waashk/dblp huggingface dataset:
aligning words using matrix factorisation. aligning words from sentences which are mutual translations is an important problem in different settings, such as bilingual terminology extraction, machine translation, or projection of linguistic features. here, we view word alignment as matrix factorisation. in order to produce proper alignments, we show that factors must satisfy a number of constraints such as orthogonality. we then propose an algorithm for orthogonal non-negative matrix factorisation, based on a probabilistic model of the alignment data, and apply it to word alignment. this is illustrated on a french-english alignment task from the hansard.
an algorithm for vp ellipsis. an algorithm is proposed to determine antecedents for vp ellipsis. the algorithm eliminates impossible antecedents, and then imposes a preference ordering on possible antecedents. the algorithm performs with 94% accuracy on a set of 304 examples of vp ellipsis collected from the brown corpus. the problem of determining antecedents for vp ellipsis has received little attention in the literature, and it is shown that the current proposal is a significant improvement over alternative approaches.
parsing aligned parallel corpus by projecting syntactic relations from annotated source corpus. example-based parsing has already been proposed in literature. in particular, attempts are being made to develop techniques for language pairs where the source and target languages are different, e.g. direct projection algorithm (hwa et al., 2005). this enables one to develop parsed corpus for target languages having fewer linguistic tools with the help of a resource-rich source language. the dpa algorithm works on the assumption of direct correspondence which simply means that the relation between two words of the source language sentence can be projected directly between the corresponding words of the parallel target language sentence. however, we find that this assumption does not hold good all the time. this leads to wrong parsed structure of the target language sentence. as a solution we propose an algorithm called pseudo dpa (pdpa) that can work even if direct correspondence assumption is not guaranteed. the proposed algorithm works in a recursive manner by considering the embedded phrase structures from outermost level to the innermost. the present work discusses the pdpa algorithm, and illustrates it with respect to english-hindi language pair. link grammar based parsing has been considered as the underlying parsing scheme for this work.
polyphony and argumentative semantics. we extract from sentences a superstructure made of argumentative operators and connectives applying to the remaining set of terminal sub-sentences. we found the argumentative interpretation of utterances on a semantics defined at the linguistic level. we describe the computation of this particular semantics, based on the constraints that the superstructure impels to the argumentative power of terminal subsentences.
generation of vp ellipsis: a corpus-based approach. we present conditions under which verb phrases are elided based on a corpus of positive and negative examples. factor that affect verb phrase ellipsis include: the distance between antecedent and ellipsis site, the syntactic relation between antecedent and ellipsis site, and the presence or absence of adjuncts. building on these results, we examine where in the generation architecture a trainable algorithm for vp ellipsis should be located. we show that the best performance is achieved when the trainable module is located after the realizer and has access to surface-oriented features (error rate of 7.5%).
normal state implicature. in the right situation, a speaker can use an unqualified indefinite description without being misunderstood. this use of language, normal state implicature, is a kind of conversational implicature, i.e. a non-truth-functional context-dependent inference based upon language users' awareness of principles of cooperative conversation. i present a convention for identifying normal state implicatures which is based upon mutual beliefs of the speaker and hearer about certain properties of the speaker's plan. a key property is the precondition that an entity playing a role in the plan must be in a normal state with respect to the plan.
data-driven strategies for an automated dialogue system. we present a prototype natural-language problem-solving application for a financial services call center, developed as part of the amiti&eacute;s multilingual human-computer dialogue project. our automated dialogue system, based on empirical evidence from real call-center conversations, features a data-driven approach that allows for mixed system/customer initiative and spontaneous conversation. preliminary evaluation results indicate efficient dialogues and high user satisfaction, with performance comparable to or better than that of current conversational travel information systems.
local constraints on sentence markers and focus in somali. we present a computationally tractable account of the interactions between sentence markers and focus marking in somali. somali, as a cushitic language, has a basic pattern wherein a small 'core' clause is preceded, and in some cases followed by, a set of 'topics', which provide scene-seting information against which the core is interpreted. some topics appear to carry a 'focus marker', indicating that they are particularly salient. we will outline a computationally tractable grammar for somali in which focus marking emerges naturally from a consideration of the use of a range of sentence markers.
