Connotation Frames: A Data-Driven Investigation

Through a particular choice of a predicate (e.g., "x violated y"), a writer can subtly connote a range of implied sentiments and presupposed facts about the entities x and y: (1) writer's perspective: projecting x as an "antagonist"and y as a "victim", (2) entities' perspective: y probably dislikes x, (3) effect: something bad happened to y, (4) value: y is something valuable, and (5) mental state: y is distressed by the event. We introduce connotation frames as a representation formalism to organize these rich dimensions of connotation using typed relations. First, we investigate the feasibility of obtaining connotative labels through crowdsourcing experiments. We then present models for predicting the connotation frames of verb predicates based on their distributional word representations and the interplay between different types of connotative relations. Empirical results confirm that connotation frames can be induced from various data sources that reflect how people use language and give rise to the connotative meanings. We conclude with analytical results that show the potential use of connotation frames for analyzing subtle biases in online news media.Comment: 11 pages, published in Proceedings of ACL 201

Information extraction of +/-effect events to support opinion inference

Recently, work in NLP was initiated on a type of opinion inference that arises when opinions are expressed toward events which have positive or negative effects on entities, called +/-effect events. The ultimate goal is to develop a fully automatic system capable of recognizing inferred attitudes. To achieve its results, the inference system requires all instances of +/-effect events. Therefore, this dissertation focuses on +/-effect events to support opinion inference. To extract +/-effect events, we first need the list of +/-effect events. Due to significant sense ambiguity, our goal is to develop a sense-level rather than word-level lexicon. To handle sense-level information, WordNet is adopted. We adopt a graph-based method which is seeded by entries culled from FrameNet and then expanded by exploiting semantic relations in WordNet. We show that WordNet relations are useful for the polarity propagation in the graph model. In addition, to maximize the effectiveness of different types of information, we combine a graph-based method using WordNet relations and a standard classifier using gloss information. Further, we provide evidence that the model is an effective way to guide manual annotation to find +/-effect senses that are not in the seed set. To exploit the sense-level lexicons, we have to carry out word sense disambiguation. We present a knowledge-based +/-effect coarse-grained word sense disambiguation method based on selectional preferences via topic models. For more information, we first group senses, and then utilize topic models to model selectional preferences. Our experiments show that selectional preferences are helpful in our work. To support opinion inferences, we need to identify not only +/-effect events but also their affected entities automatically. Thus, we address both +/-effect event detection and affected entity identification. Since +/-effect events and their affected entities are closely related, instead of a pipeline system, we present a joint model to extract +/-effect events and their affected entities simultaneously. We demonstrate that our joint model is promising to extract +/-effect events and their affected entities jointly

