Beyond gender stereotypes in language comprehension: self sex-role descriptions affect the brain’s potentials associated with agreement processing

We recorded Event-Related Potentials to investigate differences in the use of gender information during the processing of reflexive pronouns. Pronouns either matched the gender provided by role nouns (such as “king” or “engineer”) or did not. We compared two types of gender information, definitional information, which is semantic in nature (a mother is female), or stereotypical (a nurse is likely to be female). When they followed definitional role-nouns, gender-mismatching pronouns elicited a P600 effect reflecting a failure in the agreement process. When instead the gender violation occurred after stereotypical role-nouns the Event Related Potential response was biphasic, being positive in parietal electrodes and negative in anterior left electrodes. The use of a correlational approach showed that those participants with more “feminine” or “expressive” self sex-role descriptions showed a P600 response for stereotype violations, suggesting that they experienced the mismatch as an agreement violation; whereas less “expressive” participants showed an Nref effect, indicating more effort spent in linking the pronouns with the possible, although less likely, counter-stereotypical referent

Abstractive Multi-Document Summarization via Phrase Selection and Merging

We propose an abstraction-based multi-document summarization framework that can construct new sentences by exploring more fine-grained syntactic units than sentences, namely, noun/verb phrases. Different from existing abstraction-based approaches, our method first constructs a pool of concepts and facts represented by phrases from the input documents. Then new sentences are generated by selecting and merging informative phrases to maximize the salience of phrases and meanwhile satisfy the sentence construction constraints. We employ integer linear optimization for conducting phrase selection and merging simultaneously in order to achieve the global optimal solution for a summary. Experimental results on the benchmark data set TAC 2011 show that our framework outperforms the state-of-the-art models under automated pyramid evaluation metric, and achieves reasonably well results on manual linguistic quality evaluation.Comment: 11 pages, 1 figure, accepted as a full paper at ACL 201

Focusing for Pronoun Resolution in English Discourse: An Implementation

Anaphora resolution is one of the most active research areas in natural language processing. This study examines focusing as a tool for the resolution of pronouns which are a kind of anaphora. Focusing is a discourse phenomenon like anaphora. Candy Sidner formalized focusing in her 1979 MIT PhD thesis and devised several algorithms to resolve definite anaphora including pronouns. She presented her theory in a computational framework but did not generally implement the algorithms. Her algorithms related to focusing and pronoun resolution are implemented in this thesis. This implementation provides a better comprehension of the theory both from a conceptual and a computational point of view. The resulting program is tested on different discourse segments, and evaluation and analysis of the experiments are presented together with the statistical results.Comment: iii + 49 pages, compressed, uuencoded Postscript file; revised version of the first author's Bilkent M.S. thesis, written under the supervision of the second author; notify Akman via e-mail ([email protected]) or fax (+90-312-266-4126) if you are unable to obtain hardcopy, he'll work out somethin

On the use of verb-based implicit causality in sentence comprehension: Evidence from self-paced reading and eye tracking

In two experiments, we examined the recent claim (Stewart, Pickering, & Sanford, 2000) that verb-based implicit causality information is used during sentence–final clausal integration only. We did so by looking for mid-sentence reading delays caused by pronouns that are inconsistent with the bias of a preceding implicit causality verb (e.g., “David praised Linda because he…”). In a self-paced reading task, such pronouns immediately slowed down reading, at the two words immediately following the pronoun. In eye tracking, bias-inconsistent pronouns also immediately perturbed the reading process, as indexed by significant delays in various first pass measures at and shortly after the critical pronoun. Hence, readers can recruit verb-based implicit causality information in the service of comprehension rapidly enough to impact on the interpretation of a pronoun early in the subordinate clause. We take our results to suggest that implicit causality is used proactively, allowing readers to focus on, and perhaps even predict, who or what will be talked about next

