Understanding metonymies in discourse

We propose a new computational model for the resolution of metonymies, a particular type of figurative language. Typically, metonymies are considered as a violation of semantic constraints (e.g., those expressed by selectional restrictions) that require some repair mechanism (e.g., type coercion) for proper interpretation. We reject this view, arguing that it misses out on the interpretation of a considerable number of utterances. Instead, we treat literal and figurative language on a par, by computing both kinds of interpretation independently from each other as long as their semantic representation structures are consistent with the underlying knowledge representation structures of the domain of discourse. The following general heuristic principles apply for making reasonable selections from the emerging readings. We argue that the embedding of utterances in a coherent discourse context is as important for recognizing and interpreting metonymic utterances as intrasentential semantic constraints. Therefore, in our approach, (metonymic or literal) interpretations that establish referential cohesion are preferred over ones that do not. In addition, metonymic interpretations that conform to a metonymy schema are preferred over metonymic ones that do not, and metonymic interpretations that are in conformance with knowledge-based aptness conditions are preferred over metonymic ones that are not. We lend further credit to our model by discussing empirical data from an evaluation study which highlights the importance of the discourse embedding of metonymy interpretation for both anaphora and metonymy resolution

Contextual Effects on Metaphor Comprehension: Experiment and Simulation

This paper presents a computational model of referential metaphor comprehension. This model is designed on top of Latent Semantic Analysis (LSA), a model of the representation of word and text meanings. Compre­hending a referential metaphor consists in scanning the semantic neighbors of the metaphor in order to find words that are also semantically related to the context. The depth of that search is compared to the time it takes for humans to process a metaphor. In particular, we are interested in two independent variables : the nature of the reference (either a literal meaning or a figurative meaning) and the nature of the context (inductive or not inductive). We show that, for both humans and model, first, metaphors take longer to process than the literal meanings and second, an inductive context can shorten the processing time

Reconnaissance d'entités nommées : enrichissement d'un système à base de connaissances à partir de techniques de fouille de textes

International audienceIn this paper, we present and analyze the results obtained by our named entity recognition system, CasEN, during the Ester2 evaluation campaign. We identify on what difficulties our system was the most challenged, which mainly are: out-of-vocabulary words, metonymy and detection of the boundaries of named entities. Next, we propose a direction which may help us for improving performances of our system, by using exhaustive hierarchical and sequential data mining algorithms. This approach aims at extracting patterns corresponding to useful linguistic constructs for recognizing named entities. Finaly, we describe our experiments, give the results we currently obtain and analyze those results

An Analysis of the Performances of the CasEN Named Entities Recognition System in the Ester2 Evaluation Campaign

8 pagesIn this paper, we present a detailed and critical analysis of the behaviour of the CasEN named entity recognition system during the French Ester2 evaluation campaign. In this project, CasEN has been confronted with the task of detecting and categorizing named entities in manual and automatic transcriptions of radio broadcastings. At first, we give a general presentation of the Ester2 campaign. Then, we describe our system, based on transducers. Next, we depict how systems were evaluated during this campaign and we report the main official results. Afterwards, we investigate in details the influence of some annotation biases which have significantly affected the estimation of the performances of systems. At last, we conduct an in-depth analysis of the effective errors of the CasEN system, providing us with some useful indications about phenomena that gave rise to errors (e.g. metonymy, encapsulation, detection of right boundaries) and are as many challenges for named entity recognition systems

Syntactic Features and Word Similarity for Supervised Metonymy Resolution

We present a supervised machine learning algorithm for metonymy resolution, which exploits the similarity between examples of conventional metonymy. We show that syntactic head-modifier relations are a high precision feature for metonymy recognition but suffer from data sparseness

