waashk/dblp huggingface dataset:
error driven word sense disambiguation. in this paper we describe a method for performing word sense disambiguation (wsd). the method relies on unsupervised learning and exploits functional relations among words as produced by a shallow parser. by exploiting an error driven rule learning algorithm (brill 1997), the system is able to produce rules for wsd, which can be optionally edited by humans in order to increase the performance of the system.
a text input front-end processor as an information access platform. this paper presents a practical foreign language writing support tool which makes it much easier to utilize dictionary and example sentence resources. like a kana-kanji conversion front-end processor used to input japanese language text, this tool is also implemented as a front-end processor and can be combined with a wide variety of applications. a morphological analyzer automatically extracts key words from text as it is being input into the tool, and these words are used to locate information relevant to the input text. this information is then automatically displayed to the user. with this tool, users can concentrate better on their writing because much less interruption of their work is required for the consulting of dictionaries or for the retrieval of reference sentences. retrieval and display may be conducted in any three ways: 1) relevant information is retrieved and displayed automatically; 2) information is retrieved automatically but displayed only on user command; 3) information is both retrieved and displayed only on user command. the extent to which the retrieval and display of information proceeds automatically depends on the type of information being referenced; this element of the design adds to system efficiency. further, by combining this tool with a stepped-level interactive machine translation function, we have created a pc support tool to help japanese people write in english.
subdeletion in verb phrase ellipsis. this paper stems from an ongoing research project on verb phrase ellipsis. the project's goals are to implement a verb phrase ellipsis resolution algorithm, automatically test the algorithm on corpus data, then automatically evaluate the algorithm against human-generated answers. the paper will establish the current status of the algorithm based on this automatic evaluation, categorizing current problem situations. an algorithm to handle one of these problems, the case of subdeletion, will be described and evaluated. the algorithm attempts to detect and solve subdeletion by locating adjuncts of similar types in a verb phrase ellipsis and corresponding antecedent.
syntactic and semantic transfer with f-structures. we present two approaches for syntactic and semantic transfer based on lfg f-structures and compare the results with existing co-description and restriction operator based approaches, focusing on aspects of ambiguity preserving transfer, complex cases of syntactic structural mismatches as well as on modularity and reusability. the two transfer approaches are interfaced with an existing, implemented transfer component (verbmobil), by translating f-structures into a term language, and by interfacing f-structure representations with an existing semantic based transfer approach, respectively.
solving thematic divergences in machine translation. though most translation systems have some mechanism for translating certain types of divergent predicate-argument structures, they do not provide a general procedure that takes advantage of the relationship between lexical-semantic structure and syntactic structure. a divergent predicate-argument structure is one in which the predicate (e.g., the main verb) or its arguments (e.g., the subject and object) do not have the same syntactic ordering properties for both the source and target language. to account for such ordering differences, a machine translator must consider language-specific syntactic idiosyncrasies that distinguish a target language from a source language, while making use of lexical-semantic uniformities that tie the two languages together. this paper describes the mechanisms used by the unitran machine translation system for mapping an underlying lexical-conceptual structure to a syntactic structure (and vice versa), and it shows how these mechanisms coupled with a set of general linking routines solve the problem of thematic divergence in machine translation.
a parameterized approach to integrating aspect with lexical-semanics for machine translation. this paper discusses how a two-level knowledge representation model for machine translation integrates aspectual information with lexical-semantic information by means of parameterization. the integration of aspect with lexical-semantics is especially critical in machine translation because of the lexical selection and aspectual realization processes that operate during the production of the target-language sentence: there are often a large number of lexical and aspectual possibilities to choose from in the production of a sentence from a lexical semantic representation. aspectual information from the source-language sentence constrains the choice of target-language terms. in turn, the target-language terms limit the possibilities for generation of aspect. thus, there is a two-way communication channel between the two processes. this paper will show that the selection/realization processes may be parameterized so that they operate uniformly across more than one language and it will describe how the parameter-based approach is currently being used as the basis for extraction of aspectual information from corpora.
