nils-herrmann/clean_scirepeval huggingface dataset:
Romance Complex Predicates: Phrasal and Functional Structure

Recently there has been much work in surface constraint-based frameworks on complex predicate constructions (causative and light verb constructions), that are found in Romance and other languages. Manning et al. forthcoming). The enthusiasm for this area comes from the fact that these constructions contradict certain of the assumptions of early work in surface constraint-based frameworks. Nevertheless, recent work has provided clean, principled extensions to these frameworks that give good accounts of complex predicate constructions. In this paper I want to (i) present some of the relevant data, (ii) discuss the outlines of LFG approaches to the data, (iii) make some comparisons with corresponding work in HPSG, and (iv) look particularly at some of the ner points of clitic climbing. 1 1 Introducing the problem Complex predicates occur when two or more verbs become more closely associated than they are in an ordinary construction in which one verb takes a verbal complement as a sister. There are numerous diagnostics that point to this closer relationship, but perhaps the most salient one in Romance is clitic placement. With simplex verbs and normal clausal complement taking verbs we observe the following clitic placement behavior: 2 (1) a. Sp Luis Luis comii o eat.3sg.past las the manzanas apples amarillas yellow`Luis ate the yellow apples.' b. Luis Luis las 3pl.fem comii o atèLuis ate them.' c. Luis Luis insistii o insisted en on comer eat.inf las the manzanas apples amarillas yellow`Luis insisted on eating the yellow apples.' d. Luis insistii o en comer-las`Luis insisted on eating them.' 1 These notes are mainly extracted from Manning (1992) and Andrews and Manning (1993), although new material appears in various places, in particular, the nal section. 2 I use the standard orthographies for all Romance languages. In these orthographies, the proclitics are normally written as separate words while the enclitics are written joined to their hosts. I will sometimes set oo enclitics with a hyphen for clarity. The two bold letters before the rst sentence in each group of examples indicate the language: Full NP complements follow the verb they are an argument of, but clitics appear adjacent to the verb that they are an argument of. It is impossible for a clitictòòat' up onto another verb, as shown in (1e). The behavior of clitics with verbs forming a complex predicate diiers from what was shown above. 3 In these cases the clitic may appear on a higher …
Valency versus binding: On the distinctness of argument structure

This paper argues for the use of two levels in syntactic description, grammatical relations and argument structure, at which sentences may have diierent representations: the prominence ranking may diier, as in syntactically ergative and Western Austronesian languages, and the argument structure may be more nested, as in passives or causatives. We begin with binding theory. 1 1 Rethinking binding theory Most theories of binding in most syntactic frameworks assume that the same notion of surface obliqueness that identiies the subject of a clause is also used for obliqueness conditions on reeexive binding. For instance, in GB (Chomsky 1981), binding theory is standardly deened on S-structure, so that in (1), Nancy can bind herself due to the c-commanding connguration that also makes Nancy the subject of the sentence. (1) IP NP Nancy i VP V gave NP herself i NP Det a N raise 1 This paper is an unrevised draft of the paper I presented at the Colloque de Syntaxe et S emantique de Paris. I thank people at the conference for their comments. Further comments are also welcome. Some of the ideas here are explored in more detail in my dissertation and other papers available at
Presents embedded under pasts

According to traditional grammar, this is a sentence where sequence of tense has failed to apply (i.e., concord has been broken): standard sequence of tense rules would dictate use of a past tense when embedding an event contemporaneous to the embedding verb under a past tense verb, giving the sentence John said that Mary was pregnant . For some verbs breaking concord is impossible (*Mary said that John builds a house) or can only have a present-as-future interpretation (John said that the last spaceship to Mars leaves tomorrow), but with stative verbs, as Enç (1987) and others have observed, this failure of sequence of tense to apply is associated with a rather special meaning, which we will try to elucidate below. For the moment, let us merely observe that the use of present tense seems to cause such sentences to end up saying something about a larger interval including both the time of utterance and the time of the event described in the main clause. For this reason Enç calls them “double access sentences”, but that seems a rather dubious name as the interpretation seems to rely on evaluation at a large interval, not just at two points.
Some Notes on Kernel CRFs Xiaojin Zhu

1 Import Vector Selection for Kernel CRFs In the basic kernel CRF model, each clique c is associated with |y| parameters αj(yc). Even if we only consider vertex cliques, there would be hundreds of thousands of parameters for a typical protein dataset. This seriously affects the training efficiency. To solve the problem, we adopt the notion of “import vector machines” [ZH01]. That is, we use a subset of the training examples instead of all of them. The subset is constructed by greedily selecting training examples one at a time to minimize the loss function: argminkO(fA∪{k}, λ) −O(fA, λ) (1) where fA(x,y) = ∑
Semi-supervised learning with graphs

