waashk/dblp huggingface dataset:
multi-tagging for lexicalized-grammar parsing. with performance above 97% accuracy for newspaper text, part of speech (pos) tagging might be considered a solved problem. previous studies have shown that allowing the parser to resolve pos tag ambiguity does not improve performance. however, for grammar formalisms which use more fine-grained grammatical categories, for example tag and ccg, tagging accuracy is much lower. in fact, for these formalisms, premature ambiguity resolution makes parsing infeasible.we describe a multi-tagging approach which maintains a suitable level of lexical category ambiguity for accurate and efficient ccg parsing. we extend this multi-tagging approach to the pos level to overcome errors introduced by automatically assigned pos tags. although pos tagging accuracy seems high, maintaining some pos tag ambiguity in the language processing pipeline results in more accurate ccg supertagging.
scaling context space. context is used in many nlp systems as an indicator of a term's syntactic and semantic function. the accuracy of the system is dependent on the quality and quantity of contextual information available to describe each term. however, the quantity variable is no longer fixed by limited corpus resources. given fixed training time and computational resources, it makes sense for systems to invest time in extracting high quality contextual information from a fixed corpus. however, with an effectively limitless quantity of text available, extraction rate and representation size need to be considered. we use thesaurus extraction with a range of context extracting tools to demonstrate the interaction between context quantity, time and size on a corpus of 300 million words.
lexical disambiguation: sources of information and their statistical realization. lexical disambiguation can be achieved using different sources of information. aiming at high performance of automatic disambiguation it is important to know the relative importance and applicability of the various sources. in this paper we classify several sources of information and show how some of them can be achieved using statistical data. first evaluations indicate the extreme importance of local information, which mainly represents lexical associations and selectional restrictions for syntactically related words.
direct word sense matching for lexical substitution. this paper investigates conceptually and empirically the novel sense matching task, which requires to recognize whether the senses of two synonymous words match in context. we suggest direct approaches to the problem, which avoid the intermediate step of explicit word sense disambiguation, and demonstrate their appealing advantages and stimulating potential for future research.
two languages are more informative than one. this paper presents a new approach for resolving lexical ambiguities in one language using statistical data on lexical relations in another language. this approach exploits the differences between mappings of words to senses in different languages. we concentrate on the problem of target word selection in machine translation, for which the approach is directly applicable, and employ a statistical model for the selection mechanism. the model was evaluated using two sets of hebrew and german examples and was found to be very useful for disambiguation.
similarity-based methods for word sense disambiguation. we compare four similarity-based estimation methods against back-off and maximum-likelihood estimation methods on a pseudo-word sense disambiguation task in which we controlled for both unigram and bigram frequency. the similarity-based methods perform up to 40% better on this particular task. we also conclude that events that occur only once in the training set have major impact on similarity-based estimates.
contextual word similarity and estimation from sparse data. in recent years there is much interest in word cooccurrence relations, such as n-grams, verb-object combinations, or cooccurrence within a limited context. this paper discusses how to estimate the probability of cooccurrences that do not occur in the training data. we present a method that makes local analogies between each specific unobserved cooccurrence and other cooccurrences that contain similar words, as determined by an appropriate word similarity metric. our evaluation suggests that this method performs better than existing smoothing methods, and may provide an alternative to class based models.
similarity-based estimation of word cooccurrence probabilities. in many applications of natural language processing it is necessary to determine the likelihood of a given word combination. for example, a speech recognizer may need to determine which of the two word combinations "eat a peach" and "eat a beach" is more likely. statistical nlp methods determine the likelihood of a word combination according to its frequency in a training corpus. however, the nature of language is such that many word combinations are infrequent and do not occur in a given corpus. in this work we propose a method for estimating the probability of such previously unseen word combinations using available information on "most similar" words.we describe a probabilistic word association model based on distributional word similarity, and apply it to improving probability estimates for unseen word bigrams in a variant of katz's back-off model. the similarity-based method yields a 20% perplexity improvement in the prediction of unseen bigrams and statistically significant reductions in speech-recognition error.
cooking up referring expressions. this paper describes the referring expression generation mechanisms used in epicure, a computer program which produces natural language descriptions of cookery recipes. major features of the system include: an underlying ontology which permits the representation of non-singular entities; a notion of discriminatory power, to determine what properties should be used in a description; and a patr-like unification grammar to produce surface linguistic strings.