Deep Learning With Sentiment Inference For Discourse-Oriented Opinion Analysis

Opinions are omnipresent in written and spoken text ranging from editorials, reviews, blogs, guides, and informal conversations to written and broadcast news. However, past research in NLP has mainly addressed explicit opinion expressions, ignoring implicit opinions. As a result, research in opinion analysis has plateaued at a somewhat superficial level, providing methods that only recognize what is explicitly said and do not understand what is implied. In this dissertation, we develop machine learning models for two tasks that presumably support propagation of sentiment in discourse, beyond one sentence. The first task we address is opinion role labeling, i.e.\ the task of detecting who expressed a given attitude toward what or who. The second task is abstract anaphora resolution, i.e.\ the task of finding a (typically) non-nominal antecedent of pronouns and noun phrases that refer to abstract objects like facts, events, actions, or situations in the preceding discourse. We propose a neural model for labeling of opinion holders and targets and circumvent the problems that arise from the limited labeled data. In particular, we extend the baseline model with different multi-task learning frameworks. We obtain clear performance improvements using semantic role labeling as the auxiliary task. We conduct a thorough analysis to demonstrate how multi-task learning helps, what has been solved for the task, and what is next. We show that future developments should improve the ability of the models to capture long-range dependencies and consider other auxiliary tasks such as dependency parsing or recognizing textual entailment. We emphasize that future improvements can be measured more reliably if opinion expressions with missing roles are curated and if the evaluation considers all mentions in opinion role coreference chains as well as discontinuous roles. To the best of our knowledge, we propose the first abstract anaphora resolution model that handles the unrestricted phenomenon in a realistic setting. We cast abstract anaphora resolution as the task of learning attributes of the relation that holds between the sentence with the abstract anaphor and its antecedent. We propose a Mention-Ranking siamese-LSTM model (MR-LSTM) for learning what characterizes the mentioned relation in a data-driven fashion. The current resources for abstract anaphora resolution are quite limited. However, we can train our models without conventional data for abstract anaphora resolution. In particular, we can train our models on many instances of antecedent-anaphoric sentence pairs. Such pairs can be automatically extracted from parsed corpora by searching for a common construction which consists of a verb with an embedded sentence (complement or adverbial), applying a simple transformation that replaces the embedded sentence with an abstract anaphor, and using the cut-off embedded sentence as the antecedent. We refer to the extracted data as silver data. We evaluate our MR-LSTM models in a realistic task setup in which models need to rank embedded sentences and verb phrases from the sentence with the anaphor as well as a few preceding sentences. We report the first benchmark results on an abstract anaphora subset of the ARRAU corpus \citep{uryupina_et_al_2016} which presents a greater challenge due to a mixture of nominal and pronominal anaphors as well as a greater range of confounders. We also use two additional evaluation datasets: a subset of the CoNLL-12 shared task dataset \citep{pradhan_et_al_2012} and a subset of the ASN corpus \citep{kolhatkar_et_al_2013_crowdsourcing}. We show that our MR-LSTM models outperform the baselines in all evaluation datasets, except for events in the CoNLL-12 dataset. We conclude that training on the small-scale gold data works well if we encounter the same type of anaphors at the evaluation time. However, the gold training data contains only six shell nouns and events and thus resolution of anaphors in the ARRAU corpus that covers a variety of anaphor types benefits from the silver data. Our MR-LSTM models for resolution of abstract anaphors outperform the prior work for shell noun resolution \citep{kolhatkar_et_al_2013} in their restricted task setup. Finally, we try to get the best out of the gold and silver training data by mixing them. Moreover, we speculate that we could improve the training on a mixture if we: (i) handle artifacts in the silver data with adversarial training and (ii) use multi-task learning to enable our models to make ranking decisions dependent on the type of anaphor. These proposals give us mixed results and hence a robust mixed training strategy remains a challenge

Communication and content

Communication and content presents a comprehensive and foundational account of meaning based on new versions of situation theory and game theory. The literal and implied meanings of an utterance are derived from first principles assuming little more than the partial rationality of interacting agents. New analyses of a number of diverse phenomena – a wide notion of ambiguity and content encompassing phonetics, syntax, semantics, pragmatics, and beyond, vagueness, convention and conventional meaning, indeterminacy, universality, the role of truth in communication, semantic change, translation, Frege’s puzzle of informative identities – are developed. Communication, speaker meaning, and reference are defined. Frege’s context and compositional principles are generalized and reconciled in a fixed-point principle, and a detailed critique of Grice, several aspects of Lewis, and some aspects of the Romantic conception of meaning are offered

Communication and content

Communication and content presents a comprehensive and foundational account of meaning based on new versions of situation theory and game theory. The literal and implied meanings of an utterance are derived from first principles assuming little more than the partial rationality of interacting agents. New analyses of a number of diverse phenomena – a wide notion of ambiguity and content encompassing phonetics, syntax, semantics, pragmatics, and beyond, vagueness, convention and conventional meaning, indeterminacy, universality, the role of truth in communication, semantic change, translation, Frege’s puzzle of informative identities – are developed. Communication, speaker meaning, and reference are defined. Frege’s context and compositional principles are generalized and reconciled in a fixed-point principle, and a detailed critique of Grice, several aspects of Lewis, and some aspects of the Romantic conception of meaning are offered. Connections with other branches of linguistics, especially psycholinguistics, sociolinguistics, historical linguistics, and natural language processing, are explored. The book will be of interest to scholars in philosophy, linguistics, artificial intelligence, and cognitive science. It should also interest readers in related fields like literary and cultural theory and the social sciences

