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1. Within arms reach: Physical proximity shapes mother-infant language exchanges in real-time

   https://doi.org/10.1016/j.dcn.2023.101298  

   Suarez-Rivera, Catalina; Pinheiro-Mehta, Nicole; Tamis-LeMonda, Catherine S (December 2023, Developmental Cognitive Neuroscience)

   Van_Den_Heuvel, M; Wass, S V (Ed.)

   During everyday interactions, mothers and infants achieve behavioral synchrony at multiple levels. The ebb-and-flow of mother-infant physical proximity may be a central type of synchrony that establishes a common ground for infant-mother interaction. However, the role of proximity in language exchanges is relatively unstudied, perhaps because structured tasks—the common setup for observing infant-caregiver interactions—establish proximity by design. We videorecorded 100 mothers (U.S. Hispanic N =50, U.S. Non-Hispanic N =50) and their 13- to 23-month-old infants during natural activity at home (1-to-2 h per dyad), transcribed mother and infant speech, and coded proximity continuously (i.e., infants and mother within arms reach). In both samples, dyads entered proximity in a bursty temporal pattern, with bouts of proximity interspersed with bouts of physical distance. As hypothesized, Non-Hispanic and Hispanic mothers produced more words and a greater variety of words when within arms reach than out of arms reach. Similarly, infants produced more utterances that contained words when close to mother than when not. However, infants babbled equally often regardless of proximity, generating abundant opportunities to play with sounds. Physical proximity expands opportunities for language exchanges and infants’ communicative word use, although babies accumulate massive practice babbling even when caregivers are not proximal. 

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2. BATS: A Spectral Biclustering Approach to Single Document Topic Modeling and Segmentation

   https://doi.org/10.1145/3468268  

   Wu, Qiong; Hare, Adam; Wang, Sirui; Tu, Yuwei; Liu, Zhenming; Brinton, Christopher G.; Li, Yanhua (October 2021, ACM Transactions on Intelligent Systems and Technology)

   Existing topic modeling and text segmentation methodologies generally require large datasets for training, limiting their capabilities when only small collections of text are available. In this work, we reexamine the inter-related problems of “topic identification” and “text segmentation” for sparse document learning, when there is a single new text of interest. In developing a methodology to handle single documents, we face two major challenges. First is sparse information : with access to only one document, we cannot train traditional topic models or deep learning algorithms. Second is significant noise : a considerable portion of words in any single document will produce only noise and not help discern topics or segments. To tackle these issues, we design an unsupervised, computationally efficient methodology called Biclustering Approach to Topic modeling and Segmentation (BATS). BATS leverages three key ideas to simultaneously identify topics and segment text: (i) a new mechanism that uses word order information to reduce sample complexity, (ii) a statistically sound graph-based biclustering technique that identifies latent structures of words and sentences, and (iii) a collection of effective heuristics that remove noise words and award important words to further improve performance. Experiments on six datasets show that our approach outperforms several state-of-the-art baselines when considering topic coherence, topic diversity, segmentation, and runtime comparison metrics. 

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3. Parsimonious Morpheme Segmentation with an Application to Enriching Word Embeddings

   https://doi.org/10.1109/BigData47090.2019.9005957  

   El-Kishky, Ahmed; Xu, Frank; Zhang, Aston; Han, Jiawei (December 2019, 2019 {IEEE} International Conference on Big Data (Big Data))

   Traditionally, many text-mining tasks treat individual word-tokens as the finest meaningful semantic granularity. However, in many languages and specialized corpora, words are composed by concatenating semantically meaningful subword structures. Word-level analysis cannot leverage the semantic information present in such subword structures. With regard to word embedding techniques, this leads to not only poor embeddings for infrequent words in long-tailed text corpora but also weak capabilities for handling out-of-vocabulary words. In this paper we propose MorphMine for unsupervised morpheme segmentation. MorphMine applies a parsimony criterion to hierarchically segment words into the fewest number of morphemes at each level of the hierarchy. This leads to longer shared morphemes at each level of segmentation. Experiments show that MorphMine segments words in a variety of languages into human-verified morphemes. Additionally, we experimentally demonstrate that utilizing MorphMine morphemes to enrich word embeddings consistently improves embedding quality on a variety of of embedding evaluations and a downstream language modeling task. 

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4. Degrees of Separation in Semantic and Syntactic Relationships

   Kelly, Matthew A.; Reitter, David; West, Robert L. (July 2017, Proc 15th. International Conference on Cognitive Modeling)

   Computational models of distributional semantics can analyze a corpus to derive representations of word meanings in terms of each word’s relationship to all other words in the corpus. While these models are sensitive to topic (e.g., tiger and stripes) and synonymy (e.g., soar and fly), the models have limited sensitivity to part of speech (e.g., book and shirt are both nouns). By augmenting a holographic model of semantic memory with additional levels of representations, we present evidence that sensitivity to syntax is supported by exploiting associations between words at varying degrees of separation. We find that sensitivity to associations at three degrees of separation reinforces the relationships between words that share part-of-speech and improves the ability of the model to construct grammatical sentences. Our model provides evidence that semantics and syntax exist on a continuum and emerge from a unitary cognitive system. 

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5. Fearless Steps APOLLO: Challenges in keyword spotting and topic detection for naturalistic audio streams

   Joglekar, A.; Lopez-Espejo, I.; Hansen, J.H.L. (May 2023, Program of the meeting Acoustical Society of America)

   Fearless Steps (FS) APOLLO is a + 50,000 hr audio resource established by CRSS-UTDallas capturing all communications between NASA-MCC personnel, backroom staff, and Astronauts across manned Apollo Missions. Such a massive audio resource without metadata/unlabeled corpus provides limited benefit for communities outside Speech-and-Language Technology (SLT). Supplementing this audio with rich metadata developed using robust automated mechanisms to transcribe and highlight naturalistic communications can facilitate open research opportunities for SLT, speech sciences, education, and historical archival communities. In this study, we focus on customizing keyword spotting (KWS) and topic detection systems as an initial step towards conversational understanding. Extensive research in automatic speech recognition (ASR), speech activity, and speaker diarization using manually transcribed 125 h FS Challenge corpus has demonstrated the need for robust domain-specific model development. A major challenge in training KWS systems and topic detection models is the availability of word-level annotations. Forced alignment schemes evaluated using state-of-the-art ASR show significant degradation in segmentation performance. This study explores challenges in extracting accurate keyword segments using existing sentence-level transcriptions and proposes domain-specific KWS-based solutions to detect conversational topics in audio streams. 

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