the interpretation of tense and aspect in english. an analysis of english tense and aspect is presented that specifies temporal precedence relations within a sentence. the relevant reference points for interpretation are taken to be the initial and terminal points of events in the world, as well as two "hypothetical" times: the perfect time (when a sentence contains perfect aspect) and the progressive or during time. a method for providing temporal interpretation for nontensed elements in the sentence is also described.
investigating regular sense extensions based on intersective levin classes. in this paper we specifically address questions of polysemy with respect to verbs, and how regular extensions of meaning can be achieved through the adjunction of particular syntactic phrases. we see verb classes as the key to making generalizations about regular extensions of meaning. current approaches to english classification, levin classes and wordnet, have limitations in their applicability that impede their utility as general classification schemes. we present a refinement of levin classes, intersective sets, which are a more fine-grained classification and have more coherent sets of syntactic frames and associated semantic components. we have preliminary indications that the membership of our intersective sets will be more compatible with wordnet than the original levin classes. we also have begun to examine related classes in portuguese, and find that these verbs demonstrate similarly coherent syntactic and semantic properties.
the role of semantic roles in disambiguating verb senses. we describe an automatic word sense disambiguation (wsd) system that disambiguates verb senses using syntactic and semantic features that encode information about predicate arguments and semantic classes. our system performs at the best published accuracy on the english verbs of senseval-2. we also experiment with using the gold-standard predicate-argument labels from propbank for disambiguating fine-grained wordnet senses and course-grained propbank framesets, and show that disambiguation of verb senses can be further improved with better extraction of semantic roles.
mapping wordnets using structural information. we present a robust approach for linking already existing lexical/semantic hierarchies. we used a constraint satisfaction algorithm (relaxation labeling) to select - among a set of candidates- the node in a target taxonomy that bests matches each node in a source taxonomy. in particular, we use it to map the nominal part of wordnet 1.5 onto wordnet 1.6, with a very high precision and a very low remaining ambiguity.
a noisy-channel model for document compression. we present a document compression system that uses a hierarchical noisy-channel model of text production. our compression system first automatically derives the syntactic structure of each sentence and the overall discourse structure of the text given as input. the system then uses a statistical hierarchical model of text production in order to drop non-important syntactic and discourse constituents so as to generate coherent, grammatical document compressions of arbitrary length. the system outperforms both a baseline and a sentence-based compression system that operates by simplifying sequentially all sentences in a text. our results support the claim that discourse knowledge plays an important role in document summarization.
bayesian query-focused summarization. we present bayesum (for "bayesian summarization"), a model for sentence extraction in query-focused summarization. bayesum leverages the common case in which multiple documents are relevant to a single query. using these documents as reinforcement for query terms, bayesum is not afflicted by the paucity of information in short queries. we show that approximate inference in bayesum is possible on large data sets and results in a state-of-the-art summarization system. furthermore, we show how bayesum can be understood as a justified query expansion technique in the language modeling for ir framework.
efficient unsupervised discovery of word categories using symmetric patterns and high frequency words. we present a novel approach for discovering word categories, sets of words sharing a significant aspect of their meaning. we utilize meta-patterns of high-frequency words and content words in order to discover pattern candidates. symmetric patterns are then identified using graph-based measures, and word categories are created based on graph clique sets. our method is the first pattern-based method that requires no corpus annotation or manually provided seed patterns or words. we evaluate our algorithm on very large corpora in two languages, using both human judgments and wordnet-based evaluation. our fully unsupervised results are superior to previous work that used a pos tagged corpus, and computation time for huge corpora are orders of magnitude faster than previously reported.
assigning intonational features in synthesized spoken directions. speakers convey much of the information hearers use to interpret discourse by varying prosodic features such as phrasing, pitch accent placement, tune, and pitch range. the ability to emulate such variation is crucial to effective (synthetic) speech generation. while text-to-speech synthesis must rely primarily upon structural information to determine appropriate intonational features, speech synthesized from an abstract representation of the message to be conveyed may employ much richer sources. the implementation of an intonation assignment component for direction assistance, a program which generates spoken directions, provides a first approximation of how recent models of discourse structure can be used to control intonational variation in ways that build upon recent research in intonational meaning. the implementation further suggests ways in which these discourse models might be augmented to permit the assignment of appropriate intonational features.
