'Constituent length' effects in fMRI do not provide evidence for abstract syntactic processing

How are syntactically and semantically connected word sequences, or constituents, represented in the human language system? An influential fMRI study, Pallier et al. (2011, PNAS), manipulated the length of constituents in sequences of words or pseudowords. They reported that some language regions (in the anterior temporal cortex and near the temporo-parietal junction) were sensitive to constituent length only for sequences of real words but not pseudowords. In contrast, language regions in the inferior frontal and posterior temporal cortex showed the same pattern of increased response to longer constituents - and similar overall response magnitudes - for word and pseudoword sequences. Based on these results, Pallier et al. argued that the latter regions represent abstract sentence structure. Here we identify methodological and theoretical concerns with the Pallier et al. study and conduct a replication across two fMRI experiments. Our results do not support Pallier et al.'s critical claim of distinct neural specialization for abstract syntactic representations. Instead, we find that all language regions show a similar profile of sensitivity to both constituent length and lexicality (stronger responses to real-word than pseudoword stimuli). In addition, we argue that the constituent length effect in these experiments i) is not readily grounded in established theories of sentence processing, and ii) may not actually derive from syntactic structure building, but may instead reflect the temporal receptive window of the human language system.

Logophoricity in discourse

This paper deals with the notion of logophoricity as a phenomenon pertaining to discourse, not grammar. An examination of the discourse role configurations (discourse environments) proposed by Peter Sells has shown that particular configurations are either overloaded by role-multiplication or non-specific. Instead of discourse environments, a general pragmatic matrix (a non-overt abstract sentence) is suggested, anchoring any utterance event to the current speaker’s perspective, including his/her communicative activity and temporal and spatial location. The role of the pragmatic matrix shows that all utterance events can be considered logophoric. At the same time, the pragmatic matrix underlines specific properties of explicit performative formulae.

Peringkasan Dokumen Berdasarkan Metode Semantic Sebaran Kalimat

Sentence distribution method performs weighting based on the sentence distribution without taking the semantic meaning of the sentence spread into account. In fact, the semantic relation between sentences is believed to increase the relevance of the search results document. This study proposes new strategies to summarize documents using the semantic sentence distribution method in an effort to improve the quality of the summary. The experimental results show that the proposed method has better performance with the average performance ROUGE-1 0.412, an increase of 1,9% compared to "Sentence distribution method" and ROUGE-2 by 4,7% compared to 0.127 "sentence distribution method".Keywords: Semantic Sentence Distribution, Summarizing Document, ROUGE. Abstrak. Peringkasan dokumen menggunakan metode sebaran kalimat terbukti memiliki hasil yang lebih baik jika dibanding dengan penelitian-penelitian sebelumnya. Metode tersebut melakukan pembobotan kalimat berdasarkan sebaran kalimat tanpa memperhitungkan makna semantic kalimat yang tersebar. Faktanya hubungan semantic antar kalimat telah terbukti mampu meningkatkan relevansi hasil dalam pencarian dokumen. Penelitian ini mengajukan strategi baru dalam peringkasan dokumen yaitu menggunakan metode semantic sebaran kalimat sebagai upaya untuk meningkatkan kualitas hasil ringkasan. Hasil eksperimen didapatkan bahwa metode yang diusulkan memiliki performa lebih baik dengan capaian rata-rata ROUGE-1 0,412, meningkat 1,9% dibanding metode sebaran kalimat dan ROUGE-2 0,127 meningkat 4,7% dibanding metode sebaran kalimat.Kata Kunci: Semantic Sebaran Kalimat, Peringkasan Dokumen, ROUGE.