deriving verbal and compositional lexical aspect for nlp applications. verbal and compositional lexical aspect provide the underlying temporal structure of events. knowledge of lexical aspect, e.g., (a)telicity, is therefore required for interpreting event sequences in discourse (dowty, 1986; moens and steedman, 1988; passoneau, 1988), interfacing to temporal databases (androutsopoulos, 1996), processing temporal modifiers (antonisse, 1994), describing allowable alternations and their semantic effects (resnik, 1996; tenny, 1994), and selecting tense and lexical items for natural language generation ((dorr and olsen, 1996; klavans and chodorow, 1992), cf. (slobin and bocaz, 1988)). we show that it is possible to represent lexical aspect---both verbal and compositional---on a large scale, using lexical conceptual structure (lcs) representations of verbs in the classes cataloged by levin (1993). we show how proper consideration of these universal pieces of verb meaning may be used to refine lexical representations and derive a range of meanings from combinations of lcs representations. a single algorithm may therefore be used to determine lexical aspect classes and features at both verbal and sentence levels. finally, we illustrate how knowledge of lexical aspect facilitates the interpretation of events in nlp applications.
feature logic with weak subsumption constraints. in the general framework of a constraint-based grammar formalism often some sort of feature logic serves as the constraint language to describe linguistic objects. we investigate the extension of basic feature logic with subsumption (or matching) constraints, based on a weak notion of subsumption. this mechanism of one-way information flow is generally deemed to be necessary to give linguistically satisfactory descriptions of coordination phenomena in such formalisms. we show that the problem whether a set of constraints is satisfiable in this logic is decidable in polynomial time and give a solution algorithm.
parsing for semidirectional lambek grammar is np-complete. we study the computational complexity of the parsing problem of a variant of lambek categorial grammar that we call semidirectional. in semidirectional lambek calculus sdl there is an additional nondirectional abstraction rule allowing the formula abstracted over to appear anywhere in the premise sequent's left-hand side, thus permitting non-peripheral extraction. sdl grammars are able to generate each context-free language and more than that. we show that the parsing problem for semidirectional lambek grammar is np-complete by a reduction of the 3-partition problem.
efficient construction of underspecified semantics under massive ambiguity. we investigate the problem of determining a compact underspecified semantical representation for sentences that may be highly ambiguous. due to combinatorial explosion, the naive method of building semantics for the different syntactic readings independently is prohibitive. we present a method that takes as input a syntactic parse forest with associated constraint-based semantic construction rules and directly builds a packed semantic structure. the algorithm is fully implemented and runs in o(n4log(n)) in sentence length, if the grammar meets some reasonable 'normality' restrictions.
gemini: a natural language system for spoken-language understanding. gemini is a natural language understanding system developed for spoken language applications. this paper describes the details of the system, and includes relevant measurements of size, efficiency, and performance of each of its sub-components in detail.
practical issues in compiling typed unification grammars for speech recognition. current alternatives for language modeling are statistical techniques based on large amounts of training data, and hand-crafted context-free or finite-state grammars that are difficult to build and maintain. one way to address the problems of the grammar-based approach is to compile recognition grammars from grammars written in a more expressive formalism. while theoretically straight-forward, the compilation process can exceed memory and time bounds, and might not always result in accurate and efficient speech recognition. we will describe and evaluate two approaches to this compilation problem. we will also describe and evaluate additional techniques to reduce the structural ambiguity of the language model.
interleaving syntax and semantics in an effecient bottom-up parser. we describe an efficient bottom-up parser that interleaves syntactic and semantic structure building. two techniques are presented for reducing search by reducing local ambiguity: limited left context constraints are used to reduce local syntactic ambiguity, and deferred sortal-constraint application is used to reduce local semantic ambiguity. we experimentally evaluate these techniques, and show dramatic reductions in both number of chart edges and total parsing time. the robust processing capabilities of the parser are demonstrated in its use in improving the accuracy of a speech recognizer.
representing paraphrases using synchronous tags. this paper looks at representing paraphrases using the formalism of synchronous tags; it looks particularly at comparisons with machine translation and the modifications it is necessary to make to synchronous tags for paraphrasing. a more detailed version is in dras (1997a).
a meta-level grammar: redefining synchronous tag for translation and paraphrase. in applications such as translation and paraphrase, operations are carried out on grammars at the meta level. this paper shows how a meta-grammar, defining structure at the meta level, is useful in the case of such operations; in particular, how it solves problems in the current definition of synchronous tag (shieber, 1994) caused by ignoring such structure in mapping between grammars, for applications such as translation. moreover, essential properties of the formalism remain unchanged.
a bio-inspired approach for multi-word expression extraction. this paper proposes a new approach for multi-word expression (mwe)extraction on the motivation of gene sequence alignment because textual sequence is similar to gene sequence in pattern analysis. theory of longest common subsequence (lcs) originates from computer science and has been established as affine gap model in bioinformatics. we perform this developed lcs technique combined with linguistic criteria in mwe extraction. in comparison with traditional n-gram method, which is the major technique for mwe extraction, lcs approach is applied with great efficiency and performance guarantee. experimental results show that lcs-based approach achieves better results than n-gram.