A STYLISTIC ANALYSIS OF FIGURES OF SPEECH IN LES MISERABLES MOVIE

The way people speak is influenced by their own style. It is easier for the addressee to understand the addresser’s idea if he or she is familiar with the addresser’s language style. In stylistic approach, style can be seen in figurative language through figures of speech. Thus, the aims of this research are to describe the types of figures of speech and to find out the functions of figures of speech in Les Miserables. This research employed descriptive qualitative approach. The data were in the form of utterances (words, phrases, clauses, and utterances) spoken by the characters in Les Miserables movie. The main instrument of the study was the researcher herself. The researcher employed some steps during the data collection: watching the movie, finding its script, making data sheet, and categorizing the data. In conducting the data analysis, the researcher passed some steps, identifying, classifying, and making interpretation. To gain the data trustworthiness, the researcher asked triangulators to check the data. Using Perrine’s classification of types of figures of speech, this results show that there are eleven types of figures of speech in Les Miserables Movie. They are simile (12 times), metaphor (20 times), personification (22 times), apostrophe (10 times), metonymy (once ), synecdoche (7 times), symbol (22 times), paradox (8 times), hyperbole (13 times), irony (16 times), and litotes (10 times). The most often used types of figures of speech are personification and symbol. The character used personification often to depict a story as if this world can execute anything. Meanwhile, the use of symbol was used to represent idea in society. The functions of figures of speech found in the movie are to give imaginative pleasure (105 times), to give additional imagery (86 times), to add emotional intensity (77 times), and to concrete the meaning in a brief compass (60 times). Giving imaginative pleasure is the main function of the use of figures of speech in Les Miserables movie because most figurative language can create pleasure in readers’ mind

Resolving Other-Anaphora

Institute for Communicating and Collaborative SystemsReference resolution is a major component of any natural language system. In the past 30 years significant progress has been made in coreference resolution. However, there is more anaphora in texts than coreference. I present a computational treatment of other-anaphora, i.e., referential noun phrases (NPs) with non-pronominal heads modi- fied by “other” or “another”: [. . . ] the move is designed to more accurately reflect the value of products and to put steel on more equal footing with other commodities. Such NPs are anaphoric (i.e., they cannot be interpreted in isolation), with an antecedent that may occur in the previous discourse or the speaker’s and hearer’s mutual knowledge. For instance, in the example above, the NP “other commodities” refers to a set of commodities excluding steel, and it can be paraphrased as “commodities other than steel”. Resolving such cases requires first identifying the correct antecedent(s) of the other-anaphors. This task is the major focus of this dissertation. Specifically, the dissertation achieves two goals. First, it describes a procedure by which antecedents of other-anaphors can be found, including constraints and preferences which narrow down the search. Second, it presents several symbolic, machine learning and hybrid resolution algorithms designed specifically for other-anaphora. All the algorithms have been implemented and tested on a corpus of examples from the Wall Street Journal. The major results of this research are the following: 1. Grammatical salience plays a lesser role in resolving other-anaphors than in resolving pronominal anaphora. Algorithms that solely rely on grammatical features achieved worse results than algorithms that used semantic features as well. 2. Semantic knowledge (such as “steel is a commodity”) is crucial in resolving other-anaphors. Algorithms that operate solely on semantic features outperformed those that operate on grammatical knowledge. 3. The quality and relevance of the semantic knowledge base is important to success. WordNet proved insufficient as a source of semantic information for resolving other-anaphora. Algorithms that use the Web as a knowledge base achieved better performance than those using WordNet, because the Web contains domain specific and general world knowledge which is not available from WordNet. 4. But semantic information by itself is not sufficient to resolve other-anaphors, as it seems to overgenerate, leading to many false positives. 5. Although semantic information is more useful than grammatical information, only integration of semantic and grammatical knowledge sources can handle the full range of phenomena. The best results were obtained from a combination of semantic and grammatical resources. 6. A probabilistic framework is best at handling the full spectrum of features, both because it does not require commitment as to the order in which the features should be applied, and because it allows features to be treated as preferences, rather than as absolute constraints. 7. A full resolution procedure for other-anaphora requires both a probabilistic model and a set of informed heuristics and back-off procedures. Such a hybrid system achieved the best results so far on other-anaphora