waashk/dblp huggingface dataset:
analysis of selective strategies to build a dependency-analyzed corpus. this paper discusses sampling strategies for building a dependency-analyzed corpus and analyzes them with different kinds of corpora. we used the kyoto text corpus, a dependency-analyzed corpus of newspaper articles, and prepared the ipal corpus, a dependency-analyzed corpus of example sentences in dictionaries, as a new and different kind of corpus. the experimental results revealed that the length of the test set controlled the accuracy and that the longest-first strategy was good for an expanding corpus, but this was not the case when constructing a corpus from scratch.
supporting annotation layers for natural language processing. we demonstrate a system for flexible querying against text that has been annotated with the results of nlp processing. the system supports self-overlapping and parallel layers, integration of syntactic and ontological hierarchies, flexibility in the format of returned results, and tight integration with sql. we present a query language and its use on examples taken from the nlp literature.
recognition of the coherence relation between te-linked clauses. this paper describes a method for recognizing coherence relations between clauses which are linked by te in japanese-a translational equivalent of english and. we consider that the coherence relations are categories each of which has a prototype structure as well as the relationships among them. by utilizing this organization of the relations, we can infer an appropriate relation from the semantic structures of the clauses between which that relation holds. we carried out an experiment and obtained the correct recognition ratio of 82% for the 280 sentences.
an automatic method for summary evaluation using multiple evaluation results by a manual method. to solve a problem of how to evaluate computer-produced summaries, a number of automatic and manual methods have been proposed. manual methods evaluate summaries correctly, because humans evaluate them, but are costly. on the other hand, automatic methods, which use evaluation tools or programs, are low cost, although these methods cannot evaluate summaries as accurately as manual methods. in this paper, we investigate an automatic evaluation method that can reduce the errors of traditional automatic methods by using several evaluation results obtained manually. we conducted some experiments using the data of the text summarization challenge 2 (tsc-2). a comparison with conventional automatic methods shows that our method outperforms other methods usually used.
the lexical semantics of comparative expressions in a multi-level semantic processor. comparative expressions (ces) such as "bigger than" and "more oranges than" are highly ambiguous, and their meaning is context dependent. thus, they pose problems for the semantic interpretation algorithms typically used in natural language database interfaces. we focus on the comparison attribute ambiguities that occur with ces. to resolve these ambiguities our natural language interface interacts with the user, finding out which of the possible interpretations was intended. our multi-level semantic processor facilitates this interaction by recognizing the occurrence of comparison attribute ambiguity and then calculating and presenting a list of candidate comparison attributes from which the user may choose.
focus to emphasize tone structures for prosodic analysis in spoken language generation. we analyze the concept of focus in speech and the relationship between focus and speech acts for prosodic generation. we determine how the speaker's utterances are influenced by speaker's intention. the relationship between speech acts and focus information is used to define which parts of the sentence serve as the focus parts. we propose the focus to emphasize tones (fet) structure to analyze the focus components. we also design the fet grammar to analyze the intonation patterns and produce tone marks as a result of our analysis. we present a proof-of-the-concept working example to validate our proposal. more comprehensive evaluations are part of our current work.
improving the scalability of semi-markov conditional random fields for named entity recognition. this paper presents techniques to apply semi-crfs to named entity recognition tasks with a tractable computational cost. our framework can handle an ner task that has long named entities and many labels which increase the computational cost. to reduce the computational cost, we propose two techniques: the first is the use of feature forests, which enables us to pack feature-equivalent states, and the second is the introduction of a filtering process which significantly reduces the number of candidate states. this framework allows us to use a rich set of features extracted from the chunk-based representation that can capture informative characteristics of entities. we also introduce a simple trick to transfer information about distant entities by embedding label information into non-entity labels. experimental results show that our model achieves an f-score of 71.48% on the jnlpba 2004 shared task without using any external resources or post-processing techniques.
