6,398 research outputs found
Gathering Statistics to Aspectually Classify Sentences with a Genetic Algorithm
This paper presents a method for large corpus analysis to semantically
classify an entire clause. In particular, we use cooccurrence statistics among
similar clauses to determine the aspectual class of an input clause. The
process examines linguistic features of clauses that are relevant to aspectual
classification. A genetic algorithm determines what combinations of linguistic
features to use for this task.Comment: postscript, 9 pages, Proceedings of the Second International
Conference on New Methods in Language Processing, Oflazer and Somers ed
Exploring Metaphorical Senses and Word Representations for Identifying Metonyms
A metonym is a word with a figurative meaning, similar to a metaphor. Because
metonyms are closely related to metaphors, we apply features that are used
successfully for metaphor recognition to the task of detecting metonyms. On the
ACL SemEval 2007 Task 8 data with gold standard metonym annotations, our system
achieved 86.45% accuracy on the location metonyms. Our code can be found on
GitHub.Comment: 9 pages, 8 pages conten
Distinguishing Antonyms and Synonyms in a Pattern-based Neural Network
Distinguishing between antonyms and synonyms is a key task to achieve high
performance in NLP systems. While they are notoriously difficult to distinguish
by distributional co-occurrence models, pattern-based methods have proven
effective to differentiate between the relations. In this paper, we present a
novel neural network model AntSynNET that exploits lexico-syntactic patterns
from syntactic parse trees. In addition to the lexical and syntactic
information, we successfully integrate the distance between the related words
along the syntactic path as a new pattern feature. The results from
classification experiments show that AntSynNET improves the performance over
prior pattern-based methods.Comment: EACL 2017, 10 page
Automatic Identification of Aspectual Classes across Verbal Readings
International audienceThe automatic prediction of aspectual classes is very challenging for verbs whose aspectual value varies across readings, which are the rule rather than the exception. This paper sheds a new perspective on this problem by using a machine learning approach and a rich morpho-syntactic and semantic valency lexicon.In contrast to previous work, where the aspectual value of corpus clauses is determined on the basis of features retrieved from the corpus, we use features extracted from the lexicon, and aim to predict the aspectual value of verbal \textit{readings} rather than verbs.Studying the performance of the classifiers on a set of manually annotated verbal readings, we found that our lexicon provided enough information to reliably predict the aspectual value of verbs across their readings.We additionally tested our predictions for unseen predicates through a task based evaluation, by using them in the automatic detection of temporal relation types in TempEval 2007 tasks for French. These experiments also confirmed the reliability of our aspectual predictions, even for unseen verbs
Empirical methods for the study of denotation in nominalizations in Spanish
This article deals with deverbal nominalizations in Spanish; concretely, we focus on the denotative distinction between event and result nominalizations. The goals of this work is twofold: first, to detect the most relevant features for this denotative distinction; and, second, to build an automatic classification system of deverbal nominalizations according to their denotation. We have based our study on theoretical hypotheses dealing with this semantic distinction and we have analyzed them empirically by means of Machine Learning techniques which are the basis of the ADN-Classifier. This is the first tool that aims to automatically classify deverbal nominalizations in event, result, or underspecified denotation types in Spanish. The ADN-Classifier has helped us to quantitatively evaluate the validity of our claims regarding deverbal nominalizations. We set up a series of experiments in order to test the ADN-Classifier with different models and in different realistic scenarios depending on the knowledge resources and natural language processors available. The ADN-Classifier achieved good results (87.20% accuracy)
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