waashk/dblp huggingface dataset:
lexnet: a graphical environment for graph-based nlp. this interactive presentation describes lexnet, a graphical environment for graph-based nlp developed at the university of michigan. lexnet includes lexrank (for text summarization), biased lexrank (for passage retrieval), and tumbl (for binary classification). all tools in the collection are based on random walks on lexical graphs, that is graphs where different nlp objects (e.g., sentences or phrases) are represented as nodes linked by edges proportional to the lexical similarity between the two nodes. we will demonstrate these tools on a variety of nlp tasks including summarization, question answering, and prepositional phrase attachment.
utilizing co-occurrence of answers in question answering. in this paper, we discuss how to utilize the co-occurrence of answers in building and automatic question answering system that answers a series of questions on a specific topic in a batch mode. experiments show that the answers to the many of the questions in the series usually have a high degree of co-occurrence in relevant document passages. this feature sometimes can't be easily utilized in an automatic qa system which processes questions independently. however it can be utilized in a qa system that processes questions in a batch mode. we have used our pervious trec qa system as baseline and augmented it with new answer clustering and co-occurrence maximization components to build the batch qa system. the experiment results show that the qa system running under the batch mode get significant performance improvement over our baseline trec qa system.
reference resolution beyond coreference: a conceptual frame and its application. a model for reference use in communication is proposed, from a representationist point of view. both the sender and the receiver of a message handle representations of their common environment, including mental representations of objects. reference resolution by a computer is viewed as the construction of object representations using referring expressions from the discourse, whereas often only coreference links between such expressions are looked for. differences between these two approaches are discussed.the model has been implemented with elementary rules, and tested on complex narrative texts (hundreds to thousands of referring expressions). the results support the mental representations paradigm.
discriminative language modeling with conditional random fields and the perceptron algorithm. this paper describes discriminative language modeling for a large vocabulary speech recognition task. we contrast two parameter estimation methods: the perceptron algorithm, and a method based on conditional random fields (crfs). the models are encoded as deterministic weighted finite state automata, and are applied by intersecting the automata with word-lattices that are the output from a baseline recognizer. the perceptron algorithm has the benefit of automatically selecting a relatively small feature set in just a couple of passes over the training data. however, using the feature set output from the perceptron algorithm (initialized with their weights), crf training provides an additional 0.5% reduction in word error rate, for a total 1.8% absolute reduction from the baseline of 39.2%.
tree unification grammar. tree unification grammar is a declarative unification-based linguistic framework. the basic grammar structures of this framework are partial descriptions of trees, and the framework requires only a single grammar rule to combine these partial descriptions. using this framework, constraints associated with various linguistic phenomena (reflexivisation in particular) can be stated succinctly in the lexicon.
learning meronyms from biomedical text. the part-whole relation is of special importance in biomedicine: structure and process are organised along partitive axes. anatomy, for example, is rich in part-whole relations. this paper reports preliminary experiments on part-whole extraction from a corpus of anatomy definitions, using a fully automatic iterative algorithm to learn simple lexico-syntactic patterns from multiword terms. the experiments show that meronyms can be extracted using these patterns. a failure analysis points out factors that could contribute to improvements in both precision and recall, including pattern generalisation, pattern pruning, and term matching. the analysis gives insights into the relationship between domain terminology and lexical relations, and into evaluation strategies for relation learning.
learning information structure in the prague treebank. this paper investigates the automatic identification of aspects of information structure (is) in texts. the experiments use the prague dependency treebank which is annotated with is following the praguian approach of topic focus articulation. we automatically detect t(opic) and f(ocus), using node attributes from the treebank as basic features and derived features inspired by the annotation guidelines. we show the performance of c4.5, bagging, and ripper classifiers on several classes of instances such as nouns and pronouns, only nouns, only pronouns. a baseline system assigning always f(ocus) has an f-score of 42.5%. our best system obtains 82.04%.
evaluating the portability of revision rules for incremental summary generation. this paper presents a quantitative evaluation of the portability to the stock market domain of the revision rule hierarchy used by the system streak to incrementally generate newswire sports summaries. the evaluation consists of searching a test corpus of stock market reports for sentence pairs whose (semantic and syntactic) structures respectively match the triggering condition and application result of each revision rule. the results show that at least 59% of all rule classes are fully portable, with at least another 7% partially portable.