conversational implicatures in indirect replies. in this paper we present algorithms for the interpretation and generation of a kind of particularized conversational implicature occurring in certain indirect replies. our algorithms make use of discourse expectations, discourse plans, and discourse relations. the algorithms calculate implicatures of discourse units of one or more sentences. our approach has several advantages. first, by taking discourse relations into account, it can capture a variety of implicatures not handled before. second, by treating implicatures of discourse units which may consist of more than one sentence, it avoids the limitations of a sentence-at-a-time approach. third, by making use of properties of discourse which have been used in models of other discourse phenomena, our approach can be integrated with those models. also, our model permits the same information to be used both in interpretation and generation.
designer definites in logical form. in this paper, we represent singular definite noun phrases as functions in logical form. this representation is designed to model the behaviors of both anaphoric and non-anaphoric, distributive definites. it is also designed to obey the computational constraints suggested in harper [har88]. our initial representation of a definite places an upper bound on its behavior given its structure and location in a sentence. later, when ambiguity is resolved, the precise behavior of the definite is pinpointed.
a hybrid reasoning model for indirect answers. this paper presents our implemented computational model for interpreting and generating indirect answers to yes-no questions. its main features are 1) a discourse-plan-based approach to implicature, 2) a reversible architecture for generation and interpretation, 3) a hybrid reasoning model that employs both plan inference and logical inference, and 4) use of stimulus conditions to model a speaker's motivation for providing appropriate, unrequested information. the model handles a wider range of types of indirect answers than previous computational models and has several significant advantages.
inducing frame semantic verb classes from wordnet and ldoce. this paper presents semframe, a system that induces frame semantic verb classes from wordnet and ldoce. semantic frames are thought to have significant potential in resolving the paraphrase problem challenging many language-based applications.when compared to the handcrafted framenet, semframe achieves its best recall-precision balance with 83.2% recall (based on semframe's coverage of framenet frames) and 73.8% precision (based on semframe verbs' semantic relatedness to frame-evoking verbs). the next best performing semantic verb classes achieve 56.9% recall and 55.0% precision.
mapping lexical entries in a verbs database to wordnet senses. this paper describes automatic techniques for mapping 9611 entries in a database of english verbs to wordnet senses. the verbs were initially grouped into 491 classes based on syntactic features. mapping these verbs into wordnet senses provides a resource that supports disambiguation in multilingual applications such as machine translation and cross-language information retrieval. our techniques make use of (1) a training set of 1791 disambiguated entries, representing 1442 verb entries from 167 classes; (2) word sense probabilities, from frequency counts in a tagged corpus; (3) semantic similarity of wordnet senses for verbs within the same class; (4) probabilistic correlations between wordnet data and attributes of the verb classes. the best results achieved 72% precision and 58% recall, versus a lower bound of 62% precision and 38% recall for assigning the most frequently occurring wordnet sense, and an upper bound of 87% precision and 75% recall for human judgment.
semi-supervised conditional random fields for improved sequence segmentation and labeling. we present a new semi-supervised training procedure for conditional random fields (crfs) that can be used to train sequence segmentors and labelers from a combination of labeled and unlabeled training data. our approach is based on extending the minimum entropy regularization framework to the structured prediction case, yielding a training objective that combines unlabeled conditional entropy with labeled conditional likelihood. although the training objective is no longer concave, it can still be used to improve an initial model (e.g. obtained from supervised training) by iterative ascent. we apply our new training algorithm to the problem of identifying gene and protein mentions in biological texts, and show that incorporating unlabeled data improves the performance of the supervised crf in this case.
analysis and repair of name tagger errors. name tagging is a critical early stage in many natural language processing pipelines. in this paper we analyze the types of errors produced by a tagger, distinguishing name classification and various types of name identification errors. we present a joint inference model to improve chinese name tagging by incorporating feedback from subsequent stages in an information extraction pipeline: name structure parsing, cross-document coreference, semantic relation extraction and event extraction. we show through examples and performance measurement how different stages can correct different types of errors. the resulting accuracy approaches that of individual human annotators.
sextant: exploring unexplored contexts for semantic extraction from syntactic analysis. for a very long time, it has been considered that the only way of automatically extracting similar groups of words from a text collection for which no semantic information exists is to use document co-occurrence data. but, with robust syntactic parsers that are becoming more frequently available, syntactically recognizable phenomena about word usage can be confidently noted in large collections of texts. we present here a new system called sextant which uses these parsers and the finer-grained contexts they produce to judge word similarity.