In traditional machine learning approaches to classification, one uses only a labeled set to train the classifier. Labeled instances however are often difficult, expensive, or time consuming to obtain, as they require the efforts of experienced human annotators. Meanwhile unlabeled data may be relatively easy to collect, but there has been few ways to use them. Semi-supervised learning addresses this problem by using large amount of unlabeled data, together with the labeled data, to build better classifiers. Because semi-supervised learning requires less human effort and gives higher accuracy, it is of great interest both in theory and in practice. 
We present a series of novel semi-supervised learning approaches arising from a graph representation, where labeled and unlabeled instances are represented as vertices, and edges encode the similarity between instances. They address the following questions: How to use unlabeled data? (label propagation); What is the probabilistic interpretation? (Gaussian fields and harmonic functions); What if we can choose labeled data? (active learning); How to construct good graphs? (hyperparameter learning); How to work with kernel machines like SVM? (graph kernels); How to handle complex data like sequences? (kernel conditional random fields); How to handle scalability and induction? (harmonic mixtures). An extensive literature review is included at the end.
Person Identification in Webcam Images: An Application of Semi-Supervised Learning

An application of semi-supervised learning is
made to the problem of person identification in
low quality webcam images. Using a set of images
of ten people collected over a period of four
months, the person identification task is posed
as a graph-based semi-supervised learning problem,
where only a few training images are labeled.
The importance of domain knowledge
in graph construction is discussed, and experiments
are presented that clearly show the advantage
of semi-supervised learning over standard
supervised learning. The data used in the study
is available to the research community to encourage
further investigation of this problem.
Harmonic mixtures: combining mixture models and graph-based methods for inductive and scalable semi-supervised learning

Graph-based methods for semi-supervised learning have recently been shown to be promising for combining labeled and unlabeled data in classification problems. However, inference for graph-based methods often does not scale well to very large data sets, since it requires inversion of a large matrix or solution of a large linear program. Moreover, such approaches are inherently transductive, giving predictions for only those points in the unlabeled set, and not for an arbitrary test point. In this paper a new approach is presented that preserves the strengths of graph-based semi-supervised learning while overcoming the limitations of scalability and non-inductive inference, through a combination of generative mixture models and discriminative regularization using the graph Laplacian. Experimental results show that this approach preserves the accuracy of purely graph-based transductive methods when the data has "manifold structure," and at the same time achieves inductive learning with significantly reduced computational cost.
Kernel conditional random fields: representation and clique selection

Kernel conditional random fields (KCRFs) are introduced as a framework for discriminative modeling of graph-structured data. A representer theorem for conditional graphical models is given which shows how kernel conditional random fields arise from risk minimization procedures defined using Mercer kernels on labeled graphs. A procedure for greedily selecting cliques in the dual representation is then proposed, which allows sparse representations. By incorporating kernels and implicit feature spaces into conditional graphical models, the framework enables semi-supervised learning algorithms for structured data through the use of graph kernels. The framework and clique selection methods are demonstrated in synthetic data experiments, and are also applied to the problem of protein secondary structure prediction.
Kernel conditional random fields : representation, clique selection, and semi-supervised learning

Kernel conditional random fields are introduced as a framework for discriminative modeling of graph-structured data. A representer theorem for conditional graphical models is given which shows how kernel conditional random fields arise from risk minimization procedures defined using Mercer kernels on labeled graphs. A procedure for greedily selecting cliques in the dual representation is then proposed, which allows sparse representations. By incorporating kernels and implicit feature spaces into conditional graphical models, the framework enables semi-supervised learning algorithms for structured data through the use of graph kernels. The clique selection and semisupervised methods are demonstrated in synthetic data experiments, and are also applied to the problem of protein secondary structure prediction. This research was partially supported by the National Science Foundation (NSF), under grants IIS-0205456 and IIS-0312814. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the National Science Foundation, or the U.S. government.
Semi-Supervised Learning Using Gaussian Fields and Harmon ic Functions

An approach to semi-supervised learning is proposed that is based on a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, with edge weights encoding the similarity between instances. The learning problem is then formulated in terms of a Gaussian random field on this graph, where the mean of the field is characterized in terms of harmonic functions, and is efficiently obtained using matrix methods or belief propagation. The resulting learning algorithms have intimate connections with random walks, electric networks, and spectral graph theory. We discuss methods to incorporate class priors and the predictions of classifiers obtained by supervised learning. We also propose a method of parameter learning by entropy minimization, and show the algorithm’s ability to perform feature selection. Promising experimental results are presented for synthetic data, digit classification, and text classification tasks.
Improving trigram language modeling with the World Wide Web

