Concerned about the Turing test's ability to correctly evaluate if a system exhibits human-like intelligence, the Winograd Schema Challenge (WSC) has been proposed as an alternative. A Winograd Schema consists of a sentence and a question. The answers to the questions are intuitive for humans but are designed to be difficult for machines, as they require various forms of commonsense knowledge about the sentence. In this paper we demonstrate our progress towards addressing the WSC. We present an approach that identifies the knowledge needed to answer a challenge question, hunts down that knowledge from text repositories, and then reasons with them to come up with the answer. In the process we develop a semantic parser (www.kparser.org). We show that our approach works well with respect to a subset of Winograd schemas.

In this paper, we define the problem of coreference resolution in text as one of clustering with pairwise constraints where human experts are asked to provide pairwise constraints (pairwise judgments of coreferentiality) to guide the clustering process. Positing that these pairwise judgments are easy to obtain from humans given the right context, we show that with significantly lower number of pairwise judgments and feature-engineering effort, we can achieve competitive coreference performance. Further, we describe an active learning strategy that minimizes the overall number of such pairwise judgments needed by asking the most informative questions to human experts at each step of coreference resolution. We evaluate this hypothesis and our algorithms on both entity and event coreference tasks and on two languages.

We propose a graph-based method for extractive single-document summarization which considers importance, non-redundancy and local coherence simultaneously. We represent input documents by means of a bipartite graph consisting of sentence and entity nodes. We rank sentences on the basis of importance by applying a graph-based ranking algorithm to this graph and ensure non-redundancy and local coherence of the summary by means of an optimization step. Our graph based method is applied to scientific articles from the journal PLOS Medicine. We use human judgements to evaluate the coherence of our summaries. We compare ROUGE scores and human judgements for coherence of different systems on scientific articles. Our method performs considerably better than other systems on this data. Also, our graph-based summarization technique achieves state-of-the-art results on DUC 2002 data. Incorporating our local coherence measure always achieves the best results.

Most word embedding methods take a word as a basic unit and learn embeddings according to words' external contexts, ignoring the internal structures of words. However, in some languages such as Chinese, a word is usually composed of several  characters and contains rich internal information. The semantic meaning of a word is also related to the meanings of its composing characters. Hence, we take Chinese for example, and present a character-enhanced word embedding model (CWE). In order to address the issues of character ambiguity and non-compositional words, we propose multiple-prototype character embeddings and an effective word selection method. We evaluate the effectiveness of CWE on word relatedness computation and analogical reasoning. The results show that CWE outperforms other baseline methods which ignore internal character information.

We present a supervised framework for expanding an opinion lexicon for tweets. The lexicon contains part-of-speech (POS) disambiguated entries with a three-dimensional probability distribution for positive, negative, and neutral polarities. To obtain this distribution using machine learning, we propose word-level attributes based on POS tags and information calculated from streams of emoticon-annotated tweets. Our experimental results show that our method outperforms the three-dimensional word-level polarity classification performance obtained by semantic orientation, a state-of-the-art measure for establishing world-level sentiment.

Learning representations for semantic relations is important for various tasks such as analogy detection, relational search, and relation classification.Although there have been several proposals for learning representations for individual words,learning word representations that explicitly capture the semantic relations between words remains under developed.We propose an unsupervised method for learning vector representations for words such that the learnt representations are sensitive to the semantic relations that exist between two words.First, we extract lexical patterns from the co-occurrence contexts of two words in a corpus to represent the semantic relations that exist between those two words.Second, we represent a lexical pattern as the weighted sum of the representations of the words that co-occur with that lexical pattern. Third, we train a binary classifier to detect relationally similar versus non-similar lexical pattern pairs.The proposed method is unsupervised in the sense that the lexical pattern pairs we use as train data are automatically sampled from a corpus, without requiring any manual intervention.Our proposed method statistically significantly outperforms the current state-of-the-art word representations on three benchmark datasets for proportional analogy detection, demonstrating its ability to accurately capture the semantic relations among words.

During the last few years, the investigation of methodologies to automatically detect and characterise the figurative traits of textual contents has attracted a growing interest. Indeed, the capability to correctly deal with figurative language and more specifically with satire is fundamental to build robust approaches in several sub-fields of Artificial Intelligence including Sentiment Analysis and Affective Computing. In this paper we investigate the automatic detection of Tweets that advertise satirical news in English, Spanish and Italian. To this purpose we present a system that models Tweets from different languages by a set of language independent features that describe lexical, semantic and usage-related properties of the words of each Tweet. We approach the satire identification problem as binary classification of Tweets as satirical or not satirical messages. We test the performance of our system by performing experiments of both monolingual and cross-language classifications, evaluating the satire detection effectiveness of our features.Our system outperforms a word-based baseline and it is able to recognise if a news in Twitter is satirical or not with good accuracy. Moreover, we analyse the behaviour of the system across the different languages, obtaining interesting results.

We present a new model that describes the process of electing a group of representatives (e.g., a parliament) for a group of voters. In this model, called the voting committee model, the elected group of representatives runs a number of ballots to make final decisions regarding various issues. The satisfaction of voters comes from the final decisions made by the elected committee. Our results suggest that depending on a single-winner election system used by the committee to make these final decisions, different multi-winner election rules are most suitable for electing the committee. Furthermore, we show that if we allow not only a committee, but also an election rule used to make final decisions, to depend on the voters' preferences, we can obtain an even better representation of the voters.

However, generation of MAS models that SFL is a well-suited technique for MASs. is both error-prone and time intense, as it exponentially Literature has shown that there is no standard similarity increases with the number of agents coefficient that yields the best result for SFL [Yoo et al., 2014; and their interactions. In this paper, we propose Hofer et al., 2015; Le et al., 2013]. Empirical evaluation is a lightweight, automatic debugging-based technique, therefore essential to establish which set of heuristics excels coined ESFL-MAS, which shortens the diagnostic for the specific context to which SFL is being applied. To the process, while only relying on minimal best of our knowledge, SFL has not as yet been applied to information about the system. ESFL-MAS uses a diagnose behavioural faults in MASs; there is hence the need heuristic that quantifies the suspiciousness of an to empirically evaluate different formulae using known faults agent to be faulty; therefore, different heuristics to compare the performance yielded by several coefficients.

We study resource allocation in a model due to Brams and King [2005] and further developed by Baumeister et al. [2014]. Resource allocation deals with the distribution of resources to agents. We assume resources to be indivisible, nonshareable, and of single-unit type. Agents have ordinal preferences over single resources. Using scoring vectors, every ordinal preference induces a utility function. These utility functions are used in conjunction with utilitarian social welfare to assess the quality of allocations of resources to agents. Then allocation correspondences determine the optimal allocations that maximize utilitarian social welfare. Since agents may have an incentive to misreport their true preferences, the question of strategy-proofness is important to resource allocation. We assume that a manipulator has a strictly monotonic and strictly separable linear order on the power set of the resources. We use extension principles (from social choice theory, such as the Kelly and the Gärdenfors extension) for preferences to study manipulation of allocation correspondences. We characterize strategy-proofness of the utilitarian allocation correspondence: It is Gärdenfors/Kelly-strategy-proof if and only if the number of different values in the scoring vector is at most two or the number of occurrences of the greatest value in the scoring vector is larger than half the number of goods.