what to do when lexicalization fails: parsing german with suffix analysis and smoothing. in this paper, we present an unlexicalized parser for german which employs smoothing and suffix analysis to achieve a labelled bracket f-score of 76.2, higher than previously reported results on the negra corpus. in addition to the high accuracy of the model, the use of smoothing in an unlexicalized parser allows us to better examine the interplay between smoothing and parsing results.
probabilistic parsing for german using sister-head dependencies. we present a probabilistic parsing model for german trained on the negra treebank. we observe that existing lexicalized parsing models using head-head dependencies, while successful for english, fail to outperform an unlexicalized baseline model for german. learning curves show that this effect is not due to lack of training data. we propose an alternative model that uses sister-head dependencies instead of head-head dependencies. this model out-performs the baseline, achieving a labeled precision and recall of up to 74%. this indicates that sister-head dependencies are more appropriate for treebanks with very flat structures such as negra.
integrating syntactic priming into an incremental probabilistic parser, with an application to psycholinguistic modeling. the psycholinguistic literature provides evidence for syntactic priming, i.e., the tendency to repeat structures. this paper describes a method for incorporating priming into an incremental probabilistic parser. three models are compared, which involve priming of rules between sentences, within sentences, and within coordinate structures. these models simulate the reading time advantage for parallel structures found in human data, and also yield a small increase in overall parsing accuracy.
empirically estimating order constraints for content planning in generation. in a language generation system, a content planner embodies one or more "plans" that are usually hand--crafted, sometimes through manual analysis of target text. in this paper, we present a system that we developed to automatically learn elements of a plan and the ordering constraints among them. as training data, we use semantically annotated transcripts of domain experts performing the task our system is designed to mimic. given the large degree of variation in the spoken language of the transcripts, we developed a novel algorithm to find parallels between transcripts based on techniques used in computational genomics. our proposed methodology was evaluated two--fold: the learning and generalization capabilities were quantitatively evaluated using cross validation obtaining a level of accuracy of 89%. a qualitative evaluation is also provided.
topological dependency trees: a constraint-based account of linear precedence. we describe a new framework for dependency grammar, with a modular decomposition of immediate dependency and linear precedence. our approach distinguishes two orthogonal yet mutually constraining structures: a syntactic dependency tree and a topological dependency tree. the syntax tree is nonprojective and even non-ordered, while the topological tree is projective and partially ordered.
interpreting the human genome sequence, using stochastic grammars. the 3 billion base pair sequence of the human genome is now available, and attention is focusing on annotating it to extract biological meaning. i will discuss what we have obtained, and the methods that are being used to analyse biological sequences. in particular i will discuss approaches using stochastic grammars analogous to those used in computational linguistics, both for gene finding and protein family classification.
a noisy-channel approach to question answering. we introduce a probabilistic noisy-channel model for question answering and we show how it can be exploited in the context of an end-to-end qa system. our noisy-channel system outperforms a state-of-the-art rule-based qa system that uses similar resources. we also show that the model we propose is flexible enough to accommodate within one mathematical framework many qa-specific resources and techniques, which range from the exploitation of wordnet, structured, and semi-structured databases to reasoning, and paraphrasing.
towards a modular data model for multi-layer annotated corpora. in this paper we discuss the current methods in the representation of corpora annotated at multiple levels of linguistic organization (so-called multi-level or multi-layer corpora). taking five approaches which are representative of the current practice in this area, we discuss the commonalities and differences between them focusing on the underlying data models. the goal of the paper is to identify the common concerns in multi-layer corpus representation and processing so as to lay a foundation for a unifying, modular data model.
choosing the word most typical in context using a lexical co-occurrence network. this paper presents a partial solution to a component of the problem of lexical choice: choosing the synonym most typical, or expected, in context. we apply a new statistical approach to representing the context of a word through lexical co-occurrence networks. the implementation was trained and evaluated on a large corpus, and results show that the inclusion of second-order co-occurrence relations improves the performance of our implemented lexical choice program.