multilingual authoring using feedback texts. there are obvious reasons for trying to automate the production of multilingual documentation, especially for routine subject-matter in restricted domains (e.g. technical instructions). two approaches have been adopted: machine translation (mt) of a source text, and multilingual natural language generation (m-nlg) from a knowledge base. for mt, information extraction is a major difficulty, since the meaning must be derived by analysis of the source text; m-nlg avoids this difficulty but seems at first sight to require an expensive phase of knowledge engineering in order to encode the meaning. we introduce here a new technique which employs m-nlg during the phase of knowledge editing. a 'feedback text', generated from a possibly incomplete knowledge base, describes in natural language the knowledge encoded so far, and the options for extending it. this method allows anyone speaking one of the supported languages to produce texts in all of them, requiring from the author only expertise in the subject-matter, not expertise in knowledge engineering.
an efficient generation algorithm for lexicalist mt. the lexicalist approach to machine translation offers significant advantages in the development of linguistic descriptions. however, the shake-and-bake generation algorithm of (whitelock, 1992) is np-complete. we present a polynomial time algorithm for lexicalist mt generation provided that sufficient information can be transferred to ensure more determinism.
evaluation challenges in large-scale document summarization. we present a large-scale meta evaluation of eight evaluation measures for both single-document and multi-document summarizers. to this end we built a corpus consisting of (a) 100 million automatic summaries using six summarizers and baselines at ten summary lengths in both english and chinese, (b) more than 10,000 manual abstracts and extracts, and (c) 200 million automatic document and summary retrievals using 20 queries. we present both qualitative and quantitative results showing the strengths and draw-backs of all evaluation methods and how they rank the different summarizers.
practical glossing by prioritised tining. we present the design of a practical context-sensitive glosser, incorporating current techniques for lightweight linguistic analysis based on large-scale lexical resources. we outline a general model for ranking the possible translations of the words and expressions that make up a text. this information can be used by a simple resource-bounded algorithm, of complexity o(n log n) in sentence length, that determines a consistent gloss of best translations. we then describe how the results of the general ranking model may be approximated using a simple heuristic prioritisation scheme. finally we present a preliminary evaluation of the glosser's performance.
semantic role labeling using different syntactic views. semantic role labeling is the process of annotating the predicate-argument structure in text with semantic labels. in this paper we present a state-of-the-art baseline semantic role labeling system based on support vector machine classifiers. we show improvements on this system by: i) adding new features including features extracted from dependency parses, ii) performing feature selection and calibration and iii) combining parses obtained from semantic parsers trained using different syntactic views. error analysis of the baseline system showed that approximately half of the argument identification errors resulted from parse errors in which there was no syntactic constituent that aligned with the correct argument. in order to address this problem, we combined semantic parses from a minipar syntactic parse and from a chunked syntactic representation with our original baseline system which was based on charniak parses. all of the reported techniques resulted in performance improvements.
question answering using constraint satisfaction: qa-by-dossier-with-contraints. qa-by-dossier-with-constraints is a new approach to question answering whereby candidate answers' confidences are adjusted by asking auxiliary questions whose answers constrain the original answers. these constraints emerge naturally from the domain of interest, and enable application of real-world knowledge to qa. we show that our approach significantly improves system performance (75% relative improvement in f-measure on select question types) and can create a "dossier" of information about the subject matter in the original question.
a morphologically sensitive clustering algorithm for identifying arabic roots. we present a clustering algorithm for arabic words sharing the same root. root based clusters can substitute dictionaries in indexing for ir. modifying adamson and boreham (1974), our two-stage algorithm applies light stemming before calculating word pair similarity coefficients using techniques sensitive to arabic morphology. tests show a successful treatment of infixes and accurate clustering to up to 94.06% for unedited arabic text samples, without the use of dictionaries.
improving qa accuracy by question inversion. this paper demonstrates a conceptually simple but effective method of increasing the accuracy of qa systems on factoid-style questions. we define the notion of an inverted question, and show that by requiring that the answers to the original and inverted questions be mutually consistent, incorrect answers get demoted in confidence and correct ones promoted. additionally, we show that lack of validation can be used to assert no-answer (nil) conditions. we demonstrate increases of performance on trec and other question-sets, and discuss the kinds of future activities that can be particularly beneficial to approaches such as ours.