a three-valued interpretation of negation in feature structure descriptions. feature structures are informational elements that have been used in several linguistic theories and in computational systems for natural-language processing. a logical calculus has been developed and used as a description language for feature structures. in the present work, a framework in three-valued logic is suggested for defining the semantics of a feature structure description language, allowing for a more complete set of logical operators. in particular, the semantics of the negation and implication operators are examined. various proposed interpretations of negation and implication are compared within the suggested framework. one particular interpretation of the description language with a negation operator is described and its computational aspects studied.
interactively exploring a machine translation model. this paper describes a method of interactively visualizing and directing the process of translating a sentence. the method allows a user to explore a model of syntax-based statistical machine translation (mt), to understand the model's strengths and weaknesses, and to compare it to other mt systems. using this visualization method, we can find and address conceptual and practical problems in an mt system. in our demonstration at acl, new users of our tool will drive a syntax-based decoder for themselves.
an information-state approach to collaborative reference. we describe a dialogue system that works with its interlocutor to identify objects. our contributions include a concise, modular architecture with reversible processes of understanding and generation, an information-state model of reference, and flexible links between semantics and collaborative problem solving.
a nonparametric method for extraction of candidate phrasal terms. this paper introduces a new method for identifying candidate phrasal terms (also known as multiword units) which applies a nonparametric, rank-based heuristic measure. evaluation of this measure, the mutual rank ratio metric, shows that it produces better results than standard statistical measures when applied to this task.
the use of syntactic clues in discourse processing. the desirability of a syntactic parsing component in natural language understanding systems has been the subject of debate for the past several years. this paper describes an approach to automatic text processing which is entirely based on syntactic form. a program is described which processes one genre of discourse, that of newspaper reports. the program creates summaries of reports by relying on an expanded concept of text grounding: certain syntactic structures and tense/aspect pairs indicate the most important events in a news story. supportive, background material is also highly coded syntactically. certain types of information are routinely expressed with distinct syntactic forms. where more than one episode occurs in a single report, a change of episode will also be marked syntactically in a reliable way.
learning a syntagmatic and paradigmatic structure from language data with a bi-multigram model. in this paper, we present a stochastic language modeling tool which aims at retrieving variable-length phrases (multigrams), assuming bigram dependencies between them. the phrase retrieval can be intermixed with a phrase clustering procedure, so that the language data are iteratively structured at both a paradigmatic and a syntagmatic level in a fully integrated way. perplexity results on atr travel arrangement data with a bi-multigram model (assuming bigram correlations between the phrases) come very close to the trigram scores with a reduced number of entries in the language model. also the ability of the class version of the model to merge semantically related phrases into a common class is illustrated.
experiments with learning parsing heuristics. any large language processing software relies in its operation on heuristic decisions concerning the strategy of processing. these decisions are usually "hard-wired" into the software in the form of hand-crafted heuristic rules, independent of the nature of the processed texts. we propose an alternative, adaptive approach in which machine learning techniques learn the rules from examples of sentences in each class. we have experimented with a variety of learning techniques on a representative instance of this problem within the realm of parsing. our approach lead to the discovery of new heuristics that perform significantly better than the current hand-crafted heuristic. we discuss the entire cycle of application of machine learning and suggest a methodology for the use of machine learning as a technique for the adaptive optimisation of language-processing software.
answer extraction, semantic clustering, and extractive summarization for clinical question answering. this paper presents a hybrid approach to question answering in the clinical domain that combines techniques from summarization and information retrieval. we tackle a frequently-occurring class of questions that takes the form "what is the best drug treatment for x?" starting from an initial set of medline citations, our system first identifies the drugs under study. abstracts are then clustered using semantic classes from the umls ontology. finally, a short extractive summary is generated for each abstract to populate the clusters. two evaluations---a manual one focused on short answers and an automatic one focused on the supporting abstract---demonstrate that our system compares favorably to pubmed, the search system most widely used by physicians today.
relieving the data acquisition bottleneck in word sense disambiguation. supervised learning methods for wsd yield better performance than unsupervised methods. yet the availability of clean training data for the former is still a severe challenge. in this paper, we present an unsupervised bootstrapping approach for wsd which exploits huge amounts of automatically generated noisy data for training within a supervised learning framework. the method is evaluated using the 29 nouns in the english lexical sample task of senseval 2. our algorithm does as well as supervised algorithms on 31% of this test set, which is an improvement of 11% (absolute) over state-of-the-art bootstrapping wsd algorithms. we identify seven different factors that impact the performance of our system.