robust parsing based on discourse information: completing partial parses of ill-formed sentences on the basis of discourse information. in a consistent text, many words and phrases are repeatedly used in more than one sentence. when an identical phrase (a set of consecutive words) is repeated in different sentences, the constituent words of those sentences tend to be associated in identical modification patterns with identical parts of speech and identical modifiee-modifier relationships. thus, when a syntactic parser cannot parse a sentence as a unified structure, parts of speech and modifiee-modifier relationships among morphologically identical words in complete parses of other sentences within the same text provide useful information for obtaining partial parses of the sentence.in this paper, we describe a method for completing partial parses by maintaining consistency among morphologically identical words within the same text as regards their part of speech and their modifiee-modifier relationship. the experimental results obtained by using this method with technical documents offer good prospects for improving the accuracy of sentence analysis in a broad-coverage natural language processing system such as a machine translation system.
a term recognition approach to acronym recognition. we present a term recognition approach to extract acronyms and their definitions from a large text collection. parenthetical expressions appearing in a text collection are identified as potential acronyms. assuming terms appearing frequently in the proximity of an acronym to be the expanded forms (definitions) of the acronyms, we apply a term recognition method to enumerate such candidates and to measure the likelihood scores of the expanded forms. based on the list of the expanded forms and their likelihood scores, the proposed algorithm determines the final acronym-definition pairs. the proposed method combined with a letter matching algorithm achieved 78% precision and 85% recall on an evaluation corpus with 4,212 acronym-definition pairs.
meaningful clustering of senses helps boost word sense disambiguation performance. fine-grained sense distinctions are one of the major obstacles to successful word sense disambiguation. in this paper, we present a method for reducing the granularity of the wordnet sense inventory based on the mapping to a manually crafted dictionary encoding sense hierarchies, namely the oxford dictionary of english. we assess the quality of the mapping and the induced clustering, and evaluate the performance of coarse wsd systems in the senseval-3 english all-words task.
a computational view of the cognitive semantics of spatial prepostions. this paper outlines the linguistic semantic commitments underlying an application which automatically constructs depictions of verbal spatial descriptions. our approach draws on the ideational view of linguistic semantics developed by ronald langacker in his theory of cognitive grammar, and the conceptual representation of physical objects from the two-level semantics of bierwisch and lang. in particular the dimensions of the process of conventional imagery are used as a metric for the design of our own conceptual representation.
valido: a visual tool for validating sense annotations. in this paper we present valido, a tool that supports the difficult task of validating sense choices produced by a set of annotators. the validator can analyse the semantic graphs resulting from each sense choice and decide which sense is more coherent with respect to the structure of the adopted lexicon. we describe the interface and report an evaluation of the tool in the validation of manual sense annotations.
the effects of interaction on spoken discourse. near-term spoken language systems will likely be limited in their interactive capabilities. to design them, we shall need to model how the presence or absence of speaker interaction influences spoken discourse patterns in different types of tasks. in this research, a comprehensive examination is provided of the discourse structure and performance efficiency of both interactive and noninteractive spontaneous speech in a seriated assembly task. more specifically, telephone dialogues and audiotape monologues are compared, which represent opposites in terms of the opportunity for confirmation feedback and clarification subdialogues. keyboard communication patterns, upon which most natural language heuristics and algorithms have been based, also are contrasted with patterns observed in the two speech modalities. finally, implications are discussed for the design of near-term limited-interaction spoken language systems.
using bilingual dependencies to align words in english/french parallel corpora. this paper describes a word and phrase alignment approach based on a dependency analysis of french/english parallel corpora, referred to as alignment by "syntax-based propagation." both corpora are analysed with a deep and robust dependency parser. starting with an anchor pair consisting of two words that are translations of one another within aligned sentences, the alignment link is propagated to syntactically connected words.
an optimal tabular parsing algorithm. in this paper we relate a number of parsing algorithms which have been developed in very different areas of parsing theory, and which include deterministic algorithms, tabular algorithms, and a parallel algorithm. we show that these algorithms are based on the same underlying ideas.by relating existing ideas, we hope to provide an opportunity to improve some algorithms based on features of others. a second purpose of this paper is to answer a question which has come up in the area of tabular parsing, namely how to obtain a parsing algorithm with the property that the table will contain as little entries as possible, but without the possibility that two entries represent the same subderivation.
constructing semantic space models from parsed corpora. traditional vector-based models use word co-occurrence counts from large corpora to represent lexical meaning. in this paper we present a novel approach for constructing semantic spaces that takes syntactic relations into account. we introduce a formalisation for this class of models and evaluate their adequacy on two modelling tasks: semantic priming and automatic discrimination of lexical relations.