unsupervised learning of arabic stemming using a parallel corpus. this paper presents an unsupervised learning approach to building a non-english (arabic) stemmer. the stemming model is based on statistical machine translation and it uses an english stemmer and a small (10 k sentences) parallel corpus as its sole training resources. no parallel text is needed after the training phase. monolingual, unannotated text can be used to further improve the stemmer by allowing it to adapt to a desired domain or genre. examples and results will be given for arabic, but the approach is applicable to any language that needs affix removal. our resource-frugal approach results in 87.5% agreement with a state of the art, proprietary arabic stemmer built using rules, affix lists, and human annotated text, in addition to an unsupervised component. task-based evaluation using arabic information retrieval indicates an improvement of 22-38% in average precision over unstemmed text, and 96% of the performance of the proprietary stemmer above.
an information structural approach to spoken language generation. this paper presents an architecture for the generation of spoken monologues with contextually appropriate intonation. a two-tiered information structure representation is used in the high-level content planning and sentence planning stages of generation to produce efficient, coherent speech that makes certain discourse relationships, such as explicit contrasts, appropriately salient. the system is able to produce appropriate intonational patterns that cannot be generated by other systems which rely solely on word class and given/new distinctions.
wrapping of trees. we explore the descriptive power, in terms of syntactic phenomena, of a formalism that extends tree-adjoining grammar (tag) by adding a fourth level of hierarchical decomposition to the three levels tag already employs. while extending the descriptive power minimally, the additional level of decomposition allows us to obtain a uniform account of a range of phenomena that has heretofore been difficult to encompass, an account that employs unitary elementary structures and eschews synchronized derivation operations, and which is, in many respects, closer to the spirit of the intuitions underlying tag-based linguistic theory than previously considered extensions to tag.
capturing cfls with tree adjoining grammars. we define a decidable class of tags that is strongly equivalent to cfgs and is cubic-time parsable. this class serves to lexicalize cfgs in the same manner as the lcfgs of schabes and waters but with considerably less restriction on the form of the grammars. the class provides a normal form for tags that generate local sets in much the same way that regular grammars provide a normal form for cfgs that generate regular sets.
an intelligent multi-dictionary environment. an open, extendible multi-dictionary system is introduced in the paper. it supports the translator in accessing adequate entries of various bi- and monolingual dictionaries and translation examples from parallel corpora. simultaneously an unlimited number of dictionaries can be held open, thus by a single interrogation step, all the dictionaries (translations, explanations, synonyms, etc.) can be surveyed. the implemented system (called mobidic) knows morphological rules of the dictionaries' languages. thus, never the actual (inflected) words, but always their lemmas-that is, the right dictionary entries-are looked up. mobidic has an open, multimedial architecture, thus it is suitable for handling not only textual, but speaking or picture dictionaries, as well. the same system is also able to find words and expressions in corpora, dynamically providing the translators with examples from their earlier translations or other translators' works. mobidic has been designed for translator workgroups, where the translators' own glossaries (built also with the help of the system) may also be disseminated among the members of the group, with different access rights, if needed. the system has a tcp/ip-based client-server implementation for various platforms and available with a gradually increasing number of dictionaries for numerous language pairs.
a model-theoretic framework for theories of syntax. a natural next step in the evolution of constraint-based grammar formalisms from rewriting formalisms is to abstract fully away from the details of the grammar mechanism---to express syntactic theories purely in terms of the properties of the class of structures they license. by focusing on the structural properties of languages rather than on mechanisms for generating or checking structures that exhibit those properties, this model-theoretic approach can offer simpler and significantly clearer expression of theories and can potentially provide a uniform formalization, allowing disparate theories to be compared on the basis of those properties. we discuss l2k, p, a monadic second-order logical framework for such an approach to syntax that has the distinctive virtue of being superficially expressive---supporting direct statement of most linguistically significant syntactic properties---but having well-defined strong generative capacity---languages are definable in l2k, p iff they are strongly context-free. we draw examples from the realms of gpsg and gb.
a unification-based approach to morpho-syntactic parsing of agglutinative and other (highly) inflectional languages. this paper introduces a new approach to morpho-syntactic analysis through humor 99 (<u>h</u>igh-speed <u>u</u>nification <u>mor</u>phology), a reversible and unification-based morphological analyzer which has already been integrated with a variety of industrial applications. humor 99 successfully copes with problems of agglutinative (e.g. hungarian, turkish, estonian) and other (highly) inflectional languages (e.g. polish, czech, german) very effectively. the authors conclude the paper by arguing that the approach used in humor 99 is general enough to be well suitable for a wide range of languages, and can serve as basis for higher-level linguistic operations such as shallow parsing.