a collaborative framework for collecting thai unknown words from the web. we propose a collaborative framework for collecting thai unknown words found on web pages over the internet. our main goal is to design and construct a web-based system which allows a group of interested users to participate in constructing a thai unknown-word open dictionary. the proposed framework provides supporting algorithms and tools for automatically identifying and extracting unknown words from web pages of given urls. the system yields the result of unknown-word candidates which are presented to the users for verification. the approved unknown words could be combined with the set of existing words in the lexicon to improve the performance of many nlp tasks such as word segmentation, information retrieval and machine translation. our framework includes word segmentation and morphological analysis modules for handling the non-segmenting characteristic of thai written language. to take advantage of large available text resource on the web, our unknown-word boundary identification approach is based on the statistical string pattern-matching algorithm.
using conditional random fields to predict pitch accents in conversational speech. the detection of prosodic characteristics is an important aspect of both speech synthesis and speech recognition. correct placement of pitch accents aids in more natural sounding speech, while automatic detection of accents can contribute to better word-level recognition and better textual understanding. in this paper we investigate probabilistic, contextual, and phonological factors that influence pitch accent placement in natural, conversational speech in a sequence labeling setting. we introduce conditional random fields (crfs) to pitch accent prediction task in order to incorporate these factors efficiently in a sequence model. we demonstrate the usefulness and the incremental effect of these factors in a sequence model by performing experiments on hand labeled data from the switchboard corpus. our model outperforms the baseline and previous models of pitch accent prediction on the switch-board corpus.
mistake-driven mixture of hierarchical tag context trees. this paper proposes a mistake-driven mixture method for learning a tag model. the method iteratively performs two procedures: 1. constructing a tag model based on the current data distribution and 2. updating the distribution by focusing on data that are not well predicted by the constructed model. the final tag model is constructed by mixing all the models according to their performance. to well reflect the data distribution, we represent each tag model as a hierarchical tag (i.e., ntt <proper noun <noun) context tree. by using the hierarchical tag context tree, the constituents of sequential tag models gradually change from broad coverage tags (e.g., noun) to specific exceptional words that cannot be captured by general tags. in other words, the method incorporates not only frequent connetions but also infrequent ones that are often considered to be collocational. we evaluate several tag models by implementing japanese part-of-speech taggers that share all other conditions (i.e., dictionary and word model) other than their tag models. the experimental results show the proposed method significantly outperforms both hand-crafted and conventional statistical methods.
unsupervised learning of field segmentation models for information extraction. the applicability of many current information extraction techniques is severely limited by the need for supervised training data. we demonstrate that for certain field structured extraction tasks, such as classified advertisements and bibliographic citations, small amounts of prior knowledge can be used to learn effective models in a primarily unsupervised fashion. although hidden markov models (hmms) provide a suitable generative model for field structured text, general unsupervised hmm learning fails to learn useful structure in either of our domains. however, one can dramatically improve the quality of the learned structure by exploiting simple prior knowledge of the desired solutions. in both domains, we found that unsupervised methods can attain accuracies with 400 unlabeled examples comparable to those attained by supervised methods on 50 labeled examples, and that semi-supervised methods can make good use of small amounts of labeled data.
using decision trees to construct a practical parser. this paper describes novel and practical japanese parsers that uses decision trees. first, we construct a single decision tree to estimate modification probabilities; how one phrase tends to modify another. next, we introduce a boosting algorithm in which several decision trees are constructed and then combined for probability estimation. the two constructed parsers are evaluated by using the edr japanese annotated corpus. the single-tree method outperforms the conventional japanese stochastic methods by 4%. moreover, the boosting version is shown to have significant advantages; 1) better parsing accuracy than its single-tree counterpart for any amount of training data and 2) no over-fitting to data for various iterations.