an alternative lr algorithm for tags. we present a new lr algorithm for tree-adjoining grammars. it is an alternative to an existing algorithm that is shown to be incorrect. furthermore, the new algorithm is much simpler, being very close to traditional lr parsing for context-free grammars. the construction of derived trees and the computation of features also become straightforward.
optimal constituent alignment with edge covers for semantic projection. given a parallel corpus, semantic projection attempts to transfer semantic role annotations from one language to another, typically by exploiting word alignments. in this paper, we present an improved method for obtaining constituent alignments between parallel sentences to guide the role projection task. our extensions are twofold: (a) we model constituent alignment as minimum weight edge covers in a bipartite graph, which allows us to find a globally optimal solution efficiently; (b) we propose tree pruning as a promising strategy for reducing alignment noise. experimental results on an english-german parallel corpus demonstrate improvements over state-of-the-art models.
on the evaluation and comparison of taggers: the effect of noise in testing corpora. this paper addresses the issue of pos tagger evaluation. such evaluation is usually performed by comparing the tagger output with a reference test corpus, which is assumed to be error-free. currently used corpora contain noise which causes the obtained performance to be a distortion of the real value. we analyze to what extent this distortion may invalidate the comparison between taggers or the measure of the improvement given by a new system. the main conclusion is that a more rigorous testing experimentation setting/designing is needed to reliably evaluate and compare tagger accuracies.
probabilistic parsing strategies. we present new results on the relation between context-free parsing strategies and their probabilistic counter-parts. we provide a necessary condition and a sufficient condition for the probabilistic extension of parsing strategies. these results generalize existing results in the literature that were obtained by considering parsing strategies in isolation.
empirically-based control of natural language generation. in this paper we present a new approach to controlling the behaviour of a natural language generation system by correlating internal decisions taken during free generation of a wide range of texts with the surface stylistic characteristics of the resulting outputs, and using the correlation to control the generator. this contrasts with the generate-and-test architecture adopted by most previous empirically-based generation approaches, offering a more efficient, generic and holistic method of generator control. we illustrate the approach by describing a system in which stylistic variation (in the sense of biber (1988)) can be effectively controlled during the generation of short medical information texts.
an alternative method of training probabilistic lr parsers. we discuss existing approaches to train lr parsers, which have been used for statistical resolution of structural ambiguity. these approaches are nonoptimal, in the sense that a collection of probability distributions cannot be obtained. in particular, some probability distributions expressible in terms of a context-free grammar cannot be expressed in terms of the lr parser constructed from that grammar, under the restrictions of the existing approaches to training of lr parsers. we present an alternative way of training that is provably optimal, and that allows all probability distributions expressible in the context-free grammar to be carried over to the lr parser. we also demonstrate empirically that this kind of training can be effectively applied on a large treebank.
semi-supervised maximum entropy based approach to acronym and abbreviation normalization in medical texts. text normalization is an important aspect of successful information retrieval from medical documents such as clinical notes, radiology reports and discharge summaries. in the medical domain, a significant part of the general problem of text normalization is abbreviation and acronym disambiguation. numerous abbreviations are used routinely throughout such texts and knowing their meaning is critical to data retrieval from the document. in this paper i will demonstrate a method of automatically generating training data for maximum entropy (me) modeling of abbreviations and acronyms and will show that using me modeling is a promising technique for abbreviation and acronym normalization. i report on the results of an experiment involving training a number of me models used to normalize abbreviations and acronyms on a sample of 10,000 rheumatology notes with ~89% accuracy.
an extended theory of head-driven parsing. we show that more head-driven parsing algorithms can be formulated than those occurring in the existing literature. these algorithms are inspired by a family of left-to-right parsing algorithms from a recent publication. we further introduce a more advanced notion of "head-driven parsing" which allows more detailed specification of the processing order of non-head elements in the right-hand side. we develop a parsing algorithm for this strategy, based on lr parsing techniques.
modeling filled pauses in medical dictations. filled pauses are characteristic of spontaneous speech and can present considerable problems for speech recognition by being often recognized as short words. an <i>um</i> can be recognized as <i>thumb</i> or <i>arm</i> if the recognizer's language model does not adequately represent fp's. recognition of quasi-spontaneous speech (medical dictation) is subject to this problem as well. results from medical dictations by 21 family practice physicians show that using an fp model trained on the corpus populated with fp's produces overall better result than a model trained on a corpus that excluded fp's or a corpus that had random fp's.