We propose a method for using the World Wide Web to acquire trigram estimates for statistical language modeling. We submit an N-gram as a phrase query to Web search engines. The search engines return the number of Web pages containing the phrase, from which the N-gram count is estimated. The N-gram counts are then used to form Web-based trigram probability estimates. We discuss the properties of such estimates, and methods to interpolate them with traditional corpus based trigram estimates. We show that the interpolated models improve speech recognition word error rate significantly over a small test set.
Whole-sentence exponential language models: a vehicle for linguistic-statistical integration

We introduce an exponential language model which models a whole sentence or utterance as a single unit. By avoiding the chain rule, the model treats each sentence as a “bag of features", where features are arbitrary computable properties of the sentence. The new model is computationally more efficient, and more naturally suited to modeling global sentential phenomena, than the conditional exponential (e.g. maximum entropy) models proposed to date. Using the model is straightforward. Training the model requires sampling from an exponential distribution. We describe the challenge of applying Monte Carlo Markov Chain and other sampling techniques to natural language, and discuss smoothing and step-size selection. We then present a novel procedure for feature selection, which exploits discrepancies between the existing model and the training corpus. We demonstrate our ideas by constructing and analysing competitive models in the Switchboard and Broadcast News domains, incorporating lexical and syntactic information.
Multimodal Meeting Tracker

Face-to-face meetings usually encompass several modalities including speech, gesture, handwriting, and person identification. Recognition and integration of each of these modalities is important to create an accurate record of a meeting. However, each of these modalities presents recognition difficulties. Speech recognition must be speaker and domain independent, have low word error rates, and be close to real time to be useful. Gesture and handwriting recognition must be writer independent and support a wide variety of writing styles. Person identification has difficulty with segmentation in a crowded room. Furthermore, in order to produce the record automatically, we have to solve the assignment problem (who is saying what), which involves people identification and speech recognition. We follow a multimodal approach for people identification to increase the robustness (with the modules: color appearance id, face id and speaker id). This paper will examine a meeting room system under development at Carnegie Mellon University that enables us to track, capture and integrate the important aspects of a meeting from people identification to meeting transcription. Once a multimedia meeting record is created, it can be archived for later retrieval. This paper will review our meeting browser that we have developed which facilitates tracking and reviewing meetings.
Towards a multimodal meeting record

Face-to-face meetings usually encompass several modalities including speech, gesture, handwriting, and person identification. Recognition and integration of each of these modalities is important to create an accurate record of a meeting. However, each of these modalities presents recognition difficulties. Speech recognition must be speaker and domain independent, have low word error rates, and be close to real time to be useful. Gesture and handwriting recognition must be writer independent and support a wide variety of writing styles. Person identification has difficulty with segmentation in a crowded room. Furthermore, in order to produce the record automatically, we have to solve the assignment problem (who is saying what), which involves people identification and speech recognition. This paper examines a multimodal meeting room system under development at Carnegie Mellon University that enables us to track, capture and integrate the important aspects of a meeting from people identification to meeting transcription. Once a multimedia meeting record is created, it can be archived for later retrieval.
Segmenting hands of arbitrary color

Hand segmentation is a prerequisite for many gesture recognition tasks. Color has been widely used for hand segmentation. However, many approaches rely on predefined skin color models. It is very difficult to predefine a color model in a mobile application where the light condition may change dramatically over time. We propose a novel statistical approach to hand segmentation based on Bayes decision theory. The proposed method requires no predefined skin color model. Instead it generates a hand color model and a background color model for a given image, and uses these models to classify each pixel in the image as either a hand pixel or a background pixel. Models are generated using a Gaussian mixture model with the restricted EM algorithm. Our method is capable of segmenting hands of arbitrary color in a complex scene. It performs well even when there is a significant overlap between hand and background colors, or when the user wears gloves. We show that the Bayes decision method is superior to a commonly used method by comparing their upper bound performance. Experimental results demonstrate the feasibility of the proposed method.
Linguistic features for whole sentence maximum entropy language models

Whole sentence maximum entropy models directly model the probability of a sentence using features – arbitrary computable properties of the sentence. We investigate whether linguistic features that capture the underlying linguistic structure of a sentence can improve modeling. We use a shallow parser to parse sentences into linguistic constituents in two corpora; one is the original training corpus, and the other is an artificial corpus generated from an initial trigram model. We define three sets of candidate linguistic features based on these constituents, and compute the prevalence of each feature in the two data sets. We select features with significantly different frequencies. These correspond to phenomena poorly modeled by traditional trigrams, and reveal interesting linguistic deficiencies of the initial model. We found 6798 linguistic features in the Switchboard domain and achieved small improvements in perplexity and speech recognition accuracy with these features.
Interactive Feature Induction and Logistic Regression for Whole Sentence Exponential Language

