waashk/dblp huggingface dataset:
processing unknown words in hpsg. the lexical acquisition system presented in this paper incrementally updates linguistic properties of unknown words inferred from their surrounding context by parsing sentences with an hpsg grammar for german. we employ a gradual, information-based concept of "unknownness" providing a uniform treatment for the range of completely known to maximally unknown lexical entries. "unknown" information is viewed as revisable information, which is either generalizable or specializable. updating takes place after parsing, which only requires a modified lexical lookup. revisable pieces of information are identified by grammar-specified declarations which provide access paths into the parse feature structure. the updating mechanism revises the corresponding places in the lexical feature structures iff the context actually provides new information. for revising generalizable information, type union is required. a worked-out example demonstrates the inferential capacity of our implemented system.
a simple but useful approach to conjunct identification. this paper presents an approach to identifying conjuncts of coordinate conjunctions appearing in text which has been labelled with syntactic and semantic tags. the overall project of which this research is a part is also briefly discussed. the program was tested on a 10,000 word chapter of the merck veterinary manual. the algorithm is deterministic and domain independent and it performs relatively well on a large real-life domain. constructs not handled by the simple algorithm are also described in some detail.
semi-automatic recognition of noun modifier relationships. semantic relationships among words and phrases are often marked by explicit syntactic or lexical clues that help recognize such relationships in texts. within complex nominals, however, few overt clues are available. systems that analyze such nominals must compensate for the lack of surface clues with other information. one way is to load the system with lexical semantics for nouns or adjectives. this merely shifts the problem elsewhere: how do we define the lexical semantics and build large semantic lexicons? another way is to find constructions similar to a given complex nominal, for which the relationships are already known. this is the way we chose, but it too has drawbacks. similarity is not easily assessed, similar analyzed constructions may not exist, and if they do exist, their analysis may not be appropriate for the current nominal.we present a semi-automatic system that identifies semantic relationships in noun phrases without using precoded noun or adjective semantics. instead, partial matching on previously analyzed noun phrases leads to a tentative interpretation of a new input. processing can start without prior analyses, but the early stage requires user interaction. as more noun phrases are analyzed, the system learns to find better interpretations and reduces its reliance on the user. in experiments on english technical texts the system correctly identified 60--70% of relationships automatically.
towards a single proposal in spelling correction. the study presented here relies on the integrated use of different kinds of knowledge in order to improve first-guess accuracy in non-word context-sensitive correction for general unrestricted text. state of the art spelling correction systems, e.g. ispell, apart from detecting spelling errors, also assist the user by offering a set of candidate corrections that are close to the misspelled word. based on the correction proposals of ispell, we built several guessers, which were combined in different ways. firstly, we evaluated all possibilities and selected the best ones in a corpus with artificially generated typing errors. secondly, the best combinations were tested on texts with genuine spelling errors. the results for the latter suggest that we can expect automatic non-word correction for all the errors in a free running text with 80% precision and a single proposal 98% of the times (1.02 proposals on average).
redundancy: helping semantic disambiguation. redundancy is a good thing, at least in a learning process. to be a good teacher you must say what you are going to say, say it, then say what you have just said. well, three times is better than one. to acquire and learn knowledge from text for building a lexical knowledge base, we need to find a source of information that states facts, and repeats them a few times using slightly different sentence structures. a technique is needed for gathering information from that source and identify the redundant information. the extraction of the commonality is an active learning of the knowledge expressed. the proposed research is based on a clustering method developed by barri&egrave;re and popowich (1996) which performs a gathering of related information about a particular topic. individual pieces of information are represented via the conceptual graph (cg) formalism and the result of the clustering is a large cg embedding all individual graphs. in the present paper, we suggest that the identification of the redundant information within the resulting graph is very useful for disambiguation of the original information at the semantic level.
a tool kit for lexicon building. this paper describes a set of interactive routines that can be used to create, maintain, and update a computer lexicon. the routines are available to the user as a set of commands resembling a simple operating system. the lexicon produced by this system is based on lexical-semantic relations, but is compatible with a variety of other models of lexicon structure. the lexicon builder is suitable for the generation of moderate-sized vocabularies and has been used to construct a lexicon for a small medical expert system. a future version of the lexicon builder will create a much larger lexicon by parsing definitions from machine-readable dictionaries.