constraints over lambda-structures in semantic underspecification. we introduce a first-order language for semantic underspecification that we call constraint language for lambda-structures (clls). a &lambda;-structure can be considered as a &lambda;-term up to consistent renaming of bound variables (&lambda;-equality); a constraint of clls is an underspecified description of a &lambda;-structure. clls solves a capturing problem omnipresent in underspecified scope representations. clls features constraints for dominance, lambda binding, parallelism, and anaphoric links. based on clls we present a simple, integrated, and underspecified treatment of scope, parallelism, and anaphora.
unification of disjunctive feature descriptions. the paper describes a new implementation of feature structures containing disjunctive values, which can be characterized by the following main points: local representation of embedded disjunctions, avoidance of expansion to disjunctive normal form and of repeated test-unifications for checking consistence. the method is based on a modification of kasper and rounds' calculus of feature descriptions and its correctness therefore is easy to see. it can handle cyclic structures and has been incorporated successfully into an environment for grammar development.
parameter estimation for probabilistic finite-state transducers. weighted finite-state transducers suffer from the lack of a training algorithm. training is even harder for transducers that have been assembled via finite-state operations such as composition, minimization, union, concatenation, and closure, as this yields tricky parameter tying. we formulate a "parameterized fst" paradigm and give training algorithms for it, including a general bookkeeping trick ("expectation semirings") that cleanly and efficiently computes expectations and gradients.
efficient normal-form parsing for combinatory categorial grammar. under categorial grammars that have powerful rules like composition, a simple n-word sentence can have exponentially many parses. generating all parses is inefficient and obscures whatever true semantic ambiguities are in the input. this paper addresses the problem for a fairly general form of combinatory categorial grammar, by means of an efficient, correct, and easy to implement normal-form parsing technique. the parser is proved to find exactly one parse in each semantic equivalence class of allowable parses; that is, spurious ambiguity (as carefully defined) is shown to be both safely and completely eliminated.
efficient generation in primitive optimality theory. this paper introduces primitive optimality theory (otp), a linguistically motivated formalization of ot. otp specifies the class of autosegmental representations, the universal generator gen, and the two simple families of permissible constraints. in contrast to less restricted theories using generalized alignment, otp's optimal surface forms can be generated with finite-state methods adapted from (ellison, 1994). unfortunately these methods take time exponential on the size of the grammar. indeed the generation problem is shown np-complete in this sense. however, techniques are discussed for making ellison's approach fast in the typical case, including a simple trick that alone provides a 100-fold speedup on a grammar fragment of moderate size. one avenue for future improvements is a new finite-state notion, "factored automata," where regular languages are represented compactly via formal intersections &cap;ki=1ai of fsas.
efficient parsing for bilexical context-free grammars and head automaton grammars. several recent stochastic parsers use <i>bilexical</i> grammars, where each word type idiosyncratically prefers particular complements with particular head words. we present <i>o(n</i><sup>4</sup>) parsing algorithms for two bilexical formalisms, improving the prior upper bounds of <i>o(n</i><sup>5</sup>). for a common special case that was known to allow <i>o(n</i><sup>3</sup>) parsing (eisner, 1997), we present an <i>o(n</i><sup>3</sup>) algorithm with an improved grammar constant.
a modified joint source-channel model for transliteration. most machine transliteration systems transliterate out of vocabulary (oov) words through intermediate phonemic mapping. a framework has been presented that allows direct orthographical mapping between two languages that are of different origins employing different alphabet sets. a modified joint source-channel model along with a number of alternatives have been proposed. aligned transliteration units along with their context are automatically derived from a bilingual training corpus to generate the collocational statistics. the transliteration units in bengali words take the pattern c+m where c represents a vowel or a consonant or a conjunct and m represents the vowel modifier or matra. the english transliteration units are of the form c*v* where c represents a consonant and v represents a vowel. a bengali-english machine transliteration system has been developed based on the proposed models. the system has been trained to transliterate person names from bengali to english. it uses the linguistic knowledge of possible conjuncts and diphthongs in bengali and their equivalents in english. the system has been evaluated and it has been observed that the modified joint source-channel model performs best with a word agreement ratio of 69.3% and a transliteration unit agreement ratio of 89.8%.
types in functional unification grammars. functional unification grammars (fugs) are popular for natural language applications because the formalism uses very few primitives and is uniform and expressive. in our work on text generation, we have found that it also has annoying limitations: it is not suited for the expression of simple, yet very common, taxonomic relations and it does not allow the specification of completeness conditions. we have implemented an extension of traditional functional unification. this extension addresses these limitations while preserving the desirable properties of fugs. it is based on the notions of typed features and typed constituents. we show the advantages of this extension in the context of a grammar used for text generation.