Improved Coreference Resolution Using Cognitive Insights

Coreference resolution is the task of extracting referential expressions, or mentions, in text and clustering these by the entity or concept they refer to. The sustained research interest in the task reflects the richness of reference expression usage in natural language and the difficulty in encoding insights from linguistic and cognitive theories effectively. In this thesis, we design and implement LIMERIC, a state-of-the-art coreference resolution engine. LIMERIC naturally incorporates both non-local decoding and entity-level modelling to achieve the highly competitive benchmark performance of 64.22% and 59.99% on the CoNLL-2012 benchmark with a simple model and a baseline feature set. As well as strong performance, a key contribution of this work is a reconceptualisation of the coreference task. We draw an analogy between shift-reduce parsing and coreference resolution to develop an algorithm which naturally mimics cognitive models of human discourse processing. In our feature development work, we leverage insights from cognitive theories to improve our modelling. Each contribution achieves statistically significant improvements and sum to gains of 1.65% and 1.66% on the CoNLL-2012 benchmark, yielding performance values of 65.76% and 61.27%. For each novel feature we propose, we contribute an accompanying analysis so as to better understand how cognitive theories apply to real language data. LIMERIC is at once a platform for exploring cognitive insights into coreference and a viable alternative to current systems. We are excited by the promise of incorporating our and further cognitive insights into more complex frameworks since this has the potential to both improve the performance of computational models, as well as our understanding of the mechanisms underpinning human reference resolution

From deep dyslexia to agrammatic comprehension on silent reading

We report on a case of a French-speaking patient whose performance on reading aloud single words was characteristically deep dyslexic (in spite of preserved ability to identify letters), and whose comprehension on silent sentence reading was agrammatic and strikingly poorer than on oral reading. The first part of the study is mainly informative as regards (i) the relationship between letter identification, semantic paralexias and the ability to read nonwords, (ii) the differential character of silent and oral reading tasks, and (iii) the potential modality-dependent character of the deficits in comprehension encountered. In the second part of the study we examine the patient's sensitivity to verb-noun ambiguity and probe her skills in the comprehension of indexical structures by exploring her ability to cope with number agreement and temporal and prepositional relations. The results indicate the patient's sensitivity to certain dimensions of these linguistic categories, reveal a partly correct basis for certain incorrect responses, and, on the whole, favor a definition of the patient's disorders in terms of a deficit in integrating indexical information in language comprehension. More generally, the present study substantiates a microgenetic approach to neuropsychology, where the pathological behavior due to brain damage is described as an arrest of microgenesis at an early stage of development, so that patient's responses take the form of unfinished "products" which would normally undergo further development

Domain-Specific Knowledge Acquisition for Conceptual Sentence Analysis

The availability of on-line corpora is rapidly changing the field of natural language processing (NLP) from one dominated by theoretical models of often very specific linguistic phenomena to one guided by computational models that simultaneously account for a wide variety of phenomena that occur in real-world text. Thus far, among the best-performing and most robust systems for reading and summarizing large amounts of real-world text are knowledge-based natural language systems. These systems rely heavily on domain-specific, handcrafted knowledge to handle the myriad syntactic, semantic, and pragmatic ambiguities that pervade virtually all aspects of sentence analysis. Not surprisingly, however, generating this knowledge for new domains is time-consuming, difficult, and error-prone, and requires the expertise of computational linguists familiar with the underlying NLP system. This thesis presents Kenmore, a general framework for domain-specific knowledge acquisition for conceptual sentence analysis. To ease the acquisition of knowledge in new domains, Kenmore exploits an on-line corpus using symbolic machine learning techniques and robust sentence analysis while requiring only minimal human intervention. Unlike most approaches to knowledge acquisition for natural language systems, the framework uniformly addresses a range of subproblems in sentence analysis, each of which traditionally had required a separate computational mechanism. The thesis presents the results of using Kenmore with corpora from two real-world domains (1) to perform part-of-speech tagging, semantic feature tagging, and concept tagging of all open-class words in the corpus; (2) to acquire heuristics for part-ofspeech disambiguation, semantic feature disambiguation, and concept activation; and (3) to find the antecedents of relative pronouns