guided parsing of range concatenation languages. the theoretical study of the range concatenation grammar [rcg] formalism has revealed many attractive properties which may be used in nlp. in particular, range concatenation languages [rcl] can be parsed in polynomial time and many classical grammatical formalisms can be translated into equivalent rcgs without increasing their worst-case parsing time complexity. for example, after translation into an equivalent rcg, any tree adjoining grammar can be parsed in o(n6) time. in this paper, we study a parsing technique whose purpose is to improve the practical efficiency of rcl parsers. the non-deterministic parsing choices of the main parser for a language l are directed by a guide which uses the shared derivation forest output by a prior rcl parser for a suitable superset of l. the results of a practical evaluation of this method on a wide coverage english grammar are given.
parsing vs. text processing in the analysis of dictionary definitions. we have analyzed definitions from webster's seventh new collegiate dictionary using sager's linguistic string parser and again using basic unix text processing utilities such as grep and awk. this paper evaluates both procedures, compares their results, and discusses possible future lines of research exploiting and combining their respective strengths.
a simple hybrid aligner for generating lexical correspondences in parallel texts. we present an algorithm for bilingual word alignment that extends previous work by treating multi-word candidates on a par with single words, and combining some simple assumptions about the translation process to capture alignments for low frequency words. as most other alignment algorithms it uses cooccurrence statistics as a basis, but differs in the assumptions it makes about the translation process. the algorithm has been implemented in a modular system that allows the user to experiment with different combinations and variants of these assumptions. we give performance results from two evaluations, which compare will with results reported in the literature.
modeling local coherence: an entity-based approach. this article proposes a novel framework for representing and measuring local coherence. central to this approach is the entity-grid representation of discourse, which captures patterns of entity distribution in a text. the algorithm introduced in the article automatically abstracts a text into a set of entity transition sequences and records distributional, syntactic, and referential information about discourse entities. we re-conceptualize coherence assessment as a learning task and show that our entity-based representation is well-suited for ranking-based generation and text classification tasks. using the proposed representation, we achieve good performance on text ordering, summary coherence evaluation, and readability assessment.
extracting paraphrases from a parallel corpus. while paraphrasing is critical both for interpretation and generation of natural language, current systems use manual or semi-automatic methods to collect paraphrases. we present an unsupervised learning algorithm for identification of paraphrases from a corpus of multiple english translations of the same source text. our approach yields phrasal and single word lexical paraphrases as well as syntactic paraphrases.
analysis of source identified text corpora: exploring the statistics of the reused text and authorship. this paper aims at providing a view of text recycled, within a short time, by the authors themselves. we first present a simple and general method for extracting reused term sequences, and then analyze several author-identified text collections to compare the statistical quantities. the ratio of recycling is also measured for each collection. finally, related research topics are introduced together with some discussion of future research directions.
information fusion in the context of multi-document summarization. we present a method to automatically generate a concise summary by identifying and synthesizing similar elements across related text from a set of multiple documents. our approach is unique in its usage of language generation to reformulate the wording of the summary.
resolving anaphors in embedded sentences. we propose an algorithm to resolve anaphors, tackling mainly the problem of intrasentential antecedents. we base our methodology on the fact that such antecedents are likely to occur in embedded sentences. sidner's focusing mechanism is used as the basic algorithm in a more complete approach. the proposed algorithm has been tested and implemented as a part of a conceptual analyser, mainly to process pronouns. details of an evaluation are given.
generalized chart algorithm: an efficient procedure for cost-based abduction. we present an efficient procedure for cost-based abduction, which is based on the idea of using chart parsers as proof procedures. we discuss in detail three features of our algorithm --- goal-driven bottom-up derivation, tabulation of the partial results, and agenda control mechanism --- and report the results of the preliminary experiments, which show how these features improve the computational efficiency of cost-based abduction.
a flexible example-based parser based on the sstc. in this paper we sketch an approach for natural language parsing. our approach is an example-based approach, which relies mainly on examples that already parsed to their representation structure, and on the knowledge that we can get from these examples the required information to parse a new input sentence. in our approach, examples are annotated with the structured string tree correspondence (sstc) annotation schema where each sstc describes a sentence, a representation tree as well as the correspondence between substrings in the sentence and subtrees in the representation tree. in the process of parsing, we first try to build subtrees for phrases in the input sentence which have been successfully found in the example-base - a bottom up approach. these subtrees will then be combined together to form a single rooted representation tree based on an example with similar representation structure - a top down approach.
