ITGETARUNS A Linguistic Rule-Based System for Pragmatic Text Processing

We present results obtained by our system ITGetaruns for all tasks. It is a linguistic rule-based system in its bottom-up version that computes a complete parser of the input text. On top of that it produces semantics at different levels which is then used by the algorithm for sentiment and polarity detection. Our results are not remarkable apart from the ones related to Irony detection, where we ranked fourth over eight participants. The results were characterized by our intention to favour Recall over Precision and this is also testified by Recall values for Polarity which in one case rank highest of all

VENSESEVAL at SemEval-2016 task 2: iSTS - With a full-fledged rule-based approach

In our paper we present our rule-based system for semantic processing. In particular we show examples and solutions that may be challenge our approach. We then discuss problems and shortcomings of Task 2 - iSTS. We comment on the existence of a tension between the inherent need to on the one side, to make the task as much as possible "semantically feasible". Whereas the detailed presentation and some notes in the guidelines refer to inferential processes, paraphrases and the use of commonsense knowledge of the world for the interpretation to work. We then present results and some conclusions

Opinion and Factivity Analysis of Italian political discourse

The success of a newspaper article for the public opinion can be measured by the degree in which the journalist is able to report and modify (if needed) attitudes, opinions, feelings and political beliefs. We present a symbolic system for Italian, derived from GETARUNS, which integrates a range of natural language processing tools with the intent to characterise the print press discourse from a semantic and pragmatic point of view. This has been done on some 500K words of text, extracted from three Italian newspapers in order to characterize their stance on a deep political crisis situation. We tried two different approaches: a lexicon-based approach for semantic polarity using off-the-shelf dictionaries with the addition of manually supervised domain related concepts; another one is a feature-based semantic and pragmatic approach, which computes propositional level analysis with the intent to better characterize important component like factuality and subjectivity. Results are quite revealing and confirm the otherwise common knowledge about the political stance of each newspaper on such topic as the change of government that took place at the end of last year, 2011

Opinion and Sentiment Analysis of Italian print press

As it is known, the success of a newspaper article for the public opinion can be measured by the degree in which the journalist is able to report and modify (if needed) attitudes, opinions, feelings and political beliefs. We present a symbolic system for Italian, derived from GETARUNS, which integrates a range of natural language processing tools with the intent to characterise the print press discourse. The system is multilingual and can produce deep text understanding. This has been done on some 500K words of text, extracted from three Italian newspaper in order to characterize their stance on a deep political crisis situation. We tried two different approaches: a lexicon-based approach for semantic polarity using off-the-shelf dictionaries with the addition of manually supervised domain related concepts; another one is a feature-based semantic and pragmatic approach, which computes propositional level analysis with the intent to better characterize important component like factuality and subjectivity. Results are quite revealing and confirm the otherwise common knowledge about the political stance of each newspaper on such topic as the change of government, that took placeatthe end of lastyear,2011

La Linguistica Computazionale a Venezia

Questo saggio ha come argomento lo sviluppo della Linguistica Computazionale a Venezia con l’intenzione di mettere in luce le interrelazioni con gli altri componenti del Dipartimento di Linguistica, quelli di glottodidattica e quellidella linguistica teorica con le quali ha interagito nel tempo. Lo sviluppo temporale permette anche di legare gli eventi locali all'avanzamento della tecnologia e della scienza linguistica sperimentale in ambito internazionale

Can humain association norm evaluate latent semantic analysis?

This paper presents the comparison of word association norm created by a psycholinguistic experiment to association lists generated by algorithms operating on text corpora. We compare lists generated by Church and Hanks algorithm and lists generated by LSA algorithm. An argument is presented on how those automatically generated lists reflect real semantic relations

Recognizing Textual Entailment Using Description Logic And Semantic Relatedness

Textual entailment (TE) is a relation that holds between two pieces of text where one reading the first piece can conclude that the second is most likely true. Accurate approaches for textual entailment can be beneficial to various natural language processing (NLP) applications such as: question answering, information extraction, summarization, and even machine translation. For this reason, research on textual entailment has attracted a significant amount of attention in recent years. A robust logical-based meaning representation of text is very hard to build, therefore the majority of textual entailment approaches rely on syntactic methods or shallow semantic alternatives. In addition, approaches that do use a logical-based meaning representation, require a large knowledge base of axioms and inference rules that are rarely available. The goal of this thesis is to design an efficient description logic based approach for recognizing textual entailment that uses semantic relatedness information as an alternative to large knowledge base of axioms and inference rules. In this thesis, we propose a description logic and semantic relatedness approach to textual entailment, where the type of semantic relatedness axioms employed in aligning the description logic representations are used as indicators of textual entailment. In our approach, the text and the hypothesis are first represented in description logic. The representations are enriched with additional semantic knowledge acquired by using the web as a corpus. The hypothesis is then merged into the text representation by learning semantic relatedness axioms on demand and a reasoner is then used to reason over the aligned representation. Finally, the types of axioms employed by the reasoner are used to learn if the text entails the hypothesis or not. To validate our approach we have implemented an RTE system named AORTE, and evaluated its performance on recognizing textual entailment using the fourth recognizing textual entailment challenge. Our approach achieved an accuracy of 68.8 on the two way task and 61.6 on the three way task which ranked the approach as 2nd when compared to the other participating runs in the same challenge. These results show that our description logical based approach can effectively be used to recognize textual entailment

Semantic Processing for Text Entailment with VENSES

In this paper we present two new mechanisms we created in VENSES, the system for semantic evaluation of the University of Venice. The first mechanism is used to match predicate-argument structures with different governors, a verb and a noun, respectively in the Hypothesis and the Text. It can be defined Augmented Finite State Automata (FSA) which are matching procedures based on tagged words in one case, and dependency relations in another. In both cases, a number of inferences – the augmentation - is fired to match different words. The second mechanism is based on the output of our module for anaphora resolution. Our system produces antecedents for pronominal expressions and equal nominal expressions. On the contrary, no decision is taken for “bridging” expressions. So the “bridging” mechanism is activated by the Semantic Evaluator and has access to the History List and the semantic features associated to each referring expression. If constraint conditions meet, the system looks for a similar association of property/entity in web ontologies like Umbel, Yago and DBPedia. The two mechanisms have been proven to contribute a 5% and 3% accuracy, respectively

Semantic Processing for Text Entailment with VENSES

In this paper we present two new mechanisms we created in VENSES, the system for semantic evaluation of the University of Venice. The first mechanism is used to match predicate-argument structures with different governors, a verb and a noun, respectively in the Hypothesis and the Text. It can be defined Augmented Finite State Automata (FSA) which are matching procedures based on tagged words in one case, and dependency relations in another. In both cases, a number of inferences – the augmentation - is fired to match different words. The second mechanism is based on the output of our module for anaphora resolution. Our system produces antecedents for pronominal expressions and equal nominal expressions. On the contrary, no decision is taken for “bridging” expressions. So the “bridging” mechanism is activated by the Semantic Evaluator and has access to the History List and the semantic features associated to each referring expression. If constraint conditions meet, the system looks for a similar association of property/entity in web ontologies like Umbel, Yago and DBPedia. The two mechanisms have been proven to contribute a 5% and 3% accuracy, respectively