a descriptive characterization of tree-adjoining languages (project note). since the early sixties and seventies it has been known that the regular and context-free languages are characterized by definability in the monadic second-order theory of certain structures. more recently, these descriptive characterizations have been used to obtain complexity results for constraint- and principle-based theories of syntax and to provide a uniform model-theoretic framework for exploring the relationship between theories expressed in disparate formal terms. these results have been limited, to an extent, by the lack of descriptive characterizations of language classes beyond the context-free. recently, we have shown that tree-adjoining languages (in a mildly generalized form) can be characterized by recognition by automata operating on three-dimensional tree manifolds, a three-dimensional analog of trees. in this paper, we exploit these automata-theoretic results to obtain a characterization of the tree-adjoining languages by definability in the monadic second-order theory of these three-dimensional tree manifolds. this not only opens the way to extending the tools of model-theoretic syntax to the level of tals, but provides a highly flexible mechanism for defining tags in terms of logical constraints.
parsing head-driven phrase structure grammar. the head-driven phrase structure grammar project (hpsg) is an english language database query system under development at hewlett-packard laboratories. unlike other product-oriented efforts in the natural language understanding field, the hpsg system was designed and implemented by linguists on the basis of recent theoretical developments. but, unlike other implementations of linguistic theories, this system is not a toy, as it deals with a variety of practical problems not covered in the theoretical literature. we believe that this makes the hpsg system unique in its combination of linguistic theory and practical application.the hpsg system differs from its predecessor gpsg, reported on at the 1982 acl meeting (gawron et al. ([1982]), in four significant respects: syntax, lexical representation, parsing, and semantics. the paper focuses on parsing issues, but also gives a synopsis of the underlying syntactic formalism.
reasoning with descriptions of trees. in this paper we introduce a logic for describing trees which allows us to reason about both the parent and domination relationship. the use of domination has found a number of applications, such as in deterministic parsers based on description theory (marcus, hindle & fleck, 1983), in a compact organization of the basic structures of tree-adjoining grammars (vijay-shanker & schabes, 1992), and in a new characterization of the adjoining operation that allows a clean integration of tags into the unification-based framework (vijay-shanker, 1992) our logic serves to formalize the reasoning on which these applications are based.
a generative lexicon perspective for adjectival modification. this paper presents a semantic interpretation of adjectival modification in terms of the generative lexicon. it highlights the elements which can be borrowed from the gl and develops limitations and extensions. we show how elements of the qualia structure can be incorporated into semantic composition rules to make explicit the semantics of the combination adjective + noun.
how verb subcategorization frequencies are affected by corpus choice. the probabilistic relation between verbs and their arguments plays an important role in modern statistical parsers and supertaggers, and in psychological theories of language processing. but these probabilities are computed in very different ways by the two sets of researchers. computational linguists compute verb subcategorization probabilities from large corpora while psycholinguists compute them from psychological studies (sentence production and completion tasks). recent studies have found differences between corpus frequencies and psycholinguistic measures. we analyze subcategorization frequencies from four different corpora: psychological sentence production data (connine et al. 1984), written text (brown and wsj), and telephone conversation data (switchboard). we find two different sources for the differences. discourse influence is a result of how verb use is affected by different discourse types such as narrative, connected discourse, and single sentence productions. semantic influence is a result of different corpora using different senses of verbs, which have different subcategorization frequencies. we conclude that verb sense and discourse type play an important role in the frequencies observed in different experimental and corpus based sources of verb subcategorization frequencies.
classifying semantic relations in bioscience texts. a crucial step toward the goal of automatic extraction of propositional information from natural language text is the identification of semantic relations between constituents in sentences. we examine the problem of distinguishing among seven relation types that can occur between the entities "treatment" and "disease" in bioscience text, and the problem of identifying such entities. we compare five generative graphical models and a neural network, using lexical, syntactic, and semantic features, finding that the latter help achieve high classification accuracy.
the descent of hierarchy, and selection in relational semantics. in many types of technical texts, meaning is embedded in noun compounds. a language understanding program needs to be able to interpret these in order to ascertain sentence meaning. we explore the possibility of using an existing lexical hierarchy for the purpose of placing words from a noun compound into categories, and then using this category membership to determine the relation that holds between the nouns. in this paper we present the results of an analysis of this method on two-word noun compounds from the biomedical domain, obtaining classification accuracy of approximately 90%. since lexical hierarchies are not necessarily ideally suited for this task, we also pose the question: how far down the hierarchy must the algorithm descend before all the terms within the subhierarchy behave uniformly with respect to the semantic relation in question? we find that the topmost levels of the hierarchy yield an accurate classification, thus providing an economic way of assigning relations to noun compounds.