE) as a semantic relation between two text portions in dierent languages. We present dierent practical solutions to approach this problem by i) bringing CLTE back to the monolingual scenario, translating the two texts into the same language; and ii) integrating machine translation and TE algorithms and techniques. We argue that CLTE can be a core tech- nology for several cross-lingual NLP applications and tasks. Experiments on dierent datasets and two interesting cross-lingual NLP applications, namely content synchronization and machine translation evaluation, conrm the eectiveness of our approaches leading to successful results. As a complement to the research in the algorithmic side, we successfully explored the creation of cross-lingual textual entailment corpora by means of crowdsourcing, as a cheap and replicable data collection methodology that minimizes the manual work done by expert annotators

A Probabilistic Approach for Chinese Information Retrieval: Theory, Analysis and Experiments

Using probabilistic methods to retrieve information has always been a challenging task in the area of information retrieval. A key issue in probabilistic retrieval methods is the design of query term weighting functions. In this thesis, we provide a comprehensive description of the probabilistic retrieval model and propose several new weighting functions, which include both single unit weighting and compound unit weighting functions. Detailed analysis and evaluation of these new weighting functions are also provided. This thesis provides a large number of empirical results for comparing different weighting methods in Chinese word-based and character-based retrieval systems. The results show that (1) compound unit weighting is useful for improving the system performance; (2) a newly designed single unit weighting function, BM26, contributes to the improvement of Chinese information retrieval; (3) the character based system outperforms the word-based system in terms of average precision. The thesis makes three original contributions to modern information retrieval. First, it demonstrates that probabilistic compound unit weighting is useful for Chi nese information retrieval systems. Second, it proposes a new probabilistic single unit weighting function, BM26, that considers document lengths when assigning weights to documents, and it demonstrates that the new function outperforms the function that it evolved from. Third, this thesis reports the results of large scale experiments that compare Chinese word-based and character-based retrieval systems. In summary, the thesis combines a comprehensive description of the probabilis tic model of retrieval with some new designs of probabilistic weighting formulae and new systematic experiments on the Chinese TREC Programme material. The experiments demonstrate, for a large test collection, that the probabilistic model is effective and robust for Chinese text retrieval, and that it responses appropriately, with major improvements in performance, to key features of retrieval situations in Chinese text retrieval