Whole sentence exponential language models directly model the probability of an entire sentence using arbitrary computable properties of that sentence. We present an interactive methodology for feature induction, and demonstrate it in the simple but common case of a trigram baseline, focusing on features that capture the linguistic notion of semantic coherence. We then show how parametric regression can be used in this setup to efficiently estimate the model’s parameters, whereas non-parametric regression can be used to construct more powerful exponential models from the raw features.
Corpus THAT ’ S YOUR NEWS ON THE DAY BEFORE CHRISTMAS THIRTY FIVE PAST THE HOUR < / s >

Whole sentence exponential language models directly model the probability of an entire sentence using arbitrary computable properties of that sentence. We present an inter active methodology for feature induction, and demonstrate it in the simple but common case of a trigram baseline, focusing on features that capture the linguistic notion of semantic coherence. We then show how parametric regression can be used in this setup to efficiently estimate the model’s parameters, whereas non-parametric regression can be used to construct more powerful exponential models from the raw features.
Multimodal people ID for a multimedia meeting browser

A meeting browser is a system that allows users to review a multimedia meeting record from a variety of indexing methods. Identification of meeting participants is essential for creating such a multimedia meeting record. Moreover, knowing who is speaking can enhance the performance of speech recognition and indexing meeting transcription. In this paper, we present an approach that identifies meeting participants by fusing multimodal inputs. We use face ID, speaker ID, color appearance ID, and sound source directional ID to identify and track meeting. After describing the different modules in detail, we will discuss a framework for combining the information sources. Integration of the multimodal people ID into the multimedia meeting browser is in its preliminary stage.
A Core-Tools Statistical NLP Course

In the fall term of 2004, I taught a new statistical NLP course focusing on core tools and machine-learning algorithms. The course work was organized around four substantial programming assignments in which the students implemented the important parts of several core tools, including language models (for speech reranking), a maximum entropy classifier, a part-of-speech tagger, a PCFG parser, and a word-alignment system. Using provided scaffolding, students built realistic tools with nearly state-of-the-art performance in most cases. This paper briefly outlines the coverage of the course, the scope of the assignments, and some of the lessons learned in teaching the course in this way.
A Discriminative Matching Approach to Word Alignment

We present a discriminative, large-margin approach to feature-based matching for word alignment. In this framework, pairs of word tokens receive a matching score, which is based on features of that pair, including measures of association between the words, distortion between their positions, similarity of the orthographic form, and so on. Even with only 100 labeled training examples and simple features which incorporate counts from a large unlabeled corpus, we achieve AER performance close to IBM Model 4, in much less time. Including Model 4 predictions as features, we achieve a relative AER reduction of 22% in over intersected Model 4 alignments.
Application of Information Technology: Improved Identification of Noun Phrases in Clinical Radiology Reports Using a High-Performance Statistical Natural Language Parser Augmented with the UMLS Specialist Lexicon

The aim of this study was to develop and evaluate a method of extracting noun phrases with full phrase structures from a set of clinical radiology reports using natural language processing (NLP) and to investigate the effects of using the UMLS(R) Specialist Lexicon to improve noun phrase identification within clinical radiology documents.



The noun phrase identification (NPI) module is composed of a sentence boundary detector, a statistical natural language parser trained on a nonmedical domain, and a noun phrase (NP) tagger. The NPI module processed a set of 100 XML-represented clinical radiology reports in Health Level 7 (HL7)(R) Clinical Document Architecture (CDA)-compatible format. Computed output was compared with manual markups made by four physicians and one author for maximal (longest) NP and those made by one author for base (simple) NP, respectively. An extended lexicon of biomedical terms was created from the UMLS Specialist Lexicon and used to improve NPI performance.



The test set was 50 randomly selected reports. The sentence boundary detector achieved 99.0% precision and 98.6% recall. The overall maximal NPI precision and recall were 78.9% and 81.5% before using the UMLS Specialist Lexicon and 82.1% and 84.6% after. The overall base NPI precision and recall were 88.2% and 86.8% before using the UMLS Specialist Lexicon and 93.1% and 92.6% after, reducing false-positives by 31.1% and false-negatives by 34.3%.



The sentence boundary detector performs excellently. After the adaptation using the UMLS Specialist Lexicon, the statistical parser's NPI performance on radiology reports increased to levels comparable to the parser's native performance in its newswire training domain and to that reported by other researchers in the general nonmedical domain.