an unsupervised method for word sense tagging using parallel corpora. we present an unsupervised method for word sense disambiguation that exploits translation correspondences in parallel corpora. the technique takes advantage of the fact that cross-language lexicalizations of the same concept tend to be consistent, preserving some core element of its semantics, and yet also variable, reflecting differing translator preferences and the influence of context. working with parallel corpora introduces an extra complication for evaluation, since it is difficult to find a corpus that is both sense tagged and parallel with another language; therefore we use pseudo-translations, created by machine translation systems, in order to make possible the evaluation of the approach against a standard test set. the results demonstrate that word-level translation correspondences are a valuable source of information for sense disambiguation.
detecting errors in discontinuous structural annotation. consistency of corpus annotation is an essential property for the many uses of annotated corpora in computational and theoretical linguistics. while some research addresses the detection of inconsistencies in positional annotation (e.g., part-of-speech) and continuous structural annotation (e.g., syntactic constituency), no approach has yet been developed for automatically detecting annotation errors in discontinuous structural annotation. this is significant since the annotation of potentially discontinuous stretches of material is increasingly relevant, from tree-banks for free-word order languages to semantic and discourse annotation.in this paper we discuss how the variation n-gram error detection approach (dickinson and meurers, 2003a) can be extended to discontinuous structural annotation. we exemplify the approach by showing how it successfully detects errors in the syntactic annotation of the german tiger corpus (brants et al., 2002).
a corpus-based approach to topic in danish dialog. we report on an investigation of the pragmatic category of topic in danish dialog and its correlation to surface features of nps. using a corpus of 444 utterances, we trained a decision tree system on 16 features. the system achieved near-human performance with success rates of 84--89% and f1-scores of 0.63--0.72 in 10-fold cross validation tests (human performance: 89% and 0.78). the most important features turned out to be preverbal position, definiteness, pronominalisation, and non-subordination. we discovered that nps in epistemic matrix clauses (e.g. "i think ...") were seldom topics and we suspect that this holds for other interpersonal matrix clauses as well.
deep syntactic processing by combining shallow methods. we present a novel approach for finding discontinuities that outperforms previously published results on this task. rather than using a deeper grammar formalism, our system combines a simple unlexicalized pcfg parser with a shallow pre-processor. this pre-processor, which we call a trace tagger, does surprisingly well on detecting where discontinuities can occur without using phase structure information.
grammars for local and long dependencies. polarized dependency (pd-) grammars are proposed as a means of efficient treatment of discontinuous constructions. pd-grammars describe two kinds of dependencies: local, explicitly derived by the rules, and long, implicitly specified by negative and positive valencies of words. if in a pd-grammar the number of non-saturated valencies in derived structures is bounded by a constant, then it is weakly equivalent to a cf-grammar and has a o(n3)-time parsing algorithm. it happens that such bounded pd-grammars are strong enough to express such phenomena as unbounded raising, extraction and extraposition.
multext-east: parallel and comparable corpora and lexicons for six central and eastern european languages. the eu copernicus project multext-east has created a multi-lingual corpus of text and speech data, covering the six languages of the project: bulgarian, czech, estonian, hungarian, romanian, and slovene. in addition, wordform lexicons for each of the languages were developed. the corpus includes a parallel component consisting of orwell's nineteen eighty-four, with versions in all six languages tagged for part-of-speech and aligned to english (also tagged for pos). we describe the encoding format and data architecture designed especially for this corpus, which is generally usable for encoding linguistic corpora. we also describe the methodology for the development of a harmonized set of morphosyntactic descriptions (msds), which builds upon the scheme for western european languages developed within the eagles project. we discuss the special concerns for handling the six project languages, which cover three distinct language families.
machine translation using probabilistic synchronous dependency insertion grammars. syntax-based statistical machine translation (mt) aims at applying statistical models to structured data. in this paper, we present a syntax-based statistical machine translation system based on a probabilistic synchronous dependency insertion grammar. synchronous dependency insertion grammars are a version of synchronous grammars defined on dependency trees. we first introduce our approach to inducing such a grammar from parallel corpora. second, we describe the graphical model for the machine translation task, which can also be viewed as a stochastic tree-to-tree transducer. we introduce a polynomial time decoding algorithm for the model. we evaluate the outputs of our mt system using the nist and bleu automatic mt evaluation software. the result shows that our system outperforms the baseline system based on the ibm models in both translation speed and quality.