high-performance bilingual text alignment using statistical and dictionary information. this paper describes an accurate and robust text alignment system for structurally different languages. among structurally different languages such as japanese and english, there is a limitation on the amount of word correspondences that can be statistically acquired. the main reason for this is the systems of functional (closed) words are quite different in the two languages. the proposed method makes use of two kinds of word correspondences in aligning bilingual texts. one is a bilingual dictionary of general use. the other is the word correspondences that are statistically acquired in the alignment process. our method gradually determines sentence pairs (anchors) that correspond to each other by relaxing parameters. the method, by combining two kinds of word correspondences, achieves adequate word correspondences for complete alignment. as a result, texts of various length and of various genres in structurally different languages can be aligned with high precision. experimental results show our system outperforms conventional methods for various kinds of japanese&ndash;english texts.
learning strategies for open-domain natural language question answering. this work presents a model for learning inference procedures for story comprehension through inductive generalization and reinforcement learning, based on classified examples. the learned inference procedures (or strategies) are represented as of sequences of transformation rules. the approach is compared to three prior systems, and experimental results are presented demonstrating the efficacy of the model.
integrating text plans for conciseness and coherence. our experience with a critiquing system shows that when the system detects problems with the user's performance, multiple critiques are often produced. analysis of a corpus of actual critiques revealed that even though each individual critique is concise and coherent, the set of critiques as a whole may exhibit several problems that detract from conciseness and coherence, and consequently assimilation. thus a text planner was needed that could integrated the text plans for individual communicative goals to produce an overall text plan representing a concise, coherent message.this paper presents our general rule-based system for accomplishing this task. the system takes as input a set of individual text plans represented as rst-style trees, and produces a smaller set of more complex trees representing integrated messages that still achieve the multiple communicative goals of the individual text plans. domain-independent rules are used to capture strategies across domains, while the facility for addition of domain-dependent rules enables the system to be tuned to the requirements of a particular domain. the system has been tested on a corpus of critiques in the domain of trauma care.
towards abstract categorial grammars. we introduce a new categorial formalism based on intuitionistic linear logic. this formalism, which derives from current type-logical grammars, is abstract in the sense that both syntax and semantics are handled by the same set of primitives. as a consequence, the formalism is reversible and provides different computational paradigms that may be freely composed together.
improving name tagging by reference resolution and relation detection. information extraction systems incorporate multiple stages of linguistic analysis. although errors are typically compounded from stage to stage, it is possible to reduce the errors in one stage by harnessing the results of the other stages. we demonstrate this by using the results of coreference analysis and relation extraction to reduce the errors produced by a chinese name tagger. we use an n-best approach to generate multiple hypotheses and have them re-ranked by subsequent stages of processing. we obtained thereby a reduction of 24% in spurious and incorrect name tags, and a reduction of 14% in missed tags.
discovering relations among named entities from large corpora. discovering the significant relations embedded in documents would be very useful not only for information retrieval but also for question answering and summarization. prior methods for relation discovery, however, needed large annotated corpora which cost a great deal of time and effort. we propose an unsupervised method for relation discovery from large corpora. the key idea is clustering pairs of named entities according to the similarity of context words intervening between the named entities. our experiments using one year of newspapers reveals not only that the relations among named entities could be detected with high recall and precision, but also that appropriate labels could be automatically provided for the relations.
japanese idiom recognition: drawing a line between literal and idiomatic meanings. recognizing idioms in a sentence is important to sentence understanding. this paper discusses the lexical knowledge of idioms for idiom recognition. the challenges are that idioms can be ambiguous between literal and idiomatic meanings, and that they can be "transformed" when expressed in a sentence. however, there has been little research on japanese idiom recognition with its ambiguity and transformations taken into account. we propose a set of lexical knowledge for idiom recognition. we evaluated the knowledge by measuring the performance of an idiom recognizer that exploits the knowledge. as a result, more than 90% of the idioms in a corpus are recognized with 90% accuracy.
towards the automatic identification of adjectival scales: clustering adjectives according to meaning. in this paper we present a method to group adjectives according to their meaning, as a first step towards the automatic identification of adjectival scales. we discuss the properties of adjectival scales and of groups of semantically related adjectives and how they imply sources of linguistic knowledge in text corpora. we describe how our system exploits this linguistic knowledge to compute a measure of similarity between two adjectives, using statistical techniques and without having access to any semantic information about the adjectives. we also show how a clustering algorithm can use these similarities to produce the groups of adjectives, and we present results produced by our system for a sample set of adjectives. we conclude by presenting evaluation methods for the task at hand, and analyzing the significance of the results obtained.