aspects of clause politeness in japanese: an extended inquiry semantics treatment. the inquiry semantics approach of the nigel computational systemic grammar of english has proved capable of revealing distinctions within propositional content that the text planning process needs to control in order for adequate text to be generated. an extension to the chooser and inquiry framework motivated by a japanese clause generator capable of expressing levels of politeness makes this facility available for revealing the distinctions necessary among interpersonal, social meanings also. this paper shows why the previous inquiry framework was incapable of the kind of semantic control japanese politeness requires and how the implemented extension achieves that control. an example is given of the generation of a sentence that is appropriately polite for its context of use and some implications for future work are suggested.
translating named entities using monolingual and bilingual resources. named entity phrases are some of the most difficult phrases to translate because new phrases can appear from nowhere, and because many are domain specific, not to be found in bilingual dictionaries. we present a novel algorithm for translating named entity phrases using easily obtainable monolingual and bilingual resources. we report on the application and evaluation of this algorithm in translating arabic named entities to english. we also compare our results with the results obtained from human translations and a commercial system for the same task.
using aggregation for selecting content when generating referring expressions. previous algorithms for the generation of referring expressions have been developed specifically for this purpose. here we introduce an alternative approach based on a fully generic aggregation method also motivated for other generation tasks. we argue that the alternative contributes to a more integrated and uniform approach to content determination in the context of complete noun phrase generation.
distortion models for statistical machine translation. in this paper, we argue that n-gram language models are not sufficient to address word reordering required for machine translation. we propose a new distortion model that can be used with existing phrase-based smt decoders to address those n-gram language model limitations. we present empirical results in arabic to english machine translation that show statistically significant improvements when our proposed model is used. we also propose a novel metric to measure word order similarity (or difference) between any pair of languages based on word alignments.
features and agreement. this paper compares the consistency-based account of agreement phenomena in 'unification-based' grammars with an implication-based account based on a simple feature extension to lambek categorial grammar (lcg). we show that the lcg treatment accounts for constructions that have been recognized as problematic for 'unification-based' treatments.
using machine learning techniques to build a comma checker for basque. in this paper, we describe the research using machine learning techniques to build a comma checker to be integrated in a grammar checker for basque. after several experiments, and trained with a little corpus of 100,000 words, the system guesses correctly not placing commas with a precision of 96% and a recall of 98%. it also gets a precision of 70% and a recall of 49% in the task of placing commas. finally, we have shown that these results can be improved using a bigger and a more homogeneous corpus to train, that is, a bigger corpus written by one unique author.
grammatical analysis by computer of the lancaster-oslo/bergen (lob) corpus of british english texts. research has been under way at the unit for computer research on the english language at the university of lancaster, england, to develop a suite of computer programs which provide a detailed grammatical analysis of the lob corpus, a collection of about 1 million words of british english texts available in machine readable form.the first phrase of the project, completed in september 1983, produced a grammatically annotated version of the corpus giving a tag showing the word class of each word token. over 93 per cent of the word tags were correctly selected by using a matrix of tag pair probabilities and this figure was upgraded by a further 3 per cent by retagging problematic strings of words prior to disambiguation and by altering the probability weightings for sequences of three tags. the remaining 3 to 4 per cent were corrected by a human post-editor.the system was originally designed to run in batch mode over the corpus but we have recently modified procedures to run interactively for sample sentences typed in by a user at a terminal. we are currently extending the word tag set and improving the word tagging procedures to further reduce manual intervention. a similar probabilistic system is being developed for phrase and clause tagging.
an unsupervised system for identifying english inclusions in german text. we present an unsupervised system that exploits linguistic knowledge resources, namely english and german lexical databases and the world wide web, to identify english inclusions in german text. we describe experiments with this system and the corpus which was developed for this task. we report the classification results of our system and compare them to the performance of a trained machine learner in a series of in- and cross-domain experiments.
corpus-based lexical choice in natural language generation. choosing the best lexeme to realize a meaning in natural language generation is a hard task. we investigate different tree-based stochastic models for lexical choice. because of the difficulty of obtaining a sense-tagged corpus, we generalize the notion of synonymy. we show that a tree-based model can achieve a word-bag based accuracy of 90%, representing an improvement over the baseline.