efficient tabular lr parsing. we give a new treatment of tabular lr parsing, which is an alternative to tomita's generalized lr algorithm. the advantage is twofold. firstly, our treatment is conceptually more attractive because it uses simpler concepts, such as grammar transformations and standard tabulation techniques also know as chart parsing. secondly, the static and dynamic complexity of parsing, both in space and time, is significantly reduced.
high throughput modularized nlp system for clinical text. this paper presents the results of the development of a high throughput, real time modularized text analysis and information retrieval system that identifies clinically relevant entities in clinical notes, maps the entities to several standardized nomenclatures and makes them available for subsequent information retrieval and data mining. the performance of the system was validated on a small collection of 351 documents partitioned into 4 query topics and manually examined by 3 physicians and 3 nurse abstractors for relevance to the query topics. we find that simple key phrase searching results in 73% recall and 77% precision. a combination of nlp approaches to indexing improve the recall to 92%, while lowering the precision to 67%.
prefix probabilities from stochastic tree adjoining grammars. language models for speech recognition typically use a probability model of the form pr(an/a1, a2, .... an-1 stochastic grammars, on the other hand, are typically used to assign structure to utterances. a language model of the above form is constructed from such grammars by computing the prefix probability &sum;wε&sigma;* pr(a1 ...anw), where w represents all possible terminations of the prefix a1 ... an. the main result in this paper is an algorithm to compute such prefix probabilities given a stochastic tree adjoining grammar (tag). the algorithm achieves the required computation in o(n 6) time. the probability of sub-derivations that do not derive any words in the prefix, but contribute structurally to its derivation, are precomputed to achieve termination. this algorithm enables existing corpus-based estimation techniques for stochastic tags to be used for language modelling.
a trainable rule-based algorithm for word segmentation. this paper presents a trainable rule-based algorithm for performing word segmentation. the algorithm provides a simple, language-independent alternative to large-scale lexical-based segmenters requiring large amounts of knowledge engineering. as a stand-alone segmenter, we show our algorithm to produce high performance chinese segmentation. in addition, we show the transformation-based algorithm to be effective in improving the output of several existing word segmentation algorithms in three different languages.
dictionaries, dictionary grammars and dictionary entry parsing. we identify two complementary processes in the conversion of machine-readable dictionaries into lexical databases: recovery of the dictionary stucture from the typographical markings which persist on the dictionary distribution tapes and embody the publishers' notational conventions; followed by making explicit all of the codified and ellided information packed into individual entries. we discuss notational conventions and tape formats, outline structural properties of dictionaries, observe a range of representational phenomena particularly relevant to dictionary parsing, and derive a set of minimal requirements for a dictionary grammar formalism. we present a general purpose dictionary entry parser which uses a formal notation designed to describe the structure of entries and performs a mapping from the flat character stream on the tape to a highly structured and fully instantiated representation of the dictionary. we demonstrate the power of the formalism by drawing examples from a range of dictionary sources which have been processed and converted into lexical databases.
modeling local context for pitch accent prediction. pitch accent placement is a major topic in intonational phonology research and its application to speech synthesis. what factors influence whether or not a word is made intonationally prominent or not is an open question. in this paper, we investigate how one aspect of a word's local context --- its collocation with neighboring words --- influences whether it is accented or not. results of experiments on two transcribed speech corpora in a medical domain show that such collocation information is a useful predictor of pitch accent placement.
learning intonation rules for concept to speech generation. in this paper, we report on an effort to provide a general-purpose spoken language generation tool for concept-to-speech (cts) applications by extending a widely used text generation package, fuf/surge, with an intonation generation component. as a first step, we applied machine learning and statistical models to learn intonation rules based on the semantic and syntactic information typically represented in fuf/surge at the sentence level. the results of this study are a set of intonation rules learned automatically which can be directly implemented in our intonation generation component. through 5-fold cross-validation, we show that the learned rules achieve around 90% accuracy for break index, boundary tone and phrase accent and 80% accuracy for pitch accent. our study is unique in its use of features produced by language generation to control intonation. the methodology adopted here can be employed directly when more discourse/pragmatic information is to be considered in the future.