Communication and content

Communication and content presents a comprehensive and foundational account of meaning based on new versions of situation theory and game theory. The literal and implied meanings of an utterance are derived from first principles assuming little more than the partial rationality of interacting agents. New analyses of a number of diverse phenomena – a wide notion of ambiguity and content encompassing phonetics, syntax, semantics, pragmatics, and beyond, vagueness, convention and conventional meaning, indeterminacy, universality, the role of truth in communication, semantic change, translation, Frege’s puzzle of informative identities – are developed. Communication, speaker meaning, and reference are defined. Frege’s context and compositional principles are generalized and reconciled in a fixed-point principle, and a detailed critique of Grice, several aspects of Lewis, and some aspects of the Romantic conception of meaning are offered

Context Dependence and Procedural Meaning: The Semantics of Definites

This thesis argues that there is a theoretically interesting connection between members of the intuitive category of context-dependent expressions, including "we", "tall", "local", "every man", "the woman", "it", "those donkeys" and so on. A treatment of the linguistic meaning of these expressions will be proposed based on the idea that their use raises issues for the audience about the proper understanding of the utterances in which they occur. The proposal will be developed in terms of a semantics for questions, which draws on the idea that to know the meaning of a question is to know what would count as an answer. It can be summarised along similar lines: to know the meaning of a context-dependent expression is to know what properties or relations (of the appropriate type) it could be used to express. The framework in which this idea will be developed can account for why the expressions that are given this issue-based treatment can also be given dependent, bound readings. The class of definite expressions, including descriptions and pronouns, is analysed in detail. A quantificational approach, where the determiner is existential, is assumed for all forms of definiteness. In all cases, the restrictor is interpreted by an atomic definite concept. The audience's grasp of the properties which definite concepts express is the result of inferential processes which take the linguistic meaning of a definite expression as input. These processes are constrained by pragmatic principles. The analysis of context-dependent expressions is extended to account for dependent interpretations. A treatment of donkey sentences that accounts for their variable quantificational force is shown to follow naturally from the analysis. A pragmatic account of infelicitous uses of definites is provided and shown to compare favourably with that provided by dynamic semantic theories. Also, a novel treatment of plural definites is provided which accounts for their variable quantificational force

An Introduction to the Study of Pragmatics = Bevezetés a pragmatikába

A Bevezetés a pragmatikába című jegyzetünk megírásának célja az volt, hogy rávilágítsunk a pragmatika helyére és szerepére a nyelvészet tágabb kérdéskörében. A fordító szakos diákjaink számára szeretnénk érthetővé és elérhetővé tenni olyan kérdéseket és fogalmakat, mint a beszédaktus-elmélet, a beszédaktusok szerkezete és tipológiája Austin és Searle olvasatában, az illokúciók különböző vetületei, előfeltételezések, deiktikus nyelvi elemek, implikatúrák és Grice implikatúraelmélete, a beszédaktusok társadalmi beágyazódása és dimenziói, az udvariasság elmélete, valamint az udvarias közlésmód és indirekt beszédaktusok viszonya, és nem utolsósorban a kontextus meghatározó szerepe a nyelvi árnyalatok és értelmek kifejezésében. Könyvünkben külön fejezetben tárgyaljuk a pragmatika szerepét a fordítástudományban. A legfontosabb pragmatikai fogalmakon kívül a tudományág legújabb vetületeit is szeretnénk diákjaink elé tárni. Célunk az, hogy megértessük hallgatóinkkal a nyelvhasználat és az emberi lét más vetületei közt létező nagyon árnyalt kapcsolatrendszert, hogy rávilágítsunk arra, hogy a (nyelvi és társadalmi) kontextus milyen módon alkotja és csiszolja az értelmeket, valamint hogyan határozza meg a kommunikációs aktusban résztvevők szerepeinek az alakulását. Nem utolsósorban, könyvünk megírása által azt szeretnénk elérni, hogy a pragmatikát tanuló hallgatóink megértsék és tudatosítsák azt az összetett kapcsolatrendszert, amely a (nyelv)tudás és a tapasztalat, azaz a nyelv használata között létezik