jointly labeling multiple sequences: a factorial hmm approach. we present new statistical models for jointly labeling multiple sequences and apply them to the combined task of part-of-speech tagging and noun phrase chunking. the model is based on the factorial hidden markov model (fhmm) with distributed hidden states representing part-of-speech and noun phrase sequences. we demonstrate that this joint labeling approach, by enabling information sharing between tagging/chunking subtasks, out-performs the traditional method of tagging and chunking in succession. further, we extend this into a novel model, switching fhmm, to allow for explicit modeling of cross-sequence dependencies based on linguistic knowledge. we report tagging/chunking accuracies for varying dataset sizes and show that our approach is relatively robust to data sparsity.
a rote extractor with edit distance-based generalisation and multi-corpora precision calculation. in this paper, we describe a rote extractor that learns patterns for finding semantic relationships in unrestricted text, with new procedures for pattern generalization and scoring. these include the use of part-of-speech tags to guide the generalization, named entity categories inside the patterns, an edit-distance-based pattern generalization algorithm, and a pattern accuracy calculation procedure based on evaluating the patterns on several test corpora. in an evaluation with 14 entities, the system attains a precision higher than 50% for half of the relationships considered.
lexicon and grammar in probabilistic tagging of written english. the paper describes the development of software for automatic grammatical analysis of unrestricted, unedited english text at the unit for computer research on the english language (ucrel) at the university of lancaster. the work is currently funded by ibm and carried out in collaboration with colleagues at ibm uk (winchester) and ibm yorktown heights. the paper will focus on the lexicon component of the word tagging system, the ucrel grammar, the databanks of parsed sentences, and the tools that have been written to support development of these components. this work has applications to speech technology, spelling correction, and other areas of natural language processing. currently, our goal is to provide a language model using transition statistics to disambiguate alternative parses for a speech recognition device.
generalized algorithms for constructing statistical language models. recent text and speech processing applications such as speech mining raise new and more general problems related to the construction of language models. we present and describe in detail several new and efficient algorithms to address these more general problems and report experimental results demonstrating their usefulness. we give an algorithm for computing efficiently the expected counts of any sequence in a word lattice output by a speech recognizer or any arbitrary weighted automaton; describe a new technique for creating exact representations of n-gram language models by weighted automata whose size is practical for offline use even for a vocabulary size of about 500,000 words and an n-gram order n = 6; and present a simple and more general technique for constructing class-based language models that allows each class to represent an arbitrary weighted automaton. an efficient implementation of our algorithms and techniques has been incorporated in a general software library for language modeling, the grm library, that includes many other text and grammar processing functionalities.
corpus-based identification of non-anaphoric noun phrases. coreference resolution involves finding antecedents for anaphoric discourse entities, such as definite noun phrases. but many definite noun phrases are not anaphoric because their meaning can be understood from general world knowledge (e.g., "the white house" or "the news media"). we have developed a corpus-based algorithm for automatically identifying definite noun phrases that are non-anaphoric, which has the potential to improve the efficiency and accuracy of coreference resolution systems. our algorithm generates lists of non-anaphoric noun phrases and noun phrase patterns from a training corpus and uses them to recognize non-anaphoric noun phrases in new texts. using 1600 muc-4 terrorism news articles as the training corpus, our approach achieved 78% recall and 87% precision at identifying such noun phrases in 50 text documents.
toward a computational theory of speech perception. in recent years, a great deal of evidence has been collected which gives substantially increased insight into the nature of human speech perception. it is the author's belief that such data can be effectively used to infer much of the structure of a practical speech recognition system. this paper details a new view of the role of structural constraints within the several structural domains (e. g. articulation, phonetics, phonology, syntax, semantics) that must be utilized to infer the desired percept.
integrating multiple knowledge sources for detection and correction of repairs in human-computer dialog. we have analyzed 607 sentences of spontaneous human-computer speech data containing repairs, drawn from a total corpus of 10,718 sentences. we present here criteria and techniques for automatically detecting the presence of a repair, its location, and making the appropriate correction. the criteria involve integration of knowledge from several sources: pattern matching, syntactic and semantic analysis, and acoustics.
evaluating a focus-based approach to anaphora resolution. we present an approach to anaphora resolution based on a focusing algorithm, and implemented within an existing muc (message understanding conference) information extraction system, allowing quantitative evaluation against a substantial corpus of annotated real-world texts. extensions to the basic focusing mechanism can be easily tested, resulting in refinements to the mechanism and resolution rules. results show that the focusing algorithm is highly sensitive to the quality of syntactic-semantic analyses, when compared to a simpler heuristic-based approach.