La polysémie des verbes de perception visuelle (Sémantiques) (French Edition)

The key idea is that compositionality is modeled as a multi-way interaction between latent factors, which are automatically constructed from corpus data. The method consists of two steps.

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First, we compute a latent factor model for nouns from standard co-occurrence data. Next, the latent factors are used to induce a latent model of three-way subject verb object interactions. By treating language data as multi-way co-occurrence frequencies, both methods are able to properly model the tasks at hand in an entirely unsupervised way. Nos travaux portent sur deux grands axes: Cette ressource, WoNeF, est disponible en trois versions: In this talk we will present existing approaches coupling Argumentation Theory and Natural Language Processing, and then we will present our contributions in that area, highlighting the remaining open challenges.

In order to cut in on a debate on the web, the participants need first to evaluate the opinions of the other users to detect whether they are in favor or against the debated issue.

Site web Alpage : seminaire

Bipolar argumentation proposes algorithms and semantics to evaluate the set of accepted arguments, given the support and the attack relations among them. Two main problems arise.

Our talk addresses this open issue by proposing and evaluating the use of natural language techniques to identify the arguments and their relations. In particular, we adopt the textual entailment approach, a generic framework for applied semantics, where linguistic objects are mapped by means of semantic inferences at a textual level. Textual entailment is then coupled together with an abstract bipolar argumentation system which allows to identify the arguments that are accepted in the considered online debate.

Second, we address the problem of studying and comparing the different proposals put forward for modeling the support relation. The emerging scenario shows that there is not a unique interpretation of the support relation. In particular, different combinations of additional attacks among the arguments involved in a support relation are proposed. We provide a natural language account of the notion of support based on online debates, by discussing and evaluating the support relation among arguments with respect to the more specific notion of textual entailment in the natural language processing field.

This talk will introduce a new line of research currently investigated at Alpage, namely Direct Semantic Parsing. With most state-of-the-art statistical parsers routinely crossing a ninety percent performance plateau in capturing phrase structures or even higher when it comes to dependency-based parsing, the question of what next crucially arises.

Indeed, it has long been thought that the bottleneck preventing the advent of accurate syntax-to-semantic interfaces, lied in the quality of the previous phase of analysis: The truth is that most of the structures on which are trained the current parsing models are degraded versions of more informative data set: Those allows for a more straightforward parsing of graph-based predicate-argument structure.

In this talk, we will introduce this bubbling line of research and will present our work on this matter, showing that accurate models can be built around these treebanks, at the not so-sad cost of adding a little bit of surface syntax. We will also present the current state of our work on French. We show how its annotation scheme could be ported to the French Treebank and how we can induce translation rules between the surface syntactic layer and the deep one, leading to the first steps of a data driven SYNTAX to semantic interface. That is, if we understand a sentence, we infer what other facts are likely to be true in any situation described by that sentence.

These inferences are an integral part of language understanding, but they require a great deal of commonsense world knowledge. In this talk, I will consider two tasks that require systems to draw similar inferences automatically. First, I will describe our work on developing systems and data sets to associate images with sentences that describe what is depicted in them.

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I will show that systems that rely on visual and linguistic features that can be obtained with minimal supervision perform surprisingly well at describing new images. I will also define a ranking-based framework to evaluate such systems. In the second part of this talk, I will describe how we can combine ideas from distributional lexical semantics and denotational formal semantics to define novel measures of semantic similarity. We define the 'visual denotation' of linguistic expressions as the set of images they describe, and use our data set of 30K images and K descriptive captions to construct a 'denotation graph', i.

This allows us to compute denotational similarities, which we show to yield state-of-the-art performance on tasks that require semantic inference. Data, Models and Evaluation Metrics", Volume 47, pages http: From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions. The notion of event has long been central for both modeling the semantics of natural language as well as reasoning in goal-driven tasks in artificial intelligence. This talk examines developments in computational models for events, bringing together recent work from the areas of semantics, logic, computer science, and computational linguistics.

The goal is to look at event structure from a unifying perspective, enabled by a new synthesis of how these disciplines have approached the problem. This entails examining the structure of events at all levels impacted by linguistic expressions: This talk outlines a unified theory of event structure.

The demands on such a theory require it to both facilitate the systematic mapping from semantic forms to syntactic representations and support event-based inferences in texts. What emerges is a framework that represents a situation and its participants in terms of subevents, modeled dynamically through time and space. In addition, the theory must identify events as part of larger scenarios and scripts.

The course covers recent work in this direction and models unifiying these representational levels for event-based reasoning. Common to all traditions is the view that events are the means by which we model situations and changes in our world.

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We first examine the subatomic structure of events from the perspective of hybrid modal logic, using dynamic and linear temporal logics as our means of encoding change. Then, we look at the properties of atomic event structure, and the effects of discourse relations on temporal inferencing. Next, we examine the problem of identifying where events happens, which is critical for any deep causal reasoning involving events and their participants. We will develop a procedure for "event localization", which is the process of identifying the spatial extent of an event, activity, or situation.

Finally, we examine events above the level of the sentence and local discourse. That is, we study how events are structured within larger narratives and scripts, reflecting conventionalized patterns of behavior and causal and coherence relations within texts and discourse. Machine reading is a mild version of natural language understanding, aiming at extracting as many elements as possible from a text, possibly on a large scale, and with performance appropriate to industrial applications. In this presentation, we will present the outcoumes on the first shared task on statistical parsing of morphologically rich languages.

The task features data sets from nine languages Arabic, Basque, French, German, Hebrew, Hungarian, Korean, Polish and Swedish , each available both in constituency and dependency annotation. We report on the preparation of the data sets, on the proposed parsing scenarios, and on the evaluation metrics for parsing MRLs given different representation types. We present and analyze parsing results obtained by the task participants, and then provide an analysis and comparison of the parsers across languages and frameworks, reported for gold input as well as more realistic parsing scenarios.

The shared task saw submissions from seven teams, and results produced by more than 14 different systems. The parsing results were obtained in different input scenarios gold, predicted, and raw and evaluated using different protocols cross-framework, cross-scenario, and cross-language. In particular, this is the first time a multilingual evaluation campaign reports on the execution of parsers in realistic, morphologically ambiguous, settings. Ce travail est en cours.

Context-free grammars have been a cornerstone of theoretical computer science and computational linguistics since their inception over half a century ago. Topic models are a newer development in machine learning that play an important role in document analysis and information retrieval. It turns out there is a surprising connection between the two that suggests novel ways of extending both grammars and topic models.

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After explaining this connection, I go on to describe extensions which identify topical multiword collocations and automatically learn the internal structure of named-entity phrases. These new models have applications in text data mining and information retrieval. In this talk I will discuss the ongoing development of the Irish dependency treebank. Irish is an Indo-European language and shares linguistic features with other Celtic languages such as rich morphology and VSO word order.

Irish is a minority language of the EU. It is regarded as a low-density language in terms of NLP resources. My PhD research involves the development of the first treebank and statistical parser for Irish. I will discuss details such as the significance of Inter-annotator Agreement, preliminary parsing experiments and the analysis of a bootstrapping approach to the treebank development. Greedy transition-based dependency parsers are among the fastest available systems for natural language parsing but tend to suffer from search errors and subsequent error propagation.

This problem is aggravated by the fact that they are normally trained using oracles that are deterministic and incomplete in the sense that they assume a unique canonical derivation and are only valid as long as the parser does not stray from this path. In this talk, I will introduce oracles that are nondeterministic and complete in the sense that they allow different derivations of the same parse tree and make optimal predictions for all parser states, including states from which the gold parse is no longer reachable. Experimental evaluation on a wide range of data sets shows that using these oracles to train greedy parsers gives substantial improvements in accuracy because of reduced error propagation.

Moreover, this improvement comes at no cost in terms of efficiency, unlike other techniques like beam search. La reconnaissance des expressions polylexicales est cruciale pour le Traitement Automatique des Langues. In this talk, I will give an overview of FrameNet's method using the example of emotion-related words and then present recent ideas for extending FrameNet's representation for the purposes of sentiment analysis.

In the discussion of emotion vocabulary, I will address several points. First, comparing different releases, we ask how have FrameNet's criteria for distinguishing frames evolved over time? For instance, the organization of emotion frames in the current FrameNet release 1. The overall trajectory is towards an organization where frame divisions are becoming finer and lexical units in a given frame are more semantically similar to each other. A second key point is that even given the more fine-grained analysis, FrameNet's division of frames and lexical units does not follow any one psychological theory of emotion.

In the final part of the talk I suggest ways to extend FrameNet's representation for the purposes of sentiment analysis, producing a resource that allows for deep analysis of not only emotional vocabulary but evaluative and subjective language in general. A key feature of this approach is to piggy-back the extraction of opinion holders and targets onto automatic semantic role labeling systems.

Current approaches to recognizing discourse relations rely on a combination of shallow, surface- based features e. As a way to avoid both the shallowness of word-based representations and the lack of coverage of specialized linguistic features, we use a graph-based representation of discourse segments, which allows for a more abstract and hence generalizable notion of syntactic and partially of semantic structure. Empirical evaluation on a hand-annotated corpus of German discourse relations shows that our graph- based approach not only provides a suitable representation for the linguistic factors that are needed in disambiguating discourse relations, but also improves results over a strong state-of-the-art baseline.

By the licentiate Alonzo Fernandez de Avellaneda. Translated